A Simulation and Empirical Study of the Maximum Likelihood Estimator for Stochastic Volatility Jump-Diffusion Models - Bégin, Jean-François; Boudreault, Mathieu (Studies in Nonlinear Dynamics & Econometrics, 2024)
Abstract We investigate the behaviour of the maximum likelihood estimator (MLE) for stochastic volatility jump-diffusion models commonly used in financial risk management. A simulation study shows the practical conditions under which the MLE behaves according to theory. In an extensive empirical study based on nine indices and more than 6000 individual stocks, we nonetheless find that the MLE is unable to replicate key higher moments. We then introduce a moment-targeted MLE – robust to model misspecification – and revisit both simulation and empirical studies. We find it performs better than the MLE, improving the management of financial risk.

Likelihood Evaluation of Jump-Diffusion Models Using Deterministic Nonlinear Filters - Bégin, Jean-François; Boudreault, Mathieu (Journal of Computational and Graphical Statistics, 2021)

Do Jumps Matter in the Long Term? A Tale of Two Horizons - Bégin, Jean-François; Boudreault, Mathieu (North American Actuarial Journal, 2022)

Leveraging prices from credit and equity option markets for portfolio risk management - Bégin, Jean‐François; Boudreault, Mathieu; Thériault, Mathieu (Journal of Futures Markets, 2024)
Abstract This study presents a firm‐specific methodology for extracting implied default intensities and recovery rates jointly from unit recovery claim prices—backed by out‐of‐the‐money put options—and credit default swap premiums, therefore providing time‐varying and market‐consistent views of credit risk at the individual level. We apply the procedure to about 400 firms spanning different sectors of the US economy between 2003 and 2019. The main determinants of default intensities and recovery rates are analyzed with statistical and machine learning methods linking default risk and credit losses to market, sector, and individual variables. Consistent with the literature, we find that individual volatility, leverage, and corporate bond market determinants are key factors explaining the implied default intensities and recovery rates. Then, we apply the framework in the context of credit risk management in applications, like, market‐consistent credit value‐at‐risk calculation and stress testing.