2023/09/13 by Robert Stok, Paul Bilokon, Stok, Robert +1
Decision Sciences · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Computational Finance (q-fin.CP) #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Financial Risk and Volatility Modeling #Robotics (cs.RO) #Statistical Finance (q-fin.ST) #Stochastic processes and financial applications #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2311.06256
openalex publication_date 2023/09/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Calculating true volatility is an essential task for option pricing and risk management. However, it is made difficult by market microstructure noise. Particle filtering has been proposed to solve this problem as it favorable statistical properties, but relies on assumptions about underlying market dynamics. Machine learning methods have also been proposed but lack interpretability, and often lag in performance. In this paper we implement the SV-PF-RNN: a hybrid neural network and particle filter architecture. Our SV-PF-RNN is designed specifically with stochastic volatility estimation in mind. We then show that it can improve on the performance of a basic particle filter.