深層フィルタリングから深層計量経済学へ
From Deep Filtering to Deep Econometrics
確率的ボラティリティ推定のために、ニューラルネットワークと粒子フィルタを組み合わせたSV-PF-RNNを実装し、基本的な粒子フィルタより性能が向上することを示した。
著者: Robert Stok, Paul Bilokon
分類: q-fin.ST, cs.AI, cs.RO, econ.EM, q-fin.CP
原文アブストラクト
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.