Efficient SIMM-MVA Calculations for Callable Exotics
Computing Standardized Initial Margin Model Margin Valuation Adjustment (SIMM-MVA) requires the simulation of future sensitivities, but these are expensive to compute for callable products.
This paper introduces a method which avoids nested calls to the pricing function, similar to the use of least-squares Monte Carlo (LSMC) for producing future exposures. The method algorithmically differentiates continuation values and underlying swap values with respect to model parameters. These are transformed into sensitivities over market quantities via Jacobians. An illustrative numerical example is provided for a Bermudan swaption demonstrating a massive acceleration w.r.t. brute-force calculations.
Complete the form to download this research paper, “Efficient SIMM-MVA Calculations for Callable Exotics”
Authors: Dr. Alexandre Antonov, Dr. Serguei Issakov, Dr. Andrew McClelland
Computing Standardized Initial Margin Model Margin Valuation Adjustment (SIMM-MVA) requires the simulation of future sensitivities, but these are expensive to compute for callable products.
This paper introduces a method which avoids nested calls to the pricing function, similar to the use of least-squares Monte Carlo (LSMC) for producing future exposures. The method algorithmically differentiates continuation values and underlying swap values with respect to model parameters. These are transformed into sensitivities over market quantities via Jacobians. An illustrative numerical example is provided for a Bermudan swaption demonstrating a massive acceleration w.r.t. brute-force calculations.
Complete the form to download this research paper, “Efficient SIMM-MVA Calculations for Callable Exotics”
Authors: Dr. Alexandre Antonov, Dr. Serguei Issakov, Dr. Andrew McClelland
Authors
Serguei Issakov, Ph.D.
Dr. Issakov, as Chief Quantitative Officer and Senior Vice President of Global Quantitative Research and Development, oversees the company’s quantitative research globally, including the research of pricing models at Numerix. Since joining Numerix in 1999, his earlier roles at Numerix included Vice President of Financial Applications, Head of Engine Development (the forerunner to Numerix 7) and Head of Risk Analytics.
Prior to joining Numerix, Dr. Issakov held research positions in theoretical physics at the Nordic Institute for Theoretical Physics in Copenhagen, the University of Paris (Laboratory of Theoretical Physics and Statistical Models), the University of Oslo and the Center for Advanced Study in Oslo. Before that, he led research on models of brain rhythms at the Medical Radiological Center in Obninsk Russia.
Dr. Issakov has published over 40 papers in mathematics and theoretical physics. He is a co-author of the Issakov-Ouvry-Wu equations in fundamental quantum statistical mechanics. He has received numerous fellowships and research grants, including a NATO Visiting Professorship and grants from the Russian Foundation for Basic Research. He holds PhD in Theoretical and Mathematical Physics from Moscow Institute of Physics and Technology, from the Theory Group led by Physics Nobel Laureate Vitaly Ginzburg.
Dr. Alexandre Antonov
Dr. Antonov received his PhD degree from the Landau Institute for Theoretical Physics in 1997 and joined Numerix in 1998, where he currently works as a Senior Vice President of Quantitative Research. His activity is concentrated on modeling and numerical methods for interest rates, cross currency, hybrid, credit and CVA. Dr. Antonov is a published author for multiple publications in mathematical finance, including RISK magazine and a frequent speaker at financial conferences.
Andrew McClelland, Ph.D.
Andrew McClelland’s quantitative research at Numerix focuses on XVA pricing and hedging, generating counterparty credit risk metrics for structured products, and estimating risk model parameters via time-series estimation. He earned his PhD in finance at the Queensland University of Technology for a thesis on financial econometrics. He considered markets exhibiting crash feedback, option pricing for such markets, and parameter estimation for such markets using particle filtering methods. Dr. McClelland’s work has been published in the Journal of Banking and Finance, the Journal of Econometrics, and the Journal of Business and Economic Statistics.
