From Deep Filtering to Deep Econometrics
This addresses volatility estimation for financial applications, but appears incremental as it builds on existing particle filter and machine learning methods.
The paper tackled the problem of estimating true volatility for option pricing and risk management, which is complicated by market microstructure noise, and showed that their hybrid neural network and particle filter architecture (SV-PF-RNN) improves performance over a basic particle filter.
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.