Data Management for Market Risk - PRMIA webinar presentation
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Presentation on data management for risk for www.prmia.org webinar. Sets out an overview of key business issues faced and how they can be overcome.
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Data Management for Market Risk - PRMIA webinar presentation
- 1. This material is the intellectual property of the presenter and shall not be reproduced or used without the express written permission . Data Management for Market Risk Wednesday, April 23, 2014 at 12 pm EST Brian Sentance, CEO of Xenomorph • Audio: Use your computer speakers (VoIP) or call in using your telephone. • To access this webinar audio via the internet, select “Mic & Speakers” under your Audio pane. • For technical assistance contact the Citrix webinar utility customer number: 1-888-259-8414 • Questions can be submitted via the question pane on the right or on twitter @PRMIA, #riskdata • To request CPE credit, e-mail webinars@prmia.org immediately following this live webinar.
- 2. So what’s the problem?
- 10. So are the risks we see, the risks we face?
- 11. How best would you describe data quality used in your risk management processes? a) Poor quality across much of the data used b) Inconsistent quality, some high with other areas in need of improvement c) Generally good quality across all asset classes and data types d) Verifiably high quality e) Don't know
- 12. So what’s the solution?
- 13. D A T A R I S K
- 14. Consistent, transparent data for all systems and people
- 21. Do you have a formal data management process built around data used in risk management? a) Not at all b) Supported by risk management systems c) Recognized only as part of wider data management initiatives d) Explicitly implemented as a necessary function within risk management e) Don't know
- 22. When somebody says… …just what do they mean?
- 23. Does IT understand the business, and vice versa?
- 24. The only constant is change?
- 25. What about complex datasets?...
- 26. …and the value in derived data?
- 27. …what models did you use?...
- 28. Are derived data sets an integrated part of your data management processes? a) No not all, these are validated manually then supplement the standard data sets b) Integrated for some asset classes but not all c) All integrated into our processes along with simpler data like terms and conditions d) Don't know
- 29. …and be sure to be inclusive of front, middle & back office…
- 30. …or they will do their own thing.
- 31. So what’s in your workflow?
- 32. It’s about time.
- 33. Don’t design for one regulation only – you have to be flexible
- 34. How much do spreadsheets feature as part of your risk and data management processes? a) No not all, using standard integration tools b) Only used for a few datasets c) Used widely to integrate different datasets from different systems and departments d) Don't know
- 38. The good thing about standards is…
- 39. The utility of data?
- 44. Data content will follow music…
- 45. …and apps will follow content.
- 46. D A T A R I S K Summary – Data Management for Market Risk
- 47. Questions for the Presenter? Send them via the Question Pane in the webinar utility panel on the right hand side of your screen 47
- 48. Thank you for attending this PRMIA Webinar! Please go to PRMIA’s website at www.prmia.org. Click on Webinars under the Training tab to find more upcoming thought leadership webinars. Also, click on the Membership tab for information on joining PRMIA as a sustaining member. 48
- 49. Brian Sentance, CEO, Xenomorph Brian is CEO of Xenomorph and a member of the steering committee for the New York Chapter of PRMIA. He is keenly involved in gathering client needs within the field of analytics and data management in financial markets, with a particular focus on data management for risk. Prior to joining Xenomorph in 1995, Brian headed the pricing models development team in the equity derivatives group at JP Morgan, London. This role involved bridging the requirements of trading, quant and software development staff to deliver new financial products to market. In 1993 Brian completed a PhD in interest rate risk optimization at the Centre for Quantitative Finance, Imperial College, University of London. During this post-graduate study he was sponsored by British Telecom Group Treasury. Prior to this, Brian was sponsored by GEC Marconi during his M.Eng in Electronic Engineering. Brian is married with two children. His interests include cycling, table tennis, tennis, listening to music and reading.
- 50. Recent blog posts on PRMIA events: • Regulatory, Compliance, and Risk Data Technology Challenges – PRMIA • Innovations in Liquidity Risk Management – PRMIA • Risk Management in Securities Financing and Money Market Funds – PRMIA • Risk Data Aggregation and Risk Reporting from PRMIA • Model Risk Management from PRMIA • Credit Risk: Default and Loss Given Default from PRMIA
- 51. Xenomorph Links • Web: www.xenomorph.com • Blog: www.xenomorph.com/blog/ • Case studies: www.xenomorph.com/casestudies/ • White papers: www.xenomorph.com/downloads/whitepapers/
Notes de l'éditeur
- So what’s the problem? How is risk management affected by data, what are the issues and what can be done about them?
- Data used in risk management and across the financial markets industry is a little bit like the water we drink every day: none of us can function without it we are not always sure where it comes from some of us have no idea how much it really costs we don't mind if we waste it we don't like sharing it and usually, we only find if it was bad for us after we have drunk it
- So over the top of the usual responsibilities of risk management…
- …we are currently dealing with a deluge of regulatory responses to the 2007/2008 crisis, requiring more reporting in greater granularity across all of our activities: Banking Basel II/II US Dodd-Frank/Volcker Rule Investment Management FAS157 IFRS13 UCITS III/IV AIFMD EMIR MiFID Insurance Solvency II
- …and this often needs to be done against a background of siloed systems, data, calculations and processes that in many cases are very hard to unravel. So hard was it to unravel in the 2007/2008 crisis that it motivated the Basel Committee to issue guidance for banks on risk data aggregation and reporting in BCBS 239: “One of the most significant lessons learned from the global financial crisis that began in 2007 was that banks’ information technology (IT) and data architectures were inadequate to support the broad management of financial risks. Many banks lacked the ability to aggregate risk exposures and identify concentrations quickly and accurately at the bank group level, across business lines and between legal entities. Some banks were unable to manage their risks properly because of weak risk data aggregation capabilities and risk reporting practices. This had severe consequences to the banks themselves and to the stability of the financial system as a whole.”
- At best it can mean that risk and regulatory reports can take a lot of time to prepare and pull together…
- …and given the amount of data being dealt with, it can be hard to identify where the underlying data problems are when you are forced to work backwards from a headline report that is obviously “wrong”…
- …in fact sometimes it can be too hard.
- So are the risks we see, the risks we face? Human psychology seems to dictate that if we see a shiny report full of numbers we think it must be correct (it is a number after all, that is so “definite” that it must be true!), but what about the quality, consistency and transparency of the data, analytics and models that lie underneath the report?
- So what’s the solution?
- Data has to be the foundation that risk management is built upon. Any model is only as good as the quality of the input data feeding it, and so high quality “data management for risk” needs to be implemented strategically, not as an afterthought once the risk model or scenario engine has already been built.
- Building risk management on a solid foundation means that systems, processes and people can all make use of the same “golden copy” datasets, reducing data costs and increasing data consistency, quality and transparency.
- So what kinds of data are we talking about? The green data flows show the kinds of data that many market risk systems require, but in overview these datasets include…
- …reference data such as instrument IDs/symbols and instrument terms and conditions…
- …counterparty/entity data that clearly identifies who you are transacting with…
- …real-time, intraday and historic market data…
- …and transaction and positions data.
- So we know the main categories of data involved and what the ultimate goal is in terms of getting people to share the same “golden copy” datasets, but how is the “golden copy” created? The diagram on this slide shows one outline process for how a data management system must: Connect to a variety of different data sources/data feeds Cleanse and validate data automatically and enable workflow for manual data exception management Create instruments and related objects like indices, curves and surfaces Consolidate and normalize all of this data to make it easy to consume, regardless of the original data source or data format Analyze and visualize the data in any way that the user desires, to understand its quality and potentially to use the data with models to generate derived data sets Distribute the data through a variety of applications, interfaces and formats to downstream systems, processes and people And all of this process must be easy to configure and easy to control and audit.
- Having outlined one form of “Data Management” it is probably as well to remember that when different people and solution vendors talk about “Data Management” then it is possible that they are talking about very different solutions addressing very different data problems. For example a data management vendor might offer: An Extract-Transform-Load (ETL) solution to map data from input sources to downstream systems that does not implement any data model A data warehouse solution with full relational data model to support reference data and related workflows A real-time tick/time-series database solution A counterparty data management systems A messaging system for the distribution of data A real-time data metering solution A Know Your Client (KYC) system A document management system There are many more examples. It is somewhat ironic that for an industry segment that is dedicated to breaking down “Data Silos” many of the systems are “siloed” themselves around particular types of data and even asset classes. Whilst it possible to filter out many solutions as inappropriate to a particular need through research etc., ultimately there is no substitute for defining a number of business case examples and asking the vendor to show how they would address each problem.
- On a related matter of understanding, poor communication between business and IT staff remains a problem across the industry. One surprisingly simple example from one bank involved a tick data management system that did not adjust historic data for corporate events. This proved to be a major surprise to the quants and analysts who commissioned the system and wanted to use it for historic data analysis. I guess the rule here for all parties is do not make assumptions, since the detail really does matter.
- I am now going to talk about a number of issues that can drive business users away from using core systems, and core data management systems in particular. The first is the problem of changing data requirements and the need to add new data attributes and even asset classes. If a change to the data model is hard to implement or requires vendor support to implement, then it will invariably fall behind what the business needs and they will seek out alternative solutions, more of which later.
- The second of these issues is associated with the more complex “model” data. This kind of data is usually either non-standard or array-based in structure, and does not easily fit into a standard relational data model. As such many data management systems simply ignore the problem, forcing the risk manager to find some other way of managing the data outside of and in parallel with the data management systems.
- Related to “model” data such as curves and surfaces, another type of data that is often “forgotten” by data management is derived data. This is the kind of data that is generated during or at the end of some calculated process such as an instrument valuation or risk management simulation. One example here was from a financial institution that had been submitting its risk reports based on simulation paths that had never been “eye-balled” thoroughly for consistency with the model being implemented. A more typical example would be the generation of derivative and fixed income valuation numbers from desktop-bound spreadsheets, with all the operational risk that that implies.
- And not really “data”, but the valuation and risk models you use also need to be managed and controlled if they were part of the process that generated the reports the regulators or auditors saw. This kind of “Analytics Management” is a new requirement we are seeing coming through from clients dealing with derivative and fixed income products.
- The final example that can drive people away from using core data management systems is to simply not involve them from the start. Many data management vendors have their origins in back office trade data management from the T+1 days of 2000/2001, and whilst these vendors are now well aware of regulation and risk as a driver of data management, their origins do not make them ideally suited to or useable by the front office. This is a problem since the front office probably know more about data than most departments, and whilst it can be tricky there are real benefits in getting all of front, middle and back office aligned in the data they use, and the process for ensuring it is of high quality.
- And what do people do when they can’t get what they need from core systems? They use the most popular data management system in the world, Microsoft Excel. You can end up in the situation where the core systems and data are happily being ignored whilst traders, risk managers and back office staff make use of “temporary” spreadsheet solutions that have a tendency to become very “permanent”. This is a personal hobby-horse of mine, in that I think spreadsheets are great data analysis and reporting tools, but where they go particularly wrong is when they are also used as desktop-bound databases of data. It is an issue that has been around a very long time and whilst Microsoft and others are currently doing some great things in addressing transparency and consistency of spreadsheet data, the problem still seems endemic to the industry.
- On the assumption that you have persuaded your users to lift their heads away from their spreadsheets, then a topic of current popularity is workflow and the lineage of data. This often involves the management of data validation where a data point can be validated in the context of: Itself (i.e. is its current value sensible given what it is?) Its history (i.e. is its value sensible given what it was?) Its membership of other objects (i.e. it might belong to one or more curves or indices) In this regard the workflow you implement has to be sophisticated enough to know that if one validation context fails, other validation contexts may well have to be rolled back to ensure that data that is only partially validated is not used.
- Following on from workflow and the lifetime of data, then this slide covers a number of aspects of the temporality of data. First is that regulators increasingly insist upon a full audit trail of who changed data and why, so tracking the workflow followed and any changes made (and approved) to both the system and the data within it. Secondly is the general point that all data is a time series, whether it is a market price changing every millisecond or whether it is the name of a company that changes once every few years due to some corporate event – you need systems that can deal with this. Another point is around how long this data must now be maintained, and whether data archiving can be turned into an asset for an institution rather than simply left as a cost. Finally, the move towards near real-time pre-trade analysis (CVA etc) is changing business models and in the process knocking down data silos as all counterparty credit exposures need to be known in advance of a trade.
- Finally for this section, whilst implementing a system to meet a single requirement can be attractive (doing only “one” thing can be cost effective in the short-tem), it would be best to invest for the future both in terms of your data management capability and the analysis and reporting tools you choose. Given the amount of regulation existing now, the lack of specifics provided by many regulators and the future regulation yet to come, then implement tools that are flexible enough to cope with future needs.
- So what’s next for data management?
- Lets start with the obvious - regulation is not going away any time soon, nor does its content get any simpler. To illustrate this I found the following list online: Pythagoras 24 words Lords Prayer 66 words Archidmedies Priciple 66 words 10 commandments 179 words Gettysburg Address 286 words Declaration of independence 1300 words US Govt sale of cabbage 26,991 words And whilst much of the regulation is a cost (Dodd Frank will have spawned some 30,000 pages of rules apparently), where possible implementing against a regulatory requirement should ideally be seen as an opportunity to do something better than before. And in a similar way, regulation is also driving new business models, and data and its management will have a key part to play in terms of who wins and who loses.
- The importance of data in financial markets at last seems to be recognized at board level within many financial institutions. Whilst there is still much debate about what should be the remit of the “Chief Data Officer”, giving general ownership of “The Data Problem” to the CDO is a big strategic step forward for many financial institutions (…ok maybe not a big step, but “a journey of a thousand miles begins with a single step”), setting data governance policy and setting the direction for how data is to be managed and best used throughout the organization.
- “The good thing about standards is that there are so many to choose from.” With a few exceptions (FPML maybe?) the industry has not been very good at putting together data standards. It is sometimes bizarre to explain to others outside the industry that there is no industry standard financial instrument identifier, although both Bloomberg and Thomson Reuters would probably argue to the contrary. On the subject of Bloomberg and instrument identifiers, then their Bloomberg Open Symbology initiative is worth a mention, which seemingly is being adopted by other data vendors and should prompt some response from Thomson Reuters. It seems that data standards need to be regulated to make them really “happen”, and given the regulators’ focus on counterparty risk then we seem to be going through the first trial of “data regulation” with the Legal Entity Identifier (LEI). Whether this proves to be useful or becomes another data mapping overhead remains to be seen, but the aim is laudable and the framework for how “data regulation” should be implemented is being worked through. There are other industry initiatives such as the EDM Council with its FIBO standard that are progressing, but adoption is the key metric and making data standards “mandatory” is the only way to implement fundamental change in my view.
- There is a lot of discussion about managed services and the concept of data utilities in the industry at the moment. Both are based around the concept of being able to outsource data management to a third party organization on a one to one (managed service) or one to many (utility) basis, and in the process allow the client to benefit from reduced costs. Putting aside the technological debate over whether the timing is right (it has been tried before), the skill is in whether either type of service provider can i) leverage the cost benefits of the commonality in its activities across all clients, whilst offering ii) services that appear bespoke to an individual client. I would also ask whether data utilities aspire to be future data aggregators, i.e. is their desire to become the next Bloomberg or Thomson Reuters? – not a bad aim in itself given the dominance of the two big data aggregators (over 60% of the data spend in the industry combined I think) but an aim I am not yet clear on.
- As discussed previously, involving as many users and departments as possible in data management can only result in better data quality and faster resolution of issues. With self-service technologies such as wikis and enterprise social networks (think private Facebook for business) now going mainstream, the tools to enable faster more productive co-operation are only a user login away.
- It is difficult to avoid the topic of “Big Data” wherever you look. What effect will it have on data management for risk? If you stick within the existing confines of market risk then these new database technologies will certainly allow larger datasets to be managed at lower cost and in shorter timeframes than many mainstream database technologies. Outside of the data being managed now within a typical risk management function, “sentiment analysis” has become the poster child of big data applied to trading in financial markets but whether this and other datasets can be applied to better understand risk (maybe through understanding human behavior better?) remains to be seen. In risk management there have been a number of problems with “models” in recent years, but whether the results of big data analysis are enough for risk managers without understanding the causation “model” is another topic for debate.
- Big data can’t really be mentioned without mentioning the “place” (if that is not completely the wrong word in this context?) where most big data analysis takes place: the cloud. Cloud technology is gaining some momentum in risk management, particularly in situations like “burst to the cloud” when heavy compute jobs (think simulations for example) need to get done quicker than the internal infrastructure can (or has been designed to) cope with. Security and SLA concerns over cloud usage remain, but the acceptance of cloud and virtual private cloud technology is growing, allowing financial institutions to share data internally with much greater ease and at reduced (and readily definable) cost. It might take some of us a few years yet to get to the cloud, but that is where we are heading.
- So if we could use cloud technology for data management, what else could we do with it? Well taking a leaf out of the copy book of the managed service and data utility providers, maybe we could drastically reduce the costs of data cleansing and validation for the industry if we could share out the workload? With crowdsourcing then instead of a team of 25 risk managers spending half their time validating data at one institution, maybe they could spend much more time on risk management if they could see (on an anonymous basis) the distribution of corrections implemented by other risk managers at other organizations? Ok, it would need some thought and some appropriate incentives, but for non-proprietary datasets then economically it makes sense (which is obviously not the same thing as the industry actually doing it!).
- So with the advent of easier sharing and publishing of data with technologies such as cloud and big data, I think data content will eventually go the way of music consumption where you can pay for as much or as little of the data as you and your colleagues need. This may mean some uncomfortable times ahead for those data vendors that stick completely to traditional commercial models, although innovative first movers in this area may well find that there is increased revenue to be had from a potentially much larger client base.
- And with the advent of “app store” offerings for both Bloomberg and Thomson Reuters, you can see once again how financial markets technology is being driven by models from other industries. Put the content somewhere accessible, provide the interfaces necessary and let people play with it is a good recipe for faster and increased innovation in risk and financial markets technology.
- So to conclude, I couldn’t think of a better summary than this earlier slide, so here it is again - great risk management has to be built on a foundation of great data management.