Dr. Rosen, CEO of d1g1t will be presenting his latest joint research work with Prof. David Saunders from the University of Waterloo, at an invited seminar the Annual Meeting of the Canadian Society of Applied and Industrial Mathematics (CAIMS 2018) on June 4 at Ryerson University in Toronto.
The title of the presentation is: Integrating Scenarios with Market and Credit Risk Simulation Analytics.
Economic and regulatory scenarios are typically described in terms of a small number of key economic factors, and do not explicitly describe what occurs to all the relevant market risk and credit risk factors that affect a given portfolio. We need to understand how all of these risk factors behave, conditional on the outcome of the economic factors, and then map these to portfolio losses. The key insight is that the conditional expectation, and more generally the full conditional distribution of all the factors, and of the portfolio P&L, can be estimated directly from a pre-computed simulation using Least Squares Regression. Least Squares Stress testing (LSST) is a simulation-based conditional scenario generation method that offers many advantages over more traditional analytical methods. Simulation techniques are simple, flexible, and provide very transparent results, which are auditable and easy to explain. LSST can be applied to both market and credit risk stress testing with a large number of risk factors, which can follow completely general stochastic processes, with fat-tails, non-parametric and general codependence structures, autocorrelation, etc. LSST further produces explicit risk factor P&L contributions. From a methodology perspective, we also discuss some of the assumptions the LSST approach, statistical tests to check when these assumptions fail, and remedies that can be applied. We illustrate the application of the methodology through a couple of real-life examples for market risk and credit risk.