141 posts categorized "Regulation"

24 November 2014

PRMIA Risk Year in Review 2014

PRMIA put on their Risk Year in Review event at the New York Life Insurance Company on Thursday. Some of the main points from the panel, starting with trade:

  • The world continues to polarize between "open" and "closed" societies with associated attitudes towards trade and international exposure.
  • US growth at around 3% is better than the rest of the world but this progress is not seen/benefitting a lot of the poplation yet.
  • This against an economic background of Japan, Europe and China all struggling to maintain "healthy" growth (if at all).
  • Looking back at the financial crisis of 2008/9 it was the WTO rules that were in place that kept markets open and prevented isolationist and closed policies from really taking hold - although such populist inward-looking policies are still are major issue and risk for the global economy today.
  • Some optimistic examples of progress howver on world trade recently:
  • US Government is divided and needs to get back to pragmatic decision making
  • The Federval Reserve currently believes that external factors/the rest of the world are not major risks to growth in the US economy.

James Church of sponsor FINCAD then did a brief presentation on their recent experience and a recent survey of their clients in the area valuation and risk management in financial markets:

  • Risk management is now considered as a source of competitive advantage by many insitutions
  • 63% of survey respondents are currently involved in replacing risk systems
  • James gave the example of Alex Lurye saying risk is a differentiator
  • Aggregate view of risk is still difficult due to siloed systems (hello BCBS239)
  • Risk aggregation also needs consistency of modelling assumptions, data and analytics all together if you are avoid adding apples and pears
  • Institutions now need more flexibility in building curves post-crisis with OIS/Libor discounting (see FINCAD white paper)
    • 70% of survey respondents are involved in changes to curve basis
  • Many new calculations to be considered in collateralization given the move to central clearing
  • 62% of survey respondents are investing in better risk management process, so not just technology but people and process aswell

James was followed by a discussion on market/risk events this year:

  • Predictions are hard but 50 years ago Isaac Asimov made 10 predictions for 2014 and 8 of which have come true
  • Bonds and the Dollar are still up but yields are low - this is as a result of relatively poor performance of other currencies and the inward strength of US economy. US is firmly post-crisis economically and markets are anticipating both oil independence and future interest rate movements.
  • Employment level movements are no longer a predictor of interest rate moves, now more balance of payments
  • October 15th 40bp movement in yields in 3 hours (7 standard deviation move) - this was more positioning/liquidity risk in the absence of news - and an illustration of how regulation has moved power from banks to hedge funds
  • Risk On/Off - trading correlation is very difficult - oil price goes means demand up but 30% diver in price over the past 6 months - the correlation has changed
  • On the movie Interstellar, on one planet an astronaut sees a huge mountain but another sees it is a wave larger than anything seen before - all depends on forming your own view of the same information as to what you perceive or understand as risk

Some points of macro economics:

  • Modest slow down this quarter
  • Unemployment to drop to 5.2% in 2015 from 5.8%
  • CS see the Fed hiking rates in mid-2015 followed by 3 further hikes
    • The market does not yet agree, seeing a move in Q3 2015
  • Downside risks are inflation, slow US growth and wages growth anaemic
  • Upside risks - oil price boost to spending reducing cost of gas from 3.2% down to 2.4% of disposable income

Time for some audience questions/discussions:

  • One audience member asked the panel for thoughts on the high price of US Treasuries
  • Quantitative Easing (QE) was (understandably) targetted as having distorting effects
  • Treasury yields have been a proxy for the risk free rate in the past, but the volatility in this rate due to QE has a profound effect on equity valuations
  • Replacing maturing bonds with lower yielding instruments is painful
  • The Fed are concerned to not appear to loose control of interest rates, nor wants to kill the fixed income markets so rate rises will be slow.
  • One of the panelists said that all this had a human dimension not just markets, citing effectively non-existing interest rate levels but with -ve equity still in Florida, no incentive to save so money heads into stock which is risky, low IR of little benefit to senior citizens etc.
  • Taper talk last year saw massive sell off of emerging market currencies - one problem in assessing this is to define which economies are emerging markets - but key is that current account deficits/surpluses matter - which the US escapes as the world's reserve currency but emergining markets do not.
  • Emergining market boom of the past was really a commodities boom, and the US still leads the world's economies and current challenges may expose the limits of authoritarian capitalism

The discussion moved onto central clearing/collateral:

  • Interest rate assets for collateral purposes are currently expensive
  • Regulation may exacerbate volatility with unintended consequencies
  • $4.5T of collateral set aside currently set to rise to $12-13T
  • Risk is that other sovereign nations will target the production of AAA securities for collateral use that are not AAA
  • Banks will not be the place for risk, the shadow banking system will
  • Futures markets may be under collateralized and a source of future risk

One audience member was interested in downside risks for the US and couldn't understand why anyone was pessimistic given the stock market performance and other measures. The panel put forward the following as possible reasons behind a potential slow down:

  • Income inequality meaning benefits are not throughout the economy
  • Corporations making more and more money but not proportionate increase in jobs
  • Wages are flat and senior citizens are struggling
  • (The financial district is not representative of the rest of the economy in the US however surprising that may be to folks in Manhattan)
  • The rest of the US does not have jobs that make them think the future is going to get better

Other points:

  • Banks have badly underperformed the S&P
  • Regulation is a burden on the US economy that is holding US growth back
  • Republicans and Democrats need to co-operate much more
  • House prices need more oversight
  • Currently $1.2T in student loands and students are not expecting to earn more than their parents
  • Top 10 oil producers are all pumping full out
    • The Saudis are refusing to cut production
    • Venezuela funding policies from oil
    • Russia desparately generating dollars from oil
    • Will the US oil bonanza break OPEC - will they be able to co-ordinate effectively given their conflicting interests

 Summary - overall good event with a fair amount of economics to sum up the risks for 2014 and on into 2015. Food and wine tolerably good afterwards too!

 

05 November 2014

Data Management Summit NYC from the A-Team

The A-Team put on another good event at DMS New York yesterday. Lots of good stuff talked and here are a few takeaways that I remember, after a photo of Ludwig D'Angelo of JPMorgan:

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  • Data Utilities - One of presenters said that "Data Utility" was a really overused term second only to "Big Data". My comment would be that a lot of the managed services folks seem to want to talk about "Data Utilities" - seeming to prefer that term rather than what they are? Maybe because they perceive as better marketing and/or maybe because they hope to be annointed/appointed (how I don't know) as an industry "Data Utility". Anyway for me they fail to address the issue of client-specific data and its management very well, much to the detriment of their argument imho - although SmartStream did say that client data can be mixed up into the data services they offer. 
  • Andrew Gets Literaturally Physical - Andrew Delaney of the A-Team expressed a preference for "physical" books when talking about why the A-Team also prints the Regulatory Data Handbook2 as well as making it available online. I have to agree that holding a book still beats my Kindle experience but maybe I am just getting old. Andrew should check out this YouTube video on how the book was first introduced...
  • FIBO - The Financial Instrument Business Ontology (FIBO) was discussed in the context of trying to establish industry standards for data. As ever the usage of words like "Ontology" I suspect leaves a lot of business folks looking for the nearest double shot of expresso but that aside, seems like the EDM Council are making some progress on developing this standard. Main point from the event was industry adoption is key. I found some of the comments during the day a bit schizophrenic, in that some said that the regulators should not mandate standards (i.e. leave it to industry adoption and principles) but then in the next breath discussing the benefits (or otherwise) of the LEI (ok, not mandated but specific and coming from the regulators). Certainly the industry needs "help" (is that a strong enough word?) to get standards in place.
  • Data Quality - Lots on data quality with assessing the business value of data quality initiatives being a key point. On the same subject, Predrag of element-22 announced that the EDM Council will soon be announcing adoption of the Data Quality Index, which could be used to correlate data quality with operational KPIs for the business. 
  • Regulation (doh!) - It wouldn't be a data management event without lots of discussion on regulation - a key point being that even those regulations that are not directly/explicitly about data still imply that data management is key (take CVA calcs for example) - and on a related note it was suggested that BCBS239 should be considered as a more general data managment template for any business objective. 
  • Entity Hierarchies/LEI - Ludwig D'Angelo of JPMorgan gave a great talk and said that vendors were missing a massive opportunity in delivering good hierarchy datasets to clients, and that the effort expended on this at firms was enormous. Ludwig said that the lack of hierarchies in the Legal Entity Identifier (LEI) is a gap that the private sector could and should fill.  Ludwig also seemed initially to be thrown when one of the audience suggested that they were multiple "golden copies" of hierarchies needed, since definitions of ownership can differ depending on which department you are in (old battle of risk and finance departments again). Good discussion later of how regulation was driving all systems to be much more entity-centric rather than portfolio-centric, emphasising the importance of getting entity hierarchies right. 
  • DCAM - John Bottega did a great presentation on the Data Management Capability Model (DCAM). John asked Predrag of element-22 to speak about DCAM and he said that unlike previous models (DMM) then this framework would not only assess where you are in data management but will also show you where you need to go. DCAM covers data management strategy / operations / quality / business case / data architecture / tech architecture / governance / program. From what I could see it looked like a great framework - it appeared like common sense and obvious but that is in itself difficult to achieve so good effort I think. Element-22 will offer an online service around DCAM that will also allow anonymous benchmarking of data management capabilities as more institutions get involved (update: the service is called pellustro).
  • BCBS239 - Big thanks to John M. Fleming of BNY Mellon and Srikant Ganesan of Risk Focus for taking part in the panel with me. Less focus on spreadsheet use and abuse on this panel unlike the London Panel from last month. John had some very practical ideas such as the use of Wikis to publish/gather data dictionary information and with a large legacy infrastructure you are better documenting differences in definitions across systems rather than trying to change the world from day one. Echoing some of the points from DMS London, it was thought that making the use of internal data standards as part of a project sign off was very pragmatic data governance, but that also some systems should be marked/assessed as obsolete/declining and hence blocked from any additional usage in new project work. Bit of a plug for some of our recent work on data validation and exception management, but the panel said that BCBS239 needs to encompass audit/lineage on calculations/derived data/rules in addition to just the raw data

You can get more on the day by taking a look at my feed via @TheLongSentance and involving others at #DMSNYC.

 

30 October 2014

Banking Reloaded from Capco and Zicklin

Great event by Capco and Zicklin Business School at Baruch College in NYC yesterday. Topics went right through from high frequency trading, systemic risk, wealth management and bitcoin. The agenda is here and you can see some on the highlights on twitter at #BankingReloaded.

16 October 2014

TabbForum MarketTech 2014: Game of Smarts

A great afternoon event put on by TabbFORUM in New York yesterday with a number of panels and one on one interviews (see agenda). You can see some of went on at the event via the hashtag #TabbTech or via the @XenomorphNews feed.

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"Death of Legacy" Panel Discussion

13 October 2014

A-Team DMS London Event and BCBS239 Panel

Good day at the A-Team's DMS London event last Wednesday. The day started with Tom Dalglish doing a pretty passable impression of a stand-up comedian in the morning keynote to open the day - not exactly an easy thing to do if 1) you are asked to do it very much at the last minute and 2) this is data management, not the subject that most comedians would immediately reach out for. So due kudos to Tom, and some of the comments he made about technology architects and technology builders were funny and resonated with the audience, such as this quote coming from a technologist: "How can I give you the requirements, I haven't finished the code yet?" (I think we have all been there on that one a few times in our careers...).

You can find some of the main points from the various panels at via @XenomorphNews or more generally by #dmslondon (you could also find out a bit via my twitter account @TheLongSentance so long as you don't mind the odd photograph and a few bits of personal baggage now and again).

BCBS239 Panel - I took part in the panel on BCBS239 on risk data aggregation and reporting, something which I have written about before, and obviously a prime example of how regulation is influencing (dictating?) financial markets institutions to take data management seriously. Dennis Slattery of EDMWorks moderated the panel, and on the panel with me was Sally Hinds of DCMS, and Mikael Soboen, head of risk systems at BNP Paribas. 

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BCBS239 Panel at DMS London

Dennis started by outlining the four pillars of BCBS239:

  • Pillar 1 “Overarching Governance and Infrastructure.”
  • Pillar 2 “Risk aggregation” capabilities.
  • Pillar 3 “Risk reporting” capabilities.
  • Pillar 4 “Supervisory review, tools and cooperation."

Regulatory Chicken - Dennis started by asking the panel whether BCBS was another game of regulatory "chicken" where the approach of "principles" means 1) the banks do the minimum and wait for the banks to inspect and tell them what they specifically have to do 2) the regulators don't really want to be more specific beyond principles because they themselves are unsure of what is needed and want to learn from what different banks have done. General concensus from the panel debate was that firms were not doing as much as they could, but that banks needed to show at least that they had a program in place and running by the January 2016 deadline or face big issues with the regulators (so the game of regulatory chicken is "on" seems to be the conclusion). Mikael Soboen added that he was unsure whether his regulator would have the time to conduct the BCBS239 given the workload that the regulators currently faced. 

The End of Spreadsheets? - Dennis asked whether BCBS239 and the requirements for having a clear data lineage meant this sounded the bell for the end of spreadsheet usage at banks. I said not - I personally feel that a lot of folks in technology underestimate how difficult using software is for many business users and tools that make manipulating data easy like spreadsheets will have a role for the foreseeable future. I suggested that spreadsheets are a great adhoc reporting and analysis tool, and things mainly go wrong when they are used as a personal, "siloed" desktop database.

BCBS239 does not itself preclude the usage of spreadsheets and end user computing, but rather like a lot of regulation says that their usage must be taken seriously - in my view there is a tendency for some in IT to regard spreadsheets as someone else's problem, which is understandable but problematic for any CDO. Also there are approaches to spreadsheet usage that can help maintain data lineage, such as what Microsoft offers with web provision of spreadsheet dashboards using PowerView and PowerBI (used in our TimeScape MarketPlace offering), folks such as Cluster7 with their "closed circuit TV" for spreadsheet monitoring, and indeed Xenomorph with our SpreadSheet Inside approach of including centralised spreadsheet-like calculations as a supported data type within the audited data management process.

Data Dictionary - Mikael said that one responsibility he had was to represent the investment bank within the wider data dictionary initiatives due to BCBS239 at the retail bank, and said that this was challenging given the different terminology sometimes used. 

Is BCBS239 a Project or Data Governance? - The panel thought that the best approach was to use BCBS239 as a framework for compliance with current regulation and regulation to come, but that this needs to obviously be subject to having the budget to do so. There were some general comments on how the data management needs of the front office and risk were converging. Standards such as FIBO were also discussed, with feedback being that they are desirable but that it is early days where their immaturity means they are often used for specific areas such as modeling counterparty data. 

Overall a good panel (I hope!) with a good amount of audience questions and participation. Again you can find some of the main points from the various panels at via @XenomorphNews or more generally by #dmslondon (you could also find out a bit via my twitter account @TheLongSentance so long as you don't mind the odd photograph and a few bits of personal baggage now and again).

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A bit of fun - Brian looking up to Ron Wilbraham at DMS London

11 September 2014

A-Team DMS Awards 2014 - Xenomorph on the Cloud

A-Team’s DMS Data Management Awards close on the 26th of September so if you haven't already, please vote for Xenomorph!

Xenomorph on the Cloud - First of a few lookbacks at what we have been doing over the past year - firstly with a short animation about one of our major initiatives this year, cloud provision of data management and a new venture into cloud-based data publishing with the TimeScape MarketPlace

So it would be fantastic if you could support Xenomorph by voting here

Thank you!

01 July 2014

Cloud, data and analytics in London - thanks for coming along!

We had over 60 folks along to our event our the Merchant Taylors' Hall last week in London. Thanks to all who attended, all who helped with the organization of the event and sorry to miss those of you that couldn't come along this time.

Some photos from the event are below starting with Brad Sevenko of Microsoft (Director, Capital Markets Technology Strategy) in the foreground with a few of the speakers doing some last minute adjustments at the front of the room before the guests arrived:

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Rupesh Khendry of Microsoft (Head of World-Wide Capital Markets Solutions) started off the presentations at the event, introducing Microsoft's capital markets technology strategy to a packed audience:

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After a presentation by Virginie O'Shea of Aite Group on Cloud adoption in capital markets, Antonio Zurlo (below) of Microsoft (Senior Program Manager) gave a quick introduction to the services available through the Microsoft Azure cloud and then moved on to more detail around Microsoft Power BI:

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After Antonio, then yours truly (Brian Sentance, CEO, Xenomorph) gave a presentation on what we have been building with Microsoft over the past 18 months, the TimeScape MarketPlace. At this point in the presentation I was giving some introductory background on the challenges of regulatory compliance and the pros and cons between point solutions and having a more general data framework in place:

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The event ended with some networking and further discussions. Big thanks to those who came forward to speak with me afterwards, great to get some early feedback.

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24 June 2014

Cloud, data and analytics in London. Tomorrow Wednesday 25th June.

One day to go until our TimeScape MarketPlace breakfast briefing "Financial Markets Data and Analytics. Everywhere You Need Them" at Merchant Taylor's Hall tomorrow, Wednesday June 25th. With over ninety people registered so far it should be a great event, but if you can make it please register and come along, it would be great to see you there.

19 June 2014

Cloud, data and analytics in London. Next Wednesday June 25th.

Less than one week to go until our TimeScape MarketPlace breakfast briefing "Financial Markets Data and Analytics. Everywhere You Need Them" at Merchant Taylor's Hall on Wednesday June 25th. 

Come and join Xenomorph, Aite Group and Microsoft for breakfast and hear Virginie O'Shea of the analyst firm Aite Group offering some great insights from financial institutions into their adoption of cloud technology, applying it to address risk management, data management and regulatory reporting challenges.

Microsoft will be showing how their new Power BI can radically change and accelerate the integration of data for business and IT staff alike, regardless of what kind of data it is, what format it is stored in or where it is located.

And Xenomorph will be demonstrating the TimeScape MarketPlace, our new cloud-based data mashup service for publishing and consuming financial markets data and analytics. 

In the meantime, please take a look at the event and register if you can come along, it would be great to see you there.

11 June 2014

Financial Markets Data and Analytics. Everywhere London Needs Them.

Pleased to announce that our TimeScape MarketPlace event "Financial Markets Data and Analytics. Everywhere You Need Them" is coming to London, at Merchant Taylor's Hall on Wednesday June 25th. 

Come and join Xenomorph, Aite Group and Microsoft for breakfast and hear Virginie O'Shea of the analyst firm Aite Group offering some great insights from financial institutions into their adoption of cloud technology, applying it to address risk management, data management and regulatory reporting challenges.

Microsoft will be showing how their new Power BI can radically change and accelerate the integration of data for business and IT staff alike, regardless of what kind of data it is, what format it is stored in or where it is located.

And Xenomorph will be demonstrating the TimeScape MarketPlace, our new cloud-based data mashup service for publishing and consuming financial markets data and analytics. 

In the meantime, please take a look at the event and register if you can come along, it would be great to see you there.

14 May 2014

Clients and Partners. Everywhere You Need Them.

Quick thank you to the clients and partners who took some time out of their working day to attend our breakfast briefing, "Financial Markets Data and Analytics. Everywhere You Need Them." at Microsoft's Times Square offices last Friday morning. Not particularly great weather on here in Manhattan so it was great to see around 60 folks turn up...

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Rupesh Khendry of Microsoft (Head of World-Wide Capital Markets Solutions) started the event and set out the agenda for the morning. Rupesh described the expense of data within financial markets, and the difficulties experienced by risk managers in pulling together all the data and analytics they need...  Photo 2
 
 ...and following Rupesh was Antonio Zurlo (below) of Microsoft (Senior Program Manager) who explained the fundamentals of Microsoft Azure and what services and infrastructure it offers, including public cloud, virtual private cloud and hybrid cloud architectures. Antonio also described a key usage pattern for HPC/grid on Azure being used to "burst to the cloud" when on-premise infrasture needs to be extended for end/intra-day risk calcs...
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Sang Lee (below) of Aite Group (Managing Partner) then delivered his presentation "Floating in the Capital Markets Cloud: Moving Beyond Data Storage". Sang's main findings from the survey of 20 financial institutions were that concerns about security and SLAs relating to cloud usage remain, but even those that were concerned about this also said they were planning to start a cloud project within the next 24 months. Cloud technology seems to becoming more acceptable of late, and Sang said this seems to be due to regulation, cost pressures and the desire to offer better services to clients. Sang confirmed that HPC/Grid with "burst to the cloud" is a common usage pattern and that "Data as a Service" is becoming more popular... 
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Fred Veasley (below) of Microsoft (Tech Solutions Professional) to introduce Microsoft Power BI and Office 365. Fred explained how Power BI extended the capabilities of Excel with data search (finding and retrieving publicized data sources both within an organization and over the web), its integration capabilities with standard databases, NoSQL databases, data standards such as OData and new APIs/sources of data such as Facebook. Once downloaded, the data can be shaped and merged with other datasets (for instance combining data from positions databases/systems with analytics and data from the cloud), and kept up to date automatically. In addition to Power BI, Power View enables great visualizations and interactive dashboards to be created, and once finalized these can be deployed centrally via web pages down to end users...
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After Fred, Brian Sentance (below), CEO of Xenomorph explained the origins of the TimeScape MarketPlace. Based on some discussions with Microsoft about 18 months back, the idea was effectively to firstly to get TimeScape running in the Microsoft Azure cloud, secondly to turn the data management capabilities of TimeScape "upside-down" by using it as a means to upload and publish data to the cloud and thirdly to provide one-to-many access to multiple sources of data via web interfaces and key delivery tools such as Microsoft Power BI. Put another way, without any local software or hardware infrastructure both business users and IT staff can access multiple data sources in the same format and using the same data model wherever the data is needed. In addition to .NET and Java interfaces to the TimeScape MarketPlace via OData, web API delivery into F#, Python, R and MATLAB are all in development...
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...and in addition to downloading data via Power BI, Brian also demonstrated how you could build on the data using "Power View" to create powerful analytical dashboard functionality that could be built and tested in Excel, then deployed centrally within a browser for access by users outside of Excel. He added that partners was one of the key aspects for the platform, and introduced the TimeScape MarketPlace Partner Program for the platform to get data, analytics, model vendors, software and service vendors involved and building on the platform. Andrew Tognela (below) of Microsoft (Worldwide Managing Director) closed the presentations...
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02 May 2014

7 days to go - Financial Markets Data and Analytics. Everywhere You Need Them.

Quick reminder that there are just 7 days left to register for Xenomorph's breakfast briefing event at Microsoft's Times Square offices on Friday May 9th, "Financial Markets Data and Analytics. Everywhere You Need Them."

With 90 registrants so far it looks to be a great event with presentations from Sang Lee of Aite Group on the adoption of cloud technology in financial markets, Microsoft showing the self-service (aka easy!) data integration capabilities of Microsoft Power BI for Excel, and introducing the TimeScape MarketPlace, Xenomorph's new cloud-based data mashup service for publishing and consuming financial markets data and analytics.

Hope to see you there and have a great weekend!

 

30 April 2014

Xenomorph Releases TimeScape Data Validation Dashboard

Very pleased to announce general availability of TimeScape Data Validation Dashboard which we announced this morning. You can see find out more here. Big thank you to all the staff and the clients involved, who have helped us to put this together over the past year. 

17 April 2014

Regulatory, Compliance, and Risk Data Technology Challenges - PRMIA

The New York Chapter of PRMIA hosted "Regulatory, Compliance, and Risk Data Technology Challenges" at Credit Suisse's offices in New York, last Thursday 10th April. Abraham Thomas introduce the panelists, and Don Wesnofske started off by setting the scene for the evening's event.

Don outlined how in reaction to the 2008 Crisis the regulators now require data retention for up to 10 years or more. Don cited one particular example where data must be reconstructed within 24 to 48 hours for any date up to 7 years back, and said that this kind of "forensic" investigation capability was an important consideration for many financial institutions. He took us through a good presentation slide of his view on data management/risk architecture, and outlined how operational risk is comprised of people, process, technology and events. Don ended his presentation by taking us through Wikipedia's definition of "Big Data", and in particular talked about how data has a life cycle going through:

  • Production
  • Retention
  • Archive
  • Purged

Don handed then handed over to Luigi Mercone of Credit Suisse who is a Director of Engineering Strategy & Architecture at Credit Suisse. Luigi started by saying that to the business at CS, he is technical support which involves asking "What is on fire today? And whats going to be on fire tomorrow?" Luigi described how some time back CS had regulatory enquiry around their equities business which required them to reconstruct data from 2 years back.

The project to do this took around 4-5 months of database adminstrators time to reconstruct the world as at that point in time (I guess because tape storage was being used, and this needed restoring to disk/database). This was for an equity order management system that had doubled in size every year for the past 17 years, and at that point CS was only retaining data going back 2 years. Luigi said that it was then thought that with new regulations requiring the ability to produce forensice evidence at any point in time would potentially swamp CS's resources unless it was addressed head on and strategically. 

Luigi described the original architecture that they were using being based on an in-memory database for intraday workloads, then standard Sybase (probably ASE I guess) and then Sybase IQ for longer term archiving, taking advantage of the column-store capabilities of Sybase IQ and the resulting data compression possible. He added that the data storage requirements of the system had grown from 150TB to 1.2PB in 4 years.

Luigi then offered a comparison of this original architecture with what he found by implementing RainStor, in the original architecture the Sybase IQ database compressed data down into 160TB, whereas this was improved by a further factor of 10 down to 14TB using RainStor. He said that the RainStor was self-service providing a standard SQL interface, eliminated the need for tape storage, reduced the system "footprint" by 90% at CS, was 1/5 of the cost and the performance was good. (I guess here I would like to caveat that I know nothing of the original architecture other than the summary Luigi provided, and as such it is hard to judge whether the original architecture was optimal for the data growth experienced, and hence whether this was overall an objective comparison of Sybase IQ's capabilities with RainStor.) Luigi closed by saying that whilst RainStor was a great archive database, its original origins were in in-memory databases and he would encourage RainStor to re-enter that market too, given his experience so far. 

John Bantleman CEO of RainStor took over and described how RainStor had been designed specifically for the needs of data archiving (I guess talking more about what it does now rather than its origins outlined by Luigi above). He said that RainStor offers a 20-40x storage footprint reduction over traditional database technology and operates efficiently even at the PetaByte (PB) scale, based around RainStor proprietary database technology making use of columnar storage and being capable of storing data in both relational-style tabular format and also in more "document" style using XML and JSON formats using Key-Value access. John mention that in terms of being able to store data that not only could RainStor retrieve data at a point in time, but it could retrieve the schema being used at that point in time for a more complete view of the state of the world at that point. This echos a couple of past articles that I have penned, one for IRD and one for Wilmott Magazine on bitemporal regulatory requirements.

John said that regulation was driving the need for data archiving capabilities, with 1400 regulations added since 2008 (not sure of source, but believable) and the comment from a Chief Data Officer (CDO) at one financial markets client that if a project wasn't driven by regulatory compliance then the project isn't going to get done (certainly sounds like regulatory overload). John's opening remarks were really around how regulatory cost, complexity and compliance were driving forces behind the growth of RainStor in financial services technology, and whilst regulation is the driver, firms should look at archiving of data as an opportunity too, in order to create value from corporate memory, and to be proactive in addressing future reporting and analysis needs.

John illustrated the regulatory need for data archiving through the Consolidated Audit Trail (CAT) regulation with data retention over 7 years will generate 100PB of data. He also mentioned SEC Rule 17a-4 for broker dealers as another example of "data retention" regulation, with particular reference to storage of records in on-rewriteable, non-erasable format. John termed this WORM storage, meaning Write Once, Read Many. John seemed to imply that both the software (RainStor) and the hardware it runs on (e.g. EMC or Teradata etc) need to be WORM compliant. One of the audience members asked John about BCBS 239, to which John said that he didn't know that particular regulation (fair enough that John didn't know in my opinion, RainStor's tech is general about "data" and is applicable across many industries, whereas BCBS 239 is obviously about banks specifically and is more about data aggregation and reporting than data retention/archiving to my understanding, and this seems to be confirmed with a quick doc scan for "archive" or "retention".)

To finish off the main part of the event (before the drinks and food began) there was a panel discussion. Luigi said that it was best to "prepare for all time, not just specifics" with respect to data retention and that there were dangers in rolling up data (effectively aggregating and loosing granularity to reduce storage needs). John added that his definition of "Big Data" was "All information, for ever". Luigi added that implementing RainStor had allowed CS to spend more time on interesting questions rather than on database restoration. John proposed that version 1 of Big Data involved the retention of web data, and as such loosing a data point here and their didn't matter. Version 2 of Big Data is concerned more with enterprise data where all data has value and needs to be retained i.e. lots of high value data. He added that this was an opportunity for risk and compliance to become an asset. 

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Abraham (second from left), Don (center) and John (second from right)

Overall it was a good event which I found very interesting (but I have to admit to a certain geeky interest in this kind of tech). The event would have benefitted from say another competitive or complementary technology vendor involved maybe, plus maybe an academic to give a different slant on data retention and on what the regulators hope to gain from this kind of mandated data retention. Not that the regulators have been that good at managing data themselves recently.

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 Networking afterwards courtesy of Credit Suisse and RainStor

 

 

 

 

 

 

 

15 April 2014

Financial Markets Data and Analytics. Everywhere You Need Them.

Very pleased to announce that Xenomorph will be hosting an event, "Financial Markets Data and Analytics. Everywhere You Need Them.", at Microsoft's Times Square New York offices on May 9th.

This breakfast briefing includes Sang Lee of the analyst firm Aite Group offering some great insights from financial institutions into their adoption of cloud technology, applying it to address risk management, data management and regulatory reporting challenges.

Microsoft will be showing how their new Power BI can radically change and accelerate the integration of data for business and IT staff alike, regardless of what kind of data it is, what format it is stored in or where it is located.

And Xenomorph will be introducing the TimeScape MarketPlace, our new cloud-based data mashup service for publishing and consuming financial markets data and analytics. More background and updates on MarketPlace in coming weeks.

In the meantime, please take a look at the event and register if you can come along, it would be great to see you there.

31 March 2014

Innovations in Liquidity Risk Management - PRMIA

PRMIA put on an event at MSCI on Wednesday, called "Innovations in Liquidity Risk Management".

 

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Melissa Sexton of Morgan Stanley introduced the agenda, saying that the evening would focus on three aspects of liquidity risk management:

  • methodology
  • industry practice
  • regulation

LiquidityMetrics by MSCI - Carlo Acerbi of MSCI then took over with his presentation on "LiquidityMetrics". Carlo said that he was pleased to be involved with MSCI (and RiskMetrics, aquired by MSCI) in that it had helped to establish and define standards for risk management that were used across the industry. He said that liquidity risk management was difficult because:

  • Clarity of Definition - Carlo suggest that if he asked the audience to define liquidity risk he would receive 70 differing definitions. Put another way, he suggested that liquidity risk was "a strange animal with many faces".
  • Data Availability - Carlo said that there were aspects of the market that we unobservable and hence data was scarce/non-existent and as such this was a limit on the validity of the models that could be applied to liquidity risk.

Carlo went on to clarify that liquidity risk was different depending upon the organization type/context being considered, with banks obviously focusing on funding. He said that LiquidityMetrics was focused on asset liquidity risk, and as such was more applicable to the needs of asset managers and hedge funds given recent regulation such as UCITS/AIFMD/FormPF. The methodology is aimed at bringing traditional equity market impact models out from the trading floor across into risk management and across other asset classes. 

Liquidity Surfaces - LiquidityMetrics measures the expected price impact for an order of a given size, and as such has dimensions in:

  • order size
  • liquidity time horizon
  • transaction costs

The representation shown by Carlo was of a "liquidity surface" with x dimension of order size (both bid and ask around 0), y dimension of time horizon for liquidation and z (vertical) dimension of transaction cost. The surface shown had a U-shaped cross section around zero order size, at which the transaction cost was half the bid-ask spread (this link illustrates my attempt at verbal visualization). The U-shape cross section indicates "Market Impact", its shape over time "Market Elasticity" and the limits for what it is observable "Market Depth". 

Carlo then moved to consider a portfolio of instruments, and how obligations on an investment fund (a portfolio) can be translated into the estimated transaction costs of meeting this obligations, so as to quantify the hidden costs of redemption in a fund. He mentioned that LiquidityMetrics could be used to quantify the costs of regulations such as UCITS/AIFMD/FormPF. There was some audience questioning about portfolios of foreign assets, such as holding Russian Bonds (maybe currently topical for an audience member maybe?). Carlo said that you would use both the liquidity surfaces for both the bond itself and the FX transaction (and in FX, there is much data available). He was however keen to emphasize that LiquidityMetrics was not intended to be used to predict "regime change" i.e. it is concerned with transaction costs under normal market conditions). 

Model Calibration - In terms of model calibration, then Carlo said that the established equity market impact models (see this link for some background for instance) have observable market data to work with. In equity markets, traditionally there was a "lit" central trading venue (i.e. an exchange) with a star network of participants fanning out from it. In OTC markets such as bonds, there is no star network but rather many to many linkages establised between all market participants, where each participant may have a network of connections of different size. As such there has not been enough data around to calibrate traditional market impact models for OTC markets. As a result, Carlo said that MSCI had implemented some simple models with a relatively small number of parameters. 

Two characteristics of standard market impact models are:

  1. Permanent Effects - this is where the fair price is impacted by a large order and the order book is dragged along to follow this.
  2. Temporary Effects - this is where the order book is emptied but then liquidity regenerates

Carlo said that the effects were obviously related to the behavioural aspects of market participants. He said that the bright side for bonds (and OTC markets) was given that the trades are private there was no public information, and price movements were often constrained by theoretical pricing, therefore permanent effects could be ignored and the fair price is insenstive to trading (again under "normal" market conditions). Carlo then moved on to talk about some of the research his team was doing looking at the shape of the order book and the time needed to regenerate it. He talked of "Perfectly Elastic" markets that digest orders immediately and "Perfectly Plastic" markets that never regenerate, and how "Relaxation Time" measures in days how long the market takes to regenerate the order book. 

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Liquidity Observatory - Carlo described how the data was gathered from market participants on a monthly basis using a spreadsheet to categorize the bond/asset class type, and again using simple parameters from active "expert" traders. Take a look at this link and sign up if this is you. (This sounded to me a lot like another "market consensus" data gathering exercise which are proving increasingly popular, such as one the first I had heard of many years back in Totem - we are not quite fully ready for "crowdsourcing" in financial markets maybe, but more people are seeing sense in sharing data.). 

Panel Debate - Ron Papenek of MSCI was moderator of the panel, and asked Karen Cassidy of Morgan Stanley about her experiences in liquidity risk management.

Liqudity Risk Management at Banks - Karen started by saying that in liquidity management at Morgan Stanley they look at:

  • Funding
  • Operating Capital
  • Client Behaviour

Since 2008, Karen said that liquidity management had become a lot more rigorous and formalized, being rule based and using a categorisation of assets held from highly liquid to highly illiquid. She said that Morgan Stanley undertake stress testing by market and also by idiosyncratic risk over time frames of 1 month and 1 year. As part of this they are assessing the minimum operating liquidity needed based on working capital needs. 

Karen added that Morgan Stanley are expending a lot of effect currently on data collection and modelling given that their data is specific to a retail broker-dealer unit, unlike many other firms. They are also looking at metrics around financial advisors, and how many clients follow the financial advisor when he or she decides to switch firms. 

Business or Regulation Driving Liquidity Risk Management - Ron asked Karen what were the drivers of their processes at Morgan Stanley. Karen said that in 2008 the focus was on fundability of assets, saying that the FED was monitoring this on a daily basis. She made the side comment that this monitoring was not unusual since "Regulators live with us anyway". Karen said that it was the responsibility of firms to come up with the controls and best practice needed to manage liquidity risk, and that is what Morgan Stanley do anyway.

Karen added that in her view the industry was over-funding and funding too long in response to regulation, and that funding would be at lower but still pragmatic levels in the absence of regulatory pressure. Like many in the industry, Karen thought the regulation had swung too far in response to the 2008 crisis and would eventually swing back to more normal levels. 

Carlo added that he had written an unintentionally prescient academic paper on liquidity management in 2008 just prior to the crisis hitting, and he thought the regulators certainly arrived "after" the crisis rather than anticipating it in any way. He thought that the banks have anticipated the regulators very well with measures such as LCR and SFR already in place. 

In contrast, Carlo said that the regulators were lost in dealing with liquidity risk management for asset managers and hedge funds, with regulation such as UCITS being very vague on this topic and regulators themselves seeking guidance from the industry. He recounted a meeting he had with BaFin in 2009 where he told them that certain of their regulations made no sense and he said they acknowledge this and said the asset management industry needed to tell them what to implement (sounds like the German regulator is using the same card as the UK regulators in keeping regulations vague when they are uncertain, waiting for regulated firms to implement them to see what the regulation really becomes...). 

What Have We Learnt Since 2008 - Karen said that back in 2008 liquidity was not managed to term, funding basis was not rigorous and relied heavily on unsecured debt. She said that since then Morgan Stanley had been actively involved in shaping the requirements of better liquidity risk management with more rigorous analysis of counterparties and funding capacity. Karen said that stronger governance was a foundation for the creation of better policy and process. She said that regulators were receptive to new ideas and had been working with them closely.

What will be the effect of CCPs on OTC markets? Carlo said that when executing a large order, you have the choice between executing 1) multiple small orders with multiple counterparties or 2) a single large block order with one counterparty. In this regard, the equity and bond markets are very different. In lit equity venues, the best approach is 1), but in the bond markets approach 2) is taken since the trade information is not transparent to the market.

Obviously equity markets have become more fragmented, and this has resulted in improve market quality since it is harder to get all market information and hence the market is less resonant to big events/orders. Carlo added that with the increased transparency proposed for OTC markets with CCPs etc will this improve them? His answer was that this was likely to improve the counterparty risk inherent in the market but due to increased transaparency is likely to have a negative effect on transaction costs (I guess another example of the law of unintended consequencies for the regulators).

Audience Questions - there then followed some audience questions:

LiqidityMetrics extrapolation - one audience member asked about transaction cost extrapolation in Carlo's modelling. Carlo said that MSCI do not extrapolate and the liquidity surface terminates where the market terminates its liquidity. There was some extrapolation used along the time dimension however particularly in relation to the time-relaxation parameter. 

LiquidityMetrics "Cross-Impact" - looking at applying LiquidityMetrics to a portfolio, one audience member wondering if an order for one asset distorted the liquidity surface for other potentially related assets. Carlo said this was a very interesting area with little research done so far. He said that this "cross-impact" had not been detected in equity markets but that they were looking at it in other markets such as fixed income where effective two assets might be proxies for duration related trading. Carlo put forward a simple model of where the two assets are analogous to two species of animal feeding from the same source of food.

Long and short position liquidity modelling - one audience member asked Carlo what the effects would be of being long or short and that in a crisis you would prefer to be short (maybe obviously?) given the sell off by those with long positions. Carlo clarified that being "short" was not merely taking the negative number on a liquidity surface for a particular asset but rather a "short" is a borrowing position with an obligation to deliver a security at some defined point, and as such is a different asset with its own liquidity surface.  

Changing markets, changing participants - final question of the evening was from one member of the audience who asked if the general move out of fixed income trading by the banks over recent years was visible in Carlo's data? Carlo said that MSCI only have around two years of data so far and as such this was not yet visible but his team are looking for effects like this amongst others. He added that the August 2011 weak banks - weak sovereigns in Europe was visible with signals present in the data.

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Good food and good (really good I thought) wine put on by MSCI at the event reception. Great view of Manhattan from the 48th floor of World Trade Centre 7 too.

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25 March 2014

Risk Management in Securities Financing and Money Market Funds - PRMIA

I went along to this PRMIA event on Thursday evening hosted by Credit Suisse and sponsored by Acacia Capital. Viktoria Baklanova introduced the panel with Joseph Tenaga as MC for the panel and very quickly got a plug in for her about to be released book written with Joe on money market funds. For those of us who don't know so much about money market funds, then these are a form of interest-bearing fund that invests in short term debt securities. The funds attempt to maintain a stable Net Asset Value (NAV) but to quote Wikipedia they "are widely (though not necessarily accurately) regarded as being as safe as bank deposits yet providing a higher yield." Their role in the 2008 financial crisis echos on strongly through to the present day, with controversy of their supposedly stable NAV (typically $1 in the US) and the associated phrase "Breaking the Buck".

Joe Tenaga started the panel with an (unnecessary in my view) justification of academia, asking the rhetorical question "What is the point of academia?" to which Joe answered that "knowledge is what makes the impossible possible" and he added that knowledge drives us to make things better. Joe introduced the next panelist, Matthew Fink of Oppenheimer Mutual Funds. Matt said that we would be prepared to wager that he had worked in the money market funds area the longest of anyone in the room, having started his involvement in the industry in April of 1971. Matt gave a picture of the mutual funds industry at the time, with around $60B AUM in the US with 95% invested in equities. At that time the mutual funds industry was going through a very bad time, as the economy and markets were falling and fund redemptions were rising to such an extent that they had fallen to $30B over the next few years. At the time, if redemptions had continued at this rate the industry would have vanished.

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Against this background for the mutual funds industry, interest rates in the US were very high rising from 6% in 1969 to around 12% in 1974. So many people were paying very high rates on mortgage obligations whilst being limited to receiving only 4-5% on savings due to "Regulation Q". For wealthy individuals, it was possible to get around these savings limits, but only if you had $100,000 to put in a Commercial Deposit or $10,000 into a T-Bill. Ironically it was the regulation to remove one risk (it had been thought that competition on deposit rates had contributed to the bank failures of the Great Depression) that had sparked the drive to innovate to find higher returns and create the money market funds industry as a result, with the first fund being "The Reserve Fund" in 1971. (side comment - if regulation from the 1930's via the 1970 can cause problems in 2014, then I would have to defer to which ever Deity you worsphip to advise on what the longer-term consequencies will be of the current round of complexity being implemented...). 

The banks saw the money pouring into money market funds such as those from Fidelity and Dreyfus, and understandably wanted to be part of the party too. Some of the worries about money market funds were firstly what if a fund got into trouble? Secondly, the bank regulators were angry that funds were flowing into this new industry and were concerned that it would increase bank failures. 1979 saw a certain Paul Volcker (ever heard of him?) complaining that money market funds were acting like checking accounts. Matt said that he spoke with Volcker and said that this was not the case, to which Volcker replied that it was true since his wife's company was paying staff wages out on checks written against money market funds. 

Henry Shilling of Moody's took over from Matt and showed a few slides, firstly showing the number of funds with AAA (AAAmmf, Aaamf, AAAmf) from Fitch (49), Moody's (130) and S&P Ratings (156). Henry described how regulators have wanted to reduce the risk of funds by shortening the maturity of the debt held from 90 to 60 days, and having one and seven day liquidity windows. He showed that there is a high degree of concentration risk in the industry with the top 10 firms have 74% AUM and the top 20 covering 94% of the AUM for the industry. Similarly, looking at the assets invested in the funds, 80% are from financial institutions.

Igor Axenov of Barclays Capital then showed his slides, illustrating the composition of the funds by asset type prior to the crisis:

  • ABS related - 34%
  • Bank products - 23%
  • Repos - 15%
  • Corporate - 11%
  • Unsecured - 8%
  • Other - 6%

He said that the largest exposure then was to securitized products, with implicit indirect exposure to banks. Igor said that CDO issuance was rising at a rate of $300B per year through 2005/6/7 and that much of the structuring was done to ensure that the ABS products fitted the needs and regulations of money market funds. Detailing the ABS asset composition, Igor showed:

  • Asset Backed (AB) commercial paper - 50%
  • AB medium term notes - 24%
  • Extendible AB commercial paper - 17%
  • ABS Bonds - 5%

Igor said the asset backed commercial paper market (largely funded through money market funds) had grown to $1.2Trln by 2007, and has fallen precipitously since then down to around $200M now.

Looking at the current money market fund portfolio, it looks like:

  • Bank products - 41%
  • Repos - 18%
  • CP - 15%
  • US Govt + Agency debt - 10%
  • Asset backed CP - 9%
  • Other corporate - 4%

Terence Ma added that the Money Market Fund industry sat at 4Trln in 2008 and was now around $2.7Trln in 2014. Matthew Fink said that given his involvement in regulation that he had "never met the face of the enemy before" in Igor was the start of some lively but well-intended banter between the ex-regulator and structurer.  

Terence Ma of South Street Securities described his business, which exclusively involves repurchase agreements "Repos". Terry said that in the 1990's Citi were very disciplined on balanced sheet management and in his opinion, then adhead of the market in this regard. He that the Repo business earns small spreads and as a result needs a big balance sheet. When John Read took over Citi, he decided that he did not like the Repo business since its ROE could not compete with some of the products in retail and other parts of the business. So Terry and his partners wondered whether the Repo business could be managed off balance sheet, so they formed a broker-dealer business and when Citi merged with Salomen Brothers they span off. This was December of 2003 but by 2008 they were left "sucking wind" by the crisis.

Terry was quite explicit that his firm is not part of the "shadow banking system" but are subject to the SEC. He then described a few more things about his business, starting with his definition of a Repo as "an agreement to sell and repurchase a security at a fixed date in the future", with the objectives of providing cash inventory, leverage and short cover. All borrowings are lent out, unlike Lehman Brothers in 2008. They do not finance again structured products unless guaranteed, and only accept collateral from Fannie, Freddie and the US Government. 

Joe Tenaga then open out questions to the audience. Someone asked who the first MMF was (I think they missed the first part of the talk) and Matt said that the Keystone MMF filed first but the first was Reserve MMF (which got into trouble in 2008). Matt said that it was interesting that the same people like Paul Volcker were stilled involved with the same concerns about the industry many years on. 

The next question was how did early MMFs keep their NAV at $1? Henry said that the "Break the Buck" definition is when there is a mark to market fall of 50bp or more. He said that historically that fund sponsors had addressed any issues with breaking the buck with purchases of the fund at par or direct equity investment in the fund - they did this since the effect on their funds and the industry would be too great to comtemplate. Hence an MMF is not a perfect product but (up until Lehmans in 2008 with a 50% NAV loss) has a near-perfect record. He added that the first funds to break the buck were from Salomon's and First Chicago.

Matt added some further history saying the need to maintain the $1 NAV was initially due to the needs of some of the early investors in the industry, who could not invest in products unless they had fixed NAV. He mentioned that one of the companies, Federated, had a long running battle with the SEC over Money Market Funds, filing for exemptions to avoid some of the restrictions that the SEC was trying to impose since the SEC regarded the MMF industry as damaging the mutual funds industry. He mentioned Rule 2-a7 which defines the accountancy procedures for keeping the NAV at $1, and some of the battles around amortization and penny-rounding policies to facilitate this. To later questions, Matt said that the SEC wants a floating NAV for institutional MMFs but currently wants to leave retail alone (seems somewhat arbitrary choice i.e. lets only change what has been problematic before, ignore anything else and not contemplate what could happen if only we understood things better). He said that the SEC was weak and FSOC is driving the SEC to change (and FSOC itself is a pawn of the Federal Reserve). 

Overall an interesting panel, particularly when you have characters such as Matt Fink who know the history and stories within the industry so well. 

 

 

 

 

 

24 March 2014

#DMSLondon - The Hobgoblin of Little Minds: Risk and Regulation as Drivers

The second panel of the day was "Regulation and Risk as Data Management Drivers" - you can find the A-Team's write up here. Some of my thoughts/notes can be found below:

  • Ian Webster of Axioma responded to a question about whether consistency was the Holy Grail of data management said that there isn't consistent view possible for data used in risk and regulation - there are many regulations with many different requirements and so unnecessary data consistency is "the hobgoblin of little minds" in delaying progress and achieving goals in data management.
  • James of Lombard Risk suggest that firms should seek competitive advantage from regulatory compliance rather than just compliance alone - seeking the carrot and not just avoiding the stick.
  • Ian said he thought too many firms dealt with regulatory compliance in a tactical manner and asked if regulation and risk were truly related? He suggested that risk levels might remain unchanged even if regulation demanded a great deal more reporting.
  • Marcelle von Wendland said she thought that regulation added cost only, and that firms must focus on risk management and margin.
  • James said that "regulatory risk" was a category of risk all in itself alongside its mainstream comtempories.
  • Ian added that risk and finance think about risk differently and this didn't help in promoting consistency of ideas in discussions about risk management.
  • James said that the legacy of systems in financial markets was a hindrince in complying with new regulation and mentioned the example of the relatively young energy industry where STP was much easier to implement.
  • Laurent of Bloomberg said that young, emerging markets like energy were greenfield and as such easier to implement systems but that they did not have any experience or culture around data governance.
  • Marcelle said that the G20 initiatives around trade reporting at least promoted some consistency and allowed issues to be identified at last.
  • Ian said in response that was unconvinced about politically driven regulation, questioning its effectiveness and motivations.
  • Ian raised the issues of the assumptions behind VaR and said that the current stress tests were overdone.
  • Marcelle agreed that a single number for VaR or some other measure meant that other useful information has potentially been ignored/thrown away.
  • General consensus across the panel that fines were not enough and that restricting business activities might be a more effective stick for the regulators.
  • James reference the risk data aggregation paper from the Basel Committee and suggested that data should be capture once, cleaned once and used many times.
  • Ian disagreed with James in that he thought clean once, capture once and use many times was not practically possible and this goal was one of the main causes of failure within the data management industry over the past 10 years. 
  • The panel ended with Ian saying that we not just solve for the last crisis, but the underlying causes of crises were similar and mostly around asset price bubbles so in order to recuce risk in the system 1) lets make data more transparent and 2) do what we can to avoid bubbles with better indices and risk measures.

3 Regulation panel

 

12 March 2014

S&P Capital IQ Risk Event #2 - Enterprise or Risk Data Strategy?

Christian Nilsson of S&P CIQ followed up Richard Burtsal's talk with a presentation on data management for risk, containing many interesting questions for those considering data for risk management needs. Christian started his talk by taking a time machine back to 2006, and asking what were the issues then in Enterprise Data Management:

  1. There is no current crisis - we have other priorities (we now know what happened there)
  2. The business case is still too fuzzy (regulation took care of this issue)
  3. Dealing with the politics of implementation (silos are still around, but cost and regulation are weakening politics as a defence?)
  4. Understanding data dependencies (understanding this throughout the value chain, but still not clear today?)
  5. The risk of doing it wrong (there are risk you will do data management wrong given all the external parties and sources involved, but what is the risk of not doing it?)

Christian then moved on to say the current regulatory focus is on clearer roadmaps for financial institutions, citing Basel II/III, Dodd Frank/Volker Rule in the US, challenges in valuation from IASB and IFRS, fund management challenges with UCITS, AIFMD, EMIR, MiFID and MiFIR, and Solvency II in the Insurance industry. He coined the phrase that "Regulation Goes Hollywood" with multiple versions of regulation like UCITS I, II, III, IV, V, VII for example having more versions than a set of Rocky movies. 

He then touched upon some of the main motivations behind the BCBS 239 document and said that regulation had three main themes at the moment:

  1. Higher Capital and Liquidity Ratios
  2. Restrictions on Trading Activities
  3. Structural Changes ("ring fence" retail, global operations move to being capitalized local subsidiaries)

Some further observations were on what will be the implications of the effective "loss" of globablization within financial markets, and also what now can be considered as risk free assets (do such things now exist?). Christian then gave some stats on risk as a driver of data and technology spend with over $20-50B being spent over the next 2-3 years (seems a wide range, nothing like a consensus from analysts I guess!). 

The talk then moved on to what role data and data management plays within regulatory compliance, with for example:

  • LEI - Legal Entity Identifiers play out throughout most regulation, as a means to enable automated processing and as a way to understand and aggregate exposures.
  • Dodd-Frank - Data management plays within OTC processing and STP in general.
  • Solvency II - This regulation for insurers places emphasis on data quality/data lineage and within capital reserve requirements.
  • Basel III - Risk aggregation and counterparty credit risk are two areas of key focus.

Christian outlined the small budget of the regulators relative to the biggest banks (a topic discussed in previous posts, how society wants stronger, more effective regulation but then isn't prepared to pay for it directly - although I would add we all pay for it indirectly but that is another story, in part illustrated in the document this post talks about).

In addtion to the well-known term "regulatory arbitrage" dealing with different regulations in different jurisdictions, Christian also mentioned the increasingly used term "subsituted compliance" where a global company tries to optimise which jurisdictions it and its subsidiaries comply within, with the aim of avoiding compliance in more difficult regimes through compliance within others.

I think Christian outlined the "data management dichotomy" within financial markets very well :

  1. Regulation requires data that is complete, accurate and appropriate
  2. Industry standards of data management and data are poorly regulated, and there is weak industry leadership in this area.

(not sure if it was quite at this point, but certainly some of the audience questions were about whether the data vendors themselves should be regulated which was entertaining).

He also outlined the opportunity from regulation in that it could be used as a catalyst for efficiency, STP and cost base reduction.

Obviously "Big Data" (I keep telling myself to drop the quotes, but old habits die hard) is hard to avoid, and Christian mentioned that IBM say that 90% of the world's data has been created in the last 2 years. He described the opportunities of the "3 V's" of Volume, Variety, Velocity and "Dark Data" (exploiting underused data with new technology - "Dark" and "Deep" are getting more and more use of late). No mention directly in his presentation but throughout there was the implied extension of the "3 V's" to "5 V's" with Veracity (aka quality) and Value (aka we could do this, but is it worth it?). Related to the "Value" point Christian brought out the debate about what data do you capture, analyse, store but also what do you deliberately discard which is point worth more consideration that it gets (e.g. one major data vendor I know did not store its real-time tick data and now buys its tick data history from an institution who thought it would be a good idea to store the data long before the data vendor thought of it).

I will close this post taking a couple of summary lists directly from his presentation, the first being the top areas of focus for risk managers:

  • Counterparty Risk
  • Integrating risk into the Pre-trade process
  • Risk Aggregation across the firm
  • Risk Transparency
  • Cross Asset Risk Reporting
  • Cost Management/displacement

The second list outlines the main challenges:

  • Getting complete view of risk from multiple systems
  • Lack of front to back integration of systems
  • Data Mapping
  • Data availability of history
  • Lack of Instrument coverage
  • Inability to source from single vendor
  • Growing volumes of data

Christian's presentation then put forward a lot of practical ideas about how best to meet these challenges (I particularly liked the risk data warehouse parts, but I am unsurprisingly biassed). In summary if you get the chance then see or take a read of Christian's presentation, I thought it was a very thoughtful document with some interesting ideas and advice put forward.

 

 

 

 

 

 

 

10 March 2014

S&P Capital IQ Risk Event #1 - Managed Services

Attended a good event at S&P Capital IQ's offices on Tuesday morning last week in London, built around the BCBS 239 document on risk aggregation and reporting (see earlier PRMIA event on this topic too). A partner vendor of S&P CIQ, Tech Mahindra, started the morning with Richard Burtsal's presentation on "Delivering an Enterprise Data Strategy". Tech Mahindra recently acquired a data management platform from UBS Asset Management and are offering a managed service data management offering based on this (see A-Team article).

Richard said that he wasn't going to "sell" in his presentation (always a worrying admission from one of us data management vendors, it usually means entirely the opposite). That small criticism aside, Richard gave a solid update on the state of the industry and obviously on what Tech Mahindra are offering, and added that:

  • For every $1 spent directly on market data, the total cost of that data goes up by a factor of 6 by the time the data is actually used 
  • 33% of rejected trades are caused by incorrect reference data
  • 60% of staff manipulate, report on or support data on a daily basis (I wonder what the other 40% actually do then? Be good to get the Tower Group report this came from to find out maybe?)
  • 25% of reference data management is wasted due to duplication and inefficiences
  • In their work with UBS Asset Management they had jointly shown that the cost of data management were reduced by 25-30% using a managed service (sounds worth verifying what the "before" situation was I guess, but interesting/impressive).
  • Clients were pushing for much faster instrument setup and a reduction in time from the 1-2 weeks setup in some systems.

There were a few questions from the audience during Richard's talk, the first asked about the differences in doing data management with the buy-side and data management on the sell-side. Richard said that his experience was that the buy-side managed less instruments (<500,000) but with greater depth of data, and sell-side held more instruments (10M+) but with less depth of data (not sure that completely reflects my experience, but sounds worth a survey maybe). 

The second question was why is the utility model for data management going to succeed right now, when previous attempts over the past 10 years had failed? Richard responded that he thought Tech Mahindra would succeed due to:

  • Tech Mahindra are data-vendor agnostic (I assume aimed at Markit-Cadis and Bloomberg-PolarLake)
  • Tech Mahindra own all their own IP (hmm, not really so sure this is a good reason or even a differentiator, but a I guess aimed at managed services that are not run by the firm that develops the data management system?)

I think the answers to this second question need thinking through more clearly, to be fair Richard had stated the 25% cost reduction already as one benefit, and various folks have said that the technology is ripe for these kinds of offerings now, but all the same the response need to be more fully developed to convince many I think (I remain undecided personally, it would be good to have some more evidence to back this up). One of the S&P CIQ added that what he thinks clients want is "Utility of Delivery" and not "Utility of Content" which I thought was a sensible comment and one that I will be revisiting in the coming months. 

On a related note to why managed services just now, another audience member asked how client specific data was managed within a utility or managed service model, and Richard said that client specific data was often managed at the client but that they can upload and integrate client generated data into the managed service offering. I think this is a very key issue within the debate about managed services and utilities, I mean I get the point the data utility proponents make that certain datasets are simple "facts" as such are either write or wrong and hence commoditisable, but much of the data is subjective and all of the data needs validating together in the context of its intended use in my view. I guess I kind of loose myself in looping arguments about why data utility vendors aren't ultimately wanting to be the next Thomson Reuters or Bloomberg (not that that is not a laudible aim but it is not going to change the world or indeed financial markets data provision very much).

 

 

03 March 2014

See you at the A-Team Data Management Summit this week!

Xenomorph is sponsoring the networking reception at the A-Team DMS event in London this week, and if you are attending then I wanted to extend a cordial invite to you to attend the drinks and networking reception at the end of day at 5:30pm on Thursday.

In preparation for Thursday’s Agenda then the blog links below are a quick reminder of some of the main highlights from last September’s DMS:

I will also be speaking on the 2pm panel “Reporting for the C-Suite: Data Management for Enterprise & Risk Analytics”. So if you like what you have heard during the day, come along to the drinks and firm up your understanding with further discussion with like-minded individuals. Alternatively, if you find your brain is so full by then of enterprise data architecture, managed services, analytics, risk and regulation that you can hardly speak, come along and allow your cerebellum to relax and make sense of it all with your favourite beverage in hand. Either way your you will leave the event more informed then when you went in...well that’s my excuse and I am sticking with it!

Hope to see you there!

23 October 2013

Model Risk Management from PRMIA

Guest blog post by Qi Fu of PRMIA and Credit Suisse NYC with some notes on a model risk management event held ealier in September of this year. Big thank you to Qi for his notes and to all involved in organising the event:

The PRMIA event on Model Risk Management (MRM) was held in the evening of September 16th at Credit Suisse.  The discussion was sponsored by Ernst & Young, and was organized by Cynthia Williams, Regulatory Coordinator for Americas at Credit Suisse. 

As financial institutions have shifted considerable focus to model governance and independent model validation, MRM is as timely a topic as any in risk management, particularly since the Fed and OCC issued the Supervisory Guidance on Model Risk Management, also known as SR 11-7.

The event brings together a diverse range of views: the investment banks Morgan Stanley, Bank of American Merrill Lynch, and Credit Suisse are each represented, also on the panel are a consultant from E&Y and a regulator from Federal Reserve Bank of NY.  The event was well attended with over 100 attendees.

Colin Love-Mason, Head of Market Risk Analytics at CS moderated the panel, and led off by discussing his 2 functions at Credit Suisse, one being traditional model validation (MV), the other being VaR development and completing gap assessment, as well as compiling model inventory.  Colin made an analogy between model risk management with real estate.   As in real estate, there are three golden rules in MRM, which are emphasized in SR 11-7: documentation, documentation, and documentation.  Looking into the future, the continuing goals in MRM are quantification and aggregation.

Gagan Agarwala of E&Y’s Risk Advisory Practice noted that there is nothing new about many of the ideas in MRM.  Most large institutions already have in place guidance on model validation and model risk management.  In the past validation consisted of mostly quantitative analysis, but the trend has shifted towards establishing more mature, holistic, and sustainable risk management practices. 

Karen Schneck of FRBNY’s Models and Methodology Department spoke about her role at the FRB where she is on the model validation unit for stress testing for Comprehensive Capital Analysis and Review (CCAR); thus part of her work was on MRM before SR 11-7 was written.  SR 11-7 is definitely a “game changer”; since its release, there is now more formalization and organization around the oversight of MRM; rather than a rigid organization chart, the reporting structure at the FRB is much more open minded.  In addition, there is an increased appreciation of the infrastructure around the models themselves and the challenges faced by practitioners, in particularly the model implementation component, which is not always immediately recognized.

Craig Wotherspoon of BAML Model Risk Management remarked on his experience in risk management, and comments that a new feature in the structure of risk governance is that model validation is turning into a component of risk management.  In addition, the people involved are changing: risk professionals with the combination of a scientific mind, business sense, and writing skills will be in as high demand as ever.

Jon Hill, Head of Morgan Stanley’s Quantitative Analytics Group discussed his past experience in MRM since 90’s, when then the primary tools applied were “sniff tests”.  Since then, the landscape has long been completely changed.  In the past, focus had been on production, while documentation of models was an afterthought, now documentation must be detailed enough for highly qualified individual to review.  In times past the focus was only around validating methodology, nowadays it is just as important to validate the implementation.  There is an emphasis on stress testing, especially for complex models, in addition to internal threshold models and independent benchmarking.  The definition of what a model is has also expanded to anything that takes numbers in and haves numbers as output.  However, these increased demands require a substantial increase in resources; the difficulty of recruiting talent in these areas will remain a major challenge.

Colin noted a contrast in the initial comments of the panelists, on one hand some are indicating that MRM is mostly common sense; but Karen in particular emphasized the “game-changing” implications of SR 11-7, with MRM becoming more process oriented, when in the past it had been more of an intellectual exercise.  With regards to recruitment, it is difficult to find candidates with all the prerequisite skill sets, one option is to split up the workload to make it easier to hire.

Craig noted the shift in the risk governance structure, the model risk control committees are defining what models are, more formally and rigorously.  Gagan added that models have lifecycles, and there are inherent risks associated within that lifecycle.  It is important to connect the dots to make sure everything is conceptually sound, and to ascertain that other control functions understand the lifecycles.

Karen admits that additional process requirements contain the risk of trumping value.  MRM should aim to maintain high standards while not get overwhelmed by the process itself, so that some ideas become too expensive to implement.  There is also the challenge of maintaining independence of the MV team.

Jon concurred with Karen on the importance of maintaining independence.  A common experience is when validators find mistakes in the models, they become drawn into the development process with the modelers.  He also notes differences with the US, UK, and European MV processes, and Jon asserts his view that the US is ahead of the curve and setting standards.

Colin noted the issue of the lack of an analogous PRA document to SR 11-7, that drills down into nuts and bolts of the challenges in MRM.  He also concurred on the difficulty of maintaining independence, particularly in areas with no established governance.  It is important to get model developers to talk to other developers about the definition and scope of the models, as well as possible expansion of scope.  There is a wide gamut of models: core, pricing, risk, vendor, sensitivity, scenarios, etc.  Who is responsible for validating which?  Who checks on the calibration, tolerance, and weights of the models?  These are important questions to address.

Craig commented further on the complexity and uncertainty of defining what a model is, and on whose job it is to determine that, amongst the different stakeholders.  It also needs to be taken into consideration that model developers maybe biased towards limiting the number of models.

Gagan followed up by noting that while the generic definition of models is broad, and will need to be redefined, but analytics do not all need to have the same standards, the definition should leave some flexibility for context.  Also, the highest standard should be assigned to risk models.

Karen adds that, defining and validating models used to have a narrow focus, and done in a tailor-controlled environment.  It would be better to broaden the scope, and to reexamine the question on an ongoing basis (it is however important to point out that annual review does not equal annual re-validation).  In addition to the primary models, some challenge models also need to be supported; developers should discuss why they’re happy with primary model, how it is different from challenger model, and how it impacts output.

Colin brought up the point of stress-testing.  Jon asserts that stress-testing is more important for stochastic models, which are more likely to break under nonsensical inputs.  Also any model that plugs into the risk system should require judicious decision-making, as well as annual reviews to look at changes since the previous review.

Colin also brought up the topic of change management: what are the system challenges when model developers release code, which may include experimental releases.  Often discussed are concepts of annual certification and checkpoints.  Jon commented that the focus should be on changes of 5% or more, with pricing model being less of a priority; and firms should move towards centralized source code depositories.

Karen also added the question of what ought to considered material change: the more conservative answer is any variation, even if a pure code change that didn’t change model usage or business application, may need to be communicated to upper management.

Colin noted that developers often have a tendency to encapsulate intentions, and have difficulty or reluctance to document changes, thus resulting in many grey areas.  Gagan added that infrastructure is crucial.  Especially when market conditions are rapidly changing, MRM need to have controls that are in place.  Also, models are in Excel make the change management process more difficult.  

The panel discussion was followed by a lively Q&A session with an engaged audience, below are some highlights.

Q:  How do you distinguish between a trader whose model actually needs change, versus a trader who is only saying so because he/she has lost money?

Colin:  Maintain independent price verification and control functions.

Craig:  Good process for model change, and identify all stakeholders.

Karen:  Focus on what model outputs are being changed, what the trader’s assumptions are, and what is driving results.

Q:  How do you make sure models are used in business in a way that makes sense?

Colin:  This can be difficult, front office builds the models, states what is it good for, there is no simple answer from the MV perspective; usage means get as many people in the governance process as possible, internal audit and setting up controls.

Gagan:  Have coordination with other functions, holistic MRM.

Karen:  Need structure, inventory a useful tool for governance function.

Q:  Comments on models used in the insurance industry?

Colin:  Very qualitative, possible to give indications, difficult to do exact quantitative analysis, estimates are based on a range of values.  Need to be careful with inputs for very complex models, which can be based on only a few trades.

Q:  What to do about big shocks in CCAR?

Jon:  MV should validate for severe shocks, and if model fails may need only simple solution.

Karen:  Validation tools, some backtesting data, need to benchmark, quant element of stress testing need to substantiated and supported by qualitative assessment.

Q:  How to deal with vendor models?

Karen:  Not acceptable just to say it’s okay as long as the vendor is reputable, want to see testing done, consider usage also compare to original intent.

Craig:  New guidance makes it difficult to buy vendors models, but if vendor recognizes this, this will give them competitive advantage.

Q:  How to define independence for medium and small firms?

Colin:  Be flexible with resources, bring in different people, get feedback from senior management, and look for consistency.

Jon:  Hire E&Y?  There is never complete independence even in a big bank.

Gagan:  Key is the review process.

Karen:  Consultants could be cost effective; vendor validation may not be enough.

Q:  At firm level, do you see practice of assessing risk models?

Jon:  Large bank should appoint Model Risk Officer.

Karen:  Just slapping on additional capital is not enough

Q:  Who actually does MV?

Colin:  First should be user, then developer, 4 eyes principle.

Q:  Additional comments on change management?

Colin:  Ban Excel for anything official; need controlled environment.

 

21 October 2013

Credit Risk: Default and Loss Given Default from PRMIA

Great event from PRMIA on Tuesday evening of last week, entitled Credit Risk: The link between Loss Given Default and Default. The event was kicked off by Melissa Sexton of PRMIA, who introduced Jon Frye of the Federal Reserve Bank of Chicago. Jon seems to an acknowledged expert in the field of Loss Given Default (LGD) and credit risk modelling. I am sure that the slides will be up on the PRMIA event page above soon, but much of Jon's presentation seems to be around the following working paper. So take a look at the paper (which is good in my view) but I will stick to an overview and in particular any anecdotal comments made by Jon and other panelists.

Jon is an excellent speaker, relaxed in manner, very knowledgeable about his subject, humourous but also sensibly reserved in coming up with immediate answers to audience questions. He started by saying that his talk was not going to be long on philosophy, but very pragmatic in nature. Before going into detail, he outlined that the area of credit risk can and will be improved, but that this improvement becomes easier as more data is collected, and inevitably that this data collection process may need to run for many years and decades yet before the data becomes statistically significant. 

Which Formula is Simpler? Jon showed two formulas for estimating LGD, one a relatively complex looking formula (the Vasicek distribution mentioned his working paper) and the other a simple linear model of the a + b.x. Jon said that looking at the two formulas, then many would hope that the second formula might work best given its simplicity, but he wanted to convince us that the first formula was infact simpler than the second. He said that the second formula would need to be regressed on all loans to estimate its parameters, whereas the first formula depended on two parameters that most banks should have a fairly good handle on. The two parameters were Default Rate (DR) and Expected Loss (EL). The fact that these parameters were relatively well understood seemed to be the basis for saying the first formula was simpler, despite its relative mathematical complexity. This prompted an audience question on what is the difference between Probability of Default (PD) and Default Rate (DR). Apparently it turns out PD is the expected probability of default before default happens (so ex-ante) and DR is the the realised rate of default (so ex-post). 

Default and LGD over Time. Jon showed a graph (by an academic called Altman) of DR and LGD over time. When the DR was high (lots of companies failing, in a likely economic downtown) the LGD was also perhaps understandably high (so high number of companies failing, in an economic background that is both part of the causes of the failures but also not helping the loss recovery process). When DR is low, then there is a disconnect between LGD and DR. Put another way, when the number of companies failing is low, the losses incurred by those companies that do default can be high or low, there is no discernable pattern. I guess I am not sure in part whether this disconnect is due to the smaller number of companies failing meaning the sample space is much smaller and hence the outcomes are more volatile (no averaging effect), or more likely that in healthy economic times the loss given a default is much more of random variable, dependent on the defaulting company specifics rather than on general economic background.

Conclusions Beware: Data is Sparse. Jon emphasised from the graph that the Altman data went back 28 years, of which 23 years were periods of low default, with 5 years of high default levels but only across 3 separate recessions. Therefore from a statistical point of view this is very little data, so makes drawing any firm statistical conclusions about default and levels of loss given default very difficult and error-prone. 

The Inherent Risk of LGD. Jon here seemed to be focussed not on the probability of default, but rather on the conditional risk that once a default has occurred then how does LGD behave and what is the risk inherent from the different losses faced. He described how LGD affects i) Economic Capital - if LGD is more variable, then you need stronger capital reserves, ii) Risk and Reward - if a loan has more LGD risk, then the lender wants more reward, and iii) Pricing/Valuation - even if the expected LGD of two loans is equal, then different loans can still default under different conditions having different LGD levels.

Models of LGD

Jon showed a chart will LGC plotted against DR for 6 models (two of which I think he was involved in). All six models were dependent on three parameters, PD, EL and correlation, plus all six models seemed to produce almost identical results when plotted on the chart. Jon mentioned that one of his models had been validated (successfully I think, but with a lot of noise in the data) against Moody's loan data taken over the past 14 years. He added that he was surprised that all six models produced almost the same results, implying that either all models were converging around the correct solution or in total contrast that all six models were potentially subject to "group think" and were systematically all wrong in the ways the problem should be looked at.

Jon took one of his LGD models and compared it against the simple linear model, using simulated data. He showed a graph of some data points for what he called a "lucky bank" with the two models superimposed over the top. The lucky bit came in since this bank's data points for DR against LGD showed lower DR than expected for a given LGD, and lower LGD for a given DR. On this specific case, Jon said that the simple linear model fits better than his non-linear one, but when done over many data sets his LGD model fitted better overall since it seemed to be less affected by random data.

There were then a few audience questions as Jon closed his talk, one leading Jon to remind everyone of the scarcity of data in LGD modelling. In another Jon seemed to imply that he would favor using his model (maybe understandably) in the Dodd-Frank Annual Stress Tests for banks, emphasising that models should be kept simple unless a more complex model can be justified statistically. 

Steve Bennet and the Data Scarcity Issue 

Following Jon's talk, Steve Bennet of PECDC picked on Jon's issue of scare data within LGD modelling. Steve is based in the US, working for his organisation PECDC which is a cross border initiative to collect LGD and EAD (exposure at default) data. The basic premise seems to be that in dealing with the scarce data problem, we do not have 100 years of data yet, so in the mean time lets pool data across member banks and hence build up a more statistically significant data set - put another way: let's increase the width of the dataset if we can't control the depth. 

PECDC is a consortia of around 50 organisations that pool data relating to credit events. Steve said that capture data fields per default at four "snapshot" times: orgination, 1 year prior to default, at default and at resolution. He said that every bank that had joined the organisation had managed to improve its datasets. Following an audience question, he clarified that PECDC does not predict LGD with any of its own models, but rather provides the pooled data to enable the banks to model LGD better. 

Steve said that LGD turns out to be very different for different sectors of the market, particularly between SMEs and large corporations (levels of LGD for large corporations being more stable globally and less subject to regional variations). But also there is great LGD variation across specialist sectors such as aircraft finance, shipping and project finance. 

Steve ended by saying that PECDC was orginally formed in Europe, and was now attempting to get more US banks involved, with 3 US banks already involved and 7 waiting to join. There was an audience question relating to whether regulators allowed pooled data to be used under Basel IRB - apparently Nordic regulators allow this due to needing more data in a smaller market, European banks use the pooled data to validate their own data in IRB but in the US banks much use their own data at the moment.

Til Schuermann

Following Steve, Til Schuermann added his thoughts on LGD. He said that LGD has a time variation and is not random, being worse in recession when DR is high. His stylized argument to support this was that in recession there are lots of defaults, leading to lots of distressed assets and that following the laws of supply and demand, then assets used in recovery would be subject to lower prices. Til mentioned that there was a large effect in the timing of recovery, with recovery following default between 1 and 10 quarters later. He offered words of warning that not all defaults and not all collateral are created equal, emphasising that debt structures and industry stress matter. 

Summary

The evening closed with a few audience questions and a general summation by the panelists of the main issues of their talks, primarily around models and modelling, the scarcity of data and how to be pragmatic in the application of this kind of credit analysis. 

 

 

09 October 2013

And the winner of the Best Risk Data Management and Analytics Platform is...

...Xenomorph!!! Thanks to all who voted for us in the recent A-Team Data Management Awards, it was great to win the award for Best Risk Data Management and Analytics Platform. Great that our strength in the Data Management for Risk field is being recognised, and big thanks again to clients, partners and staff who make it all possible!

Please also find below some posts for the various panel debates at the event:

 Some photos, slides and videos from the event are now available on the A-Team site.

 

07 October 2013

#DMSLondon - Managed Services and the Utility Model

Andrew Delaney introduced the final panel of the day, involving Steve Cheng of Rimes, Jonathan Clark of Tech Mahindra, Tom Dalglish of UBS and Martijn Groot of Euroclear. Main points:

  • Andrew started by asking the panel for their definitions of managed data services and data utilities
  • Martijn said that a managed data service was usually the lifting out of a data process from in a company to be run by somebody else whereas a data utility had many users.
  • Tom put it another way saying that a managed service was run for you whereas a utility was run for them. Tom suggested that there were some concerns around data utilities for the industry in terms of knowing/being transparent about data vendor affinity and any data monopoly aspects.
  • When asked why past attempts at data utilities had failed, Tom said that it must be frustrating to be right but at wrong time, but in addition to the timing being right just now (costs/regulations being drivers) then the tech stack available is better and the appreciation of data usage importance is clearer.
  • Steve added a great point on the tech stack, in that it now made mass customisation much easier.
  • Jonathan made the point that past attempts at data utilities were built on product platforms used at clients, whereas the latest utilities were built on platforms specifically designed for use by a data utility.
  • Looking at the cost savings of using a data utility, Martijn said that the industry spends around $16-20B on data, and that with his Euroclear data utility they can serve 2000 clients with a staff level that is less than any one client employs directly.
  • Tom said that the savings from collapsing the data silos were primarily from more efficient/reduced usage of people and hardware to perform a specific function, and not data.
  • Steve suggested that some utilities take an incremental data services and not take all data as in the old utility model, again coming back to his earlier point of mass customisation.
  • Tom mentioned it was a bit like cable TV, where you can subscribe to a set of services of your choice but where certain services cost more than others.
  • Martijn said that there were too many vested interests to turn data costs around quickly. He said that data utilities could go a long way however. 
  • Tom concluded by saying that it was about content not feeds, licensing was important as was how to segregate data.

Good panel - additionally one final audience question/discussion was around data utilities providing LEI data, and it was argued that LEI without the hierarchy is just another set of data to map and manage. 

 

Xenomorph: analytics and data management

About Xenomorph

Xenomorph is the leading provider of analytics and data management solutions to the financial markets. Risk, trading, quant research and IT staff use Xenomorph’s TimeScape analytics and data management solution at investment banks, hedge funds and asset management institutions across the world’s main financial centres.

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