In this paper we represent a financial system using a weighted directed graph model. We simulate and analyze the impact of financial regulations regarding the collateralization of derivative trades on systemic risk, employing a novel open source risk engine. The analysis finds that introducing collateralization does reduce the costs of resolving a financial system in crisis. It does not, however, change the distribution of risk in the system. The implications of the analysis highlight the importance of scenario based testing using hands on metrics to quantify the notion of system risk.
The introduction of mandatory margining for non-cleared portfolios has major implications for the pricing and risk measurement of OTC derivatives. In particular, a model for estimating future initial margin requirements is necessary to enable the calculation of pricing adjustments (MVA), net counterparty credit exposures and credit capital (RWA). Existing literature on the topic suggests a model which makes use of regression techniques, but little detail is available on the predictive quality of these models within a Monte Carlo simulation framework. We review these regression-based initial margin models in detail and compare their output against the actual margin requirements measured by the ISDA SIMM methodology. We observe that the models generally perform well for single trades but show some degradation for single option products and larger diversified portfolios. We investigate potential extensions and improvements to the model, along with examining some additional “conservatism” features that may have application in the context of credit exposure measurement. The Initial Margin modelling approaches discussed here are similarly applicable to centrally cleared or exchange-traded portfolios.
International Swaps and Derivatives Association highlights Open-Source as future of technology infrastructure.
By Donal Gallagher, Roland Lichters, Sharyn O’Halloran, and Roland Stamm
The non-cleared over-the-counter (OTC) derivative market is estimated at $493 trillion notional . One of the central triggers of the 2008 Financial Crisis was financial institutions’ excessive exposure to counterparty risk. These exposures peaked at over $4.5 trillion in 2008 . The response of the global regulatory community to the financial crisis has been to introduce regulations and standards aimed at reducing the amount of counterparty credit risk in the financial system. These initiatives gave rise, for example, to the introduction of mandatory clearing for certain common classes of derivatives (cleared derivatives) and more recently the introduction of similar standards for non-cleared derivatives . The primary means promoted to mitigate risk are mandatory variation margin (collateral against today’s value) and mandatory initial margin (collateral against the change in valuation in the event of default). The total amount of initial margin introduced as a result of these changes is estimated at $315 billion for US banks alone . The regulatory expectation is that most derivatives classes will ultimately be subjected to mandatory clearing; however, the current volume and the slow rate of convergence toward mandatory clearing suggest that large volumes of derivative contracts will continue to be subject to the non-cleared OTC regime for the foreseeable future.
Columbia University School of Professional Studies presents
A Panel Discussion with Professor Sharyn O’Halloran, Thomas Deely, and Guests
A key issue for regulators and the financial service industry is mitigating systemic large-scale counterparty risk. Currently, individual financial institutions and regulators conduct systemic risk exposure analysis using proprietary models and data protocols absent any agreed upon baseline, best practices or public scrutiny. Without industry standards, shared benchmarks, or means to validate results, the impact of alternative policy interventions on the overall risk in the financial system remains uncertain.
This initiative showcases new open source analytical tools that develop highly granular trade and cross-asset class risk simulation and aggregate at the counterparty level. Bringing large-scale open source risk models to the public domain will enable a standard-based approach that facilitates research and greater understanding of the impact that policy levers have on the financial system.
Questions? Please contact: email@example.com
Quaternion Risk Management
tullett prebon information
Columbia School of Professional Studies
Columbia Business School
Columbia University Data Science Institute
Columbia Law school
Columbia School of International and Public Affairs
US Banking Editor
Director of the MS Program in Financial Engineering
Professor of Professional Practice
Industrial Engineering and Operations Research, Columbia University
Paul Glasserman, PhD
Jack R. Anderson Professor of Business
Decision, Risk, and Operations Research Director
Program for Financial Studies
Columbia Business School
CEO of Broker-Dealer Services & Head of Banks
Broker-Dealer and Investment Advisors Market and Alternative Asset Manager Segments
Bank New York Mellon
Managing Director of Compliance Analytics
Sharyn O’Halloran, PhD
George Blumenthal Professor of International and Public Affairs
Chief Academic Officer
Columbia University School of Professional Studies
- Andreas Boldin, Credit Suisse AG
- Roland Lichters, Quaternion Risk Management
- Andre Suess, Credit Suisse AG
- Markus Trahe, Credit Suisse AG
November 16, 2016
The tenor basis phenomenon became significant with the 2007 financial crisis and has altered the traditional way of one-curve pricing and risk management to a multi-curve phenomenon. The stochastic nature of basis spreads between curves particularly poses a challenge for forward looking applications like XVA or real world measure exposure analytics. This paper presents a Two- factor Gaussian approach for modelling multiple fixing curves and basis spreads in the risk neutral and spot measure, shows the impact on basis swap exposure, investigates the correlation structure and discusses the pros and cons of interpreting as a spread or multi curve model respectively.
Quaternion Risk Management announces the launch of opensourcerisk.org – the first end-to-end open source risk application. opensourcerisk.org will provide complex risk analytics for financial institutions through a series of releases.
Continue to follow our journey as, today, we launch our contribution to the development of next generation global risk standards. We welcome your contribution to our opensourcerisk.org framework as its evolves.
Quaternion recently sponsored a September 14th Risk.net webinar discussion on the evolution of XVAs. Participants included Scott Sobolewski (Principal Consultant at Quaternion), Yann Coatanlem (MD, Head of Multi-Asset Quantitative Analysis at Citigroup,
Massimo Morini (Head of Interest Rate and Credit Models, Banca IMI), and Peter Zeitsch (Solution Architect at Calypso Technology).
The list of valuation adjustments banks must apply when pricing a derivative has grown exponentially and given rise to an alphabet soup of new valuation adjustments. The term XVAs has been coined to describe the entire family, namely funding valuation adjustment (FVA), capital valuation adjustment (KVA), and now, margin valuation adjustment (MVA). The sheer volume of these adjustments coupled with the impact on profitability has led banks to compute the capital required to support them through the life of the trade. This has considerably increased the computational complexity as well as placed demands for real time calculation of all of this information. Needless to say, dealers attempting to manage their XVAs are finding the process extremely challenging and a few have joined-up in an effort to curb their XVAs, under the banner of optimisation and trade compression.
Discussion topics included:
- What is MVA and how does it interact with the other XVAs?
- How complex is MVA, relative to other adjustments?
- In practical terms, where is the industry on MVA at this point?
- Do you expect MVA to follow the same evolutionary path that we’ve seen for other XVAs?
- Interaction between MVA, KVA, CVA, DVA, FVA
- Rank order of the different XVAs, once IM regime has been fully implemented
- What are the key regulations that will determine the correct MVA/XVA treatment for a trade?
- What are the practical / implementation challenges?
- Given all of this, what timeline is likely for MVA to start showing up in pricing?
- How much pricing dispersion is it likely to produce?
- Can MVA already be seen for cleared trades with CCPs?
- Accounting treatment for MVA (IFRS 9, 13)
- What does the blending / blurring of XVAs mean for the way they are managed? What is the right organisational structure?
Listen to the webinar here.
Scott Sobolewski, a Principal Consultant in our Boston office, recently published an article in Treasury & Risk Magazine titled “How Do Dealer Banks Price Derivative Products?”. The article helps corporate treasurers, asset managers, and other end-users of over-the-counter (OTC) derivatives understand the various components of bank regulatory and capital charges currently built into dealer pricing. Most users have grown comfortable with concepts like CVA and FVA, though newer valuation adjustments for initial margin (MVA) and regulatory capital (KVA), as well as Basel’s new initial margin rule taking effect on September 1, 2016, make it more important than ever to keep pace with new regulation. The market environment necessitates that risk managers at large financial institutions and end-user treasury functions understand how bank pricing has evolved in the wake of Dodd Frank and Basel III, not only for regulatory compliance exercises like reporting or stress testing, but for proactive risk management. By facilitating increased understanding on both sides of a derivatives trade, Quaternion hopes to increase liquidity within the shrinking uncleared OTC derivative market and reduce overall systemic risk across the financial system.
Quaternion sponsored a QuantLib User Meeting at the Chartered Accountants’ Hall, One Moorgate Place, London EC2R 6EA on Tuesday 12th July 2016.
“The QuantLib project is aimed at providing a comprehensive software framework for quantitative finance. QuantLib is a free/open-source library for modeling, trading, and risk management in real-life. […] Appreciated by quantitative analysts and developers, it is intended for academics and practitioners alike, eventually promoting a stronger interaction between them.” (http://quantlib.org)
With this meeting we want to provide a communication platform for both QuantLib users in the industry and QuantLib’s developers, contributors and creators, to help propagating QuantLib’s usage. This meeting continues a tradition of annual user forums and brings this event back to London where the first event of this type was held on January 18th, 2011.
Final Agenda / Speakers
|09:00-09:15||Welcome||Roland Lichters (Quaternion)|
|09:15-10:15||The abcd of Interest Rate Basis Spreads||Luigi Ballabio (StatPro) and Ferdinando Ametrano (Banca IMI)|
|10:15-11:00||Open Risk Engine||Peter Caspers, Niall O’Sullivan and Roland Lichters (Quaternion)|
|11:30-12:15||Reposit 1.8 and the Future of Spreadsheet Addins||Eric Ehlers (Reposit)|
|12:15-12:45||A simple application of the QuantLib observer pattern – Multi-Curve-Sensitivities||Michael von den Driesch (IKB)|
|13:30-14:15||QuantLibAdjoint News||Alexander Sokol (CompatibL)|
|14:15-15:00||A sound modelling and backtesting framework for forecasting initial margin requirements||Daniel Aziz (Credit Suisse)|
|15:30-16:15||Calibration using Neural Networks||Andres Hernandez (IBM)|
|16:15-16:45||Multi-Curve Convexity||Sebastian Schlenkrich (d-fine)|
|16:45-17:00||Wrap up||Roland Lichters (Quaternion)|
The conference was held in the Auditorium and Atrium of the prestigious Chartered Accountants’ Hall at the Institute of Chartered Accountants, One Moorgate Place, London EC2R 6EA, details of which can be found here.
The location is easily accessible from Moorgate and Bank tube stations as can be seen here.
The presentations can be downloaded shortly from the QuantLib web site here.