Thursday 19th November: Latest XVA Developments
In this talk we try to answer two questions:
- Should KVA be part of derivatives valuation? If so, what are its properties?
- Does a change in capital requirements have an impact on shareholder value and hence lead to a KVA?
In exploring these question we find:
- There are two distinct KVA quantities that can be defined.
- KVA1 is a result of market incompleteness and captures the value of unpriced risk. This should be considered integral to an assets value.
- KVA1 is not sensitive to changes in capital levels but is risk based.
- In many cases of interest, KVA1 has implicitly already been incorporated in valuation and no additional adjustment is required.
- KVA2 is compensation for shareholder losses due to changes in a firms leverage. This represents a “Transfer of Wealth” between shareholders and creditors similar to FVA.
- KVA2 is proportional to marginal changes in capital levels. However, the effective cost of this capital is the firms junior funding rate and not the return-on-equity.
Global head of Counterparty Credit Risk Quantitative Research, J.P. Morgan
Matthias Arnsdorf: Global head of Counterparty Credit Risk Quantitative Research, J.P. Morgan
Since 2012 Matthias has been heading the counterparty credit risk quantitative research team globally.
His main responsibilities include the development & support of J.P. Morgan’s suite of credit exposure models which are used for valuation and risk management as well as credit capital.
Prior to his work in credit risk, Matthias headed the market risk capital modelling effort in EMEA for two years. Matthias started his career in finance in 2002 as a credit derivatives quantitative researcher at UBS and J.P.Morgan.
Matthias holds a PhD in Quantum Gravity from Imperial College London and has spent two years as a post-doctoral researcher at the Niels Bohr Institute in Copenhagen prior to his move to quantitative finance.
We will present a demo showing how deep neural networks can be used to speed up calibration, pricing and risk-sensitivity computations, to the point that the front office has access to real-time P/L, P/L explain and risk sensitivities for portfolios of exotic derivatives.
We’ll also discuss other use cases where fast DNN models are likely to have big impacts including XVA, FRTB and EoD operational risk reduction.
Looking ahead over the next 12 months, we discuss how AI and other technology development such as cloud computing could work together to drive RoC in capital markets, and the steps trading desks, quants and IT can start taking to effectively leverage these and many of the other recent developments in AI/ML.
Founder & CEO, Riskfuel
Ryan Ferguson: Founder & CEO, Riskfuel
Ryan is Founder and CEO at Riskfuel, a capital markets focused startup that is developing ultra-fast AI-based valuation technologies.Previously, Ryan was Managing Director and Head of Securitization, Credit Derivatives and XVA at Scotiabank. Prior roles have included credit correlation trading and managing the equity derivatives trading desk. Ryan began his career with positions in risk management and financial engineering. Ryan has a PhD in Physics from Imperial College, and a BASc and MASc in Electrical Engineering from the University of Waterloo.
Manager, Quantitative Risk, U.S. Federal Reserve Board
Michael Pykhtin: Manager, Quantitative Risk, U.S. Federal Reserve Board
Michael Pykhtin is a manager in the Quantitative Risk section at the U.S. Federal Reserve Board. Prior to joining the Board in 2009 as a senior economist, he had a successful nine-year career as a quantitative researcher at Bank of America and KeyCorp. Michael has edited “Counterparty Risk Management” (Risk Books, 2014) and “Counterparty Credit Risk Modelling” (Risk Books, 2005). He is also a contributing author to several recent edited collections. Michael has published extensively in the leading industry journals; he has been an Associate Editor of the Journal of Credit Risk since 2007. Michael is a two-time recipient of Risk Magazine’s Quant of the Year award (for 2014 and 2018). Michael holds a Ph.D. degree in Physics from the University of Pennsylvania and an M.S. degree in Physics and Applied Mathematics from Moscow Institute of Physics and Technology.
The two largest components of Capital Valuation Adjustment (KVA) are the costs of Counterparty Credit Risk (CCR) and CVA capital. For a bank using the most advanced capital models – Internal Models Method for CCR and the incoming SA-CVA capital –an accurate KVA involves forward simulating expected exposures (EE) over the lifetime of the portfolio – potentially a Monte Carlo in a Monte Carlo. We present a practical regression-based solution.
- Simulating EE: from regulatory stressed real-world measure to market implied measure
- A comparative study of regression vs brute force nested Monte Carlo
- SA-CVA: extending from simulating forward EE to simulating forward CVA sensitivities
Quantitative Strategy, Adaptiv, FIS
Justin Chan: Quantitative Strategy, Adaptiv, FIS
Justin Chan has over 11 years of experience in financial risk management and capital markets. Mr. Chan has a deep focus on quantitative modelling in areas such as xVA, credit exposure, and collateral simulations. He is currently responsible for the Risk Quantitative Strategy and Innovation program at FIS. Prior to FIS, Mr. Chan worked at Manulife Financial as a manager in corporate risk management.
Mr. Chan studied engineering science (BASc), and theoretical physics (MSc) at University of Toronto, where he also holds a Master of Mathematical Finance (MMF) degree.
Presenter to be confirmed