Quantitative Risk Solutions Lab

Date: 12th October, between 3 - 9:30pm

Venue: 12 Errol St, London EC1Y 8LX, Lecture Theatre with the reception in the Council Chambers

Co-Organised by Dr. Gareth W. Peters and Dr. Codina Cotar (UCL).

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Keynote Speakers

Mr. Daniel GEORGESCU of Bank of England and UCL.

Dr. Emmanouil KARIMALIS of Bank of England.

Dr. Pedro GURROLA-PEREZ of Bank of England.

Prof. Jon DANIELSSON of Systemic Risk Center LSE.

Prof. Stéphane CRÉPEY of Paris d'Evry.

Dr. Evengelos SEKERIS of Oliver Wymann.

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Bank of England:  Dr. Emmanouil Karimalis and Dr. Pedro Gurrola-Perez

Title: Stress testing in central clearing and the importance of jointly capturing multiple yield curve stresses.

Abstract: 

In response to the 2007-2009 financial crisis, the G20 leaders mandated that all standardized OTC contracts should be traded on exchanges or organized electronic trading platforms and cleared through central counterparties (CCPs), thus increasing significantly their systemic importance. One of the most significant objectives of a CCP is its ability to meet its financial obligations when one or more participants default simultaneously. The first line of a CCP defence are collaterals, also known as initial margins, provided by each clearing member. Losses exceeding initial margins are effectively shared (mutualised) across non-defaulting CCP members. CCPs employ a wide range of stress-testing methodologies to determine the size of the “default fund”. In this research project, we propose a modelling methodology that can be used by CCPs and other financial institutions to generate stress test scenarios. We focus on interest rates stress testing using a multi-country framework. In particular, we model jointly the temporal and cross-country dependence structure of several European sovereign yield curves and associate movements in the yields and cross-country spreads with movements in macroeconomic and financial variables as well as market-wide and country-specific measures of liquidity and credit quality. We show that the methodology is flexible and can generate a number of stress test scenarios. Further, we argue that the model can be potentially applied on different markets and can find alternative risk management applications. In the empirical part of the paper, we show that country-specific liquidity and credit quality factors are important when generating stress scenarios. Nevertheless, their importance varies with time and maturity.

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Short Bio:

 

Pedro Gurrola-Perez

 

Pedro worked as a technical specialist in the CCP supervision team at the FSA before moving to the Bank of England, where he currently leads the research team in the Markets Infrastructure Directorate. Pedro holds a PhD in Mathematics from the University of Montpellier (France) and a Master in Financial Mathematics from the Universidad Autonoma de Barcelona. He has extensive academic experience, including positions as Senior Lecturer in Finance at Regents College University (London) and as a Finance Professor at ITAM (Mexico City). His research interests include risk models and interest rate derivatives. He has published in various academic journals, including the Journal of Futures Markets, International Finance and the Journal of Risk. In 2007 he obtained the National Derivatives Research prize, awarded by the Mexican Derivatives Exchange.

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Emmanouil Karimalis

 

Emmanouil currently works as a Risk Specialist at the Financial Market Infrastructure Directorate (FMID), Bank of England. He is responsible, inter alia, for assessing quantitatively the suitability of margin and stress testing methodologies developed by UK central counterparties (CCPs) and pursuing targeted research. His research interests include multivariate modelling with applications in risk management, systemic risk and stress testing. Emmanouil holds a PhD and MRes degree in Finance from Cass Business School and an MSc in Economics and Finance from Warwick Business School (WBS). Prior to his doctoral studies Emmanouil worked at JP Morgan as a market risk associate in commodities markets.

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Bank of England:  Mr. Daniel Georgescu

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Title: Aggregation, Dependency and Stress Testing in Insurance - Opportunities for Academic Collaboration

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Abstract:

With the introduction of Solvency II, there have many changes to the regulation of insurance, impacting on capital calculation and associated stress testing. These present key opportunities for academics to help shape emerging practice. Examples include: (i) completing capital correlation matrices where some of the entries are not known (a closely related problem to the nearest correlation matrix) and (ii) designing stress tests using intuitive tools which respect the stress test designer’s intuition about causal direction, can be calibrated to pre-determined parameters such as correlations between risks, and can be easily communicated to and challenged by non-technical audiences. 

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Short Bio:

Dan Georgescu is an actuary and Senior Technical Specialist in Insurance at the Bank of England, and has recently returned from a secondment at HM Treasury. He is completing an MSc in Statistics and will be starting PhD research related to the topics of his presentation in UCL in the QRSLab.

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London School of Economics:  Prof. Jon Danielsson

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Title: Nature of Risk

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Abstract:

The focus of the presentation is on issues in the measurement of financial risk, the  measurable and what matters, the impact of model risk and what this means for risk management and financial regulations.

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Short Bio:

Jón Daníelsson is director of the ESRC funded Systemic Risk Centre at the London School of Economics. Jón received his PhD in the economics from Duke University, where his dissertation focussed on stochastic volatility. His research interests include systemic risk, financial risk forecasting and financial regulations. Jón has written two books, Financial Risk Forecasting and Global Financial Systems: Stability and Risk and published a number of articles in leading academic journals.

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Université d’Evry:  Prof. Stéphane CRÉPEY

 

Title: Central Clearing Valuation Adjustment

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Abstract: 

This paper develops an XVA analysis of centrally cleared trading, parallel to the one that has been developed in the last years for bilateral transactions. We introduce a dynamic framework that incorporates the sequence of cash-flows involved in the waterfall of resources of the CCP. The total cost of the clearance framework for a member of the clearinghouse, called CCVA for central clearing valuation adjustment, is decomposed into a CVA corresponding to the cost of its losses on the default fund in case of defaults of other member, an MVA corresponding to the cost of funding its margins and a KVA corresponding to the cost of the capital that the member implicitly provides to the CCP through its default fund contribution (for completeness and reference we also compute a DVA term). In the end the structures of the XVA equations for bilateral and cleared portfolios are similar, but the input data to these equations are not the same, reflecting completely different financial network structures. The resulting XVA numbers are therefore very different, but interestingly they become quite comparable after scaling by a suitable netting ratio.

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Short Bio: 

Stéphane Crépey is professor at the mathematics department of University of Evry (France), head of probability and mathematical finance and head of the engineering and finance branch (M2IF) of the Paris-Saclay master program in financial mathematics. His research interests are counterparty and credit risk, enlargement of filtration, backward stochastic differential equations and numerical finance. He is the author of numerous research papers and two books: ``Financial Modeling: A Backward Stochastic Differential Equations Perspective'' (S. Crépey, Springer Finance Textbook Series, 2013) and ``Counterparty Risk and Funding, a Tale of Two Puzzles'' (S. Crépey, T. Bielecki and D. Brigo, Chapman & Hall/CRC Financial Mathematics Series, 2014). 

 

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Oliver Wymann:  Dr. Evangelos Sekeris

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Title: Operational risk modelling in a post AMA world, is stress testing the way forward?

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Abstract:  

The impetus for modelling operational risk was the Advanced Methodology Approach (AMA) requirement in the Basel 2 Accord. The shallow understanding the industry had of operational risk when the Accord was being drafted is reflected in the very open ended nature of the AMA requirement which amounts to little more than a general requirement to collect data and model it. This vague requirement paired with the very specific requirement of estimating capital defined as the 1 in a 1000 loss led to the development of a large number of modelling techniques spanning the full range of complexity from the overly simplistic to the needlessly complex. Over the past few years we started observing a convergence in modelling practices, a direct result of the years of research and practice. The Loss Distribution Approach (LDA) has emerged as the go to modelling tool for operational risk and is now widely used across the industry. Despite its popularity, the LDA suffers from a number of weaknesses, the most important of which is its potential for variable capital numbers over time. While these weaknesses are undeniable, they have been used and blown out of proportion by some in the industry to indict not just the LDA but modelling of operational risk more generally. Paired with the broader anti modelling sentiment in the banking industry this dislike for the LDA has led to the Standardized Measurement Approach (SMA) for operational risk which is going to replace all current capital requirements. The introduction of the SMA is seen by many as the nail in the coffin of operational risk modelling. In this talk I argue that, while ill advised, the SMA is most likely not going to mark the end of modelling. It is even possible that the SMA paired with solid stress testing requirements for operational risk could trigger a new wave of modelling in operational risk that will focus more on the drivers of the risk and on lower quantiles of the loss distribution.

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Short Bio: 

Evan G. Sekeris is a Partner in the Financial Services practice of Oliver Wyman, based in Washington, D.C.. His areas of focus are operational risk, stress testing and model risk management. Evan’s background is in the measurement and quantification of credit risk and operational risk.  His primary focus is currently on supporting institutions in building operational risk modeling for stress testing, developing their risk identification process and developing their model risk management frameworks.

 

Prior to joining Oliver Wyman, Evan was the Head of Risk Consulting for Financial Institutions for Aon in Columbia, Maryland. He was in charge of building Aon’s risk consulting practice for financial institutions and managed multiple teams based in North America and Europe to deliver services to clients worldwide. Previously, Evan was an Assistant Vice President of the Federal Reserve Bank of Richmond, where he created the center of excellence for operational risk which served the System needs for operational risk related matters. The team was in charge of the supervision of all AMA and CCAR banks in the US and developed the Fed’s CCAR model for operational risk.

 

Evan earned a B.A. and M.A. in Economics from the Université Catholique de Louvain in Belgium. He received an additional M.A. as well as his Ph.D. in Economics from the University of California at Los Angeles.

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