2024/10/15 by Emmanuel Gnabeyeu, Gnabeyeu, Emmanuel, Omar Karkar +3 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · Engineering · #Computational Finance (q-fin.CP) #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Economics and business #FOS: Mathematics #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Probability (math.PR) #Risk Management (q-fin.RM) #Stochastic processes and financial applications #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2410.11789
openalex publication_date 2024/10/15 · openalex created_date 2024/10/20 · openalex updated_date 2026/07/28
The volatility fitting is one of the core problems in the equity derivatives business. Through a set of deterministic rules, the degrees of freedom in the implied volatility surface encoding (parametrization, density, diffusion) are defined. Whilst very effective, this approach widespread in the industry is not natively tailored to learn from shifts in market regimes and discover unsuspected optimal behaviors. In this paper, we change the classical paradigm and apply the latest advances in Deep Reinforcement Learning(DRL) to solve the fitting problem. In particular, we show that variants of Deep Deterministic Policy Gradient (DDPG) and Soft Actor Critic (SAC) can achieve at least as good as standard fitting algorithms. Furthermore, we explain why the reinforcement learning framework is appropriate to handle complex objective functions and is natively adapted for online learning.