From Deep Filtering to Deep Econometrics

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Tác giả: Paul Bilokon, Robert Stok

Ngôn ngữ: eng

Ký hiệu phân loại: 330.18 Economics

Thông tin xuất bản: 2023

Mô tả vật lý:

Bộ sưu tập: Báo, Tạp chí

ID: 198403

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.
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