大阪大学　数理・データ科学教育研究センター
Center for Mathematical Modeling and Data Science,Osaka University

Efficient simulation of the SABR model

Jaehyuk Choi (Peking University HSBC Business School)

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大阪大学 数理・データ科学セミナー 金融・保険セミナーシリーズ 第146回

Efficient simulation of the SABR model

Jaehyuk Choi (Peking University HSBC Business School)

We propose an efficient and reliable simulation scheme for the stochastic-alpha-beta-rho (SABR) model. The two challenges of the SABR simulation lie in sampling (i) the integrated variance conditional on terminal volatility and (ii) the terminal price conditional on terminal volatility and integrated variance. For the first sampling procedure, we analytically derive the first four moments of the conditional average variance, and sample it from the moment-matched shifted lognormal approximation. For the second sampling procedure, we approximate the conditional terminal price as a constant-elasticity-of-variance (CEV) distribution. Our CEV approximation preserves the martingale condition and precludes arbitrage, which is a key advantage over Islah's approximation used in most SABR simulation schemes in the literature. Then, we adopt the exact sampling method of the CEV distribution based on the shifted-Poisson-mixture Gamma random variable. Our enhanced procedures avoid the tedious Laplace inversion algorithm for sampling integrated variance and non-efficient inverse transform sampling of the forward price in some of the earlier simulation schemes. Numerical results demonstrate our simulation scheme to be highly efficient, accurate, and reliable. (This work is in collaboration with Lilian Hu and Yue Kuen Kwok. The paper is available at https://arxiv.org/abs/2408.01898)

Bio: Jaehyuk Choi is an Associate Professor at Peking University HSBC Business School. Before joining academia, He worked for nine years as a fixed-income quant analyst for Goldman Sachs in New York and Hong Kong. He is a co-founder and an advisor of quants.net, a financial analytics company. His research interests include mathematical finance, machine learning, and numerical methods.