arXiv 13 Sep 2023 · Finance — Statistical Finance · 1 citations (OpenAlex)
arXiv:2311.06256 · PDF · DOI · OpenAlex · Extracted main text
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.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Pitt MK, Shephard N (1999) Filtering via Simulation: Auxiliary Particle Filters | 0.928 | 4 | 4 | 100% |
| 2 | Black F, Scholes M (1973) The Pricing of Options and Corporate Liabilities | 0.737 | 3 | 2 | 100% |
| 3 | Christensen K, Siggaard M, Veliyev B (2021) A machine learning approach to volatility forecasting | 0.737 | 3 | 2 | 100% |
| 4 | Kim HY, Won CH (2018) Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models | 0.737 | 3 | 2 | 100% |
| 5 | Ma X, Karkus P, Hsu D (2020) Particle Filter Recurrent Neural Networks | 0.737 | 3 | 2 | 100% |
| 6 | Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity | 0.644 | 2 | 2 | 100% |
| 7 | Ge W, Lalbakhsh P, Isai L, Lenskiy A, Suominen H (2022) Neural NetworkBased Financial Volatility Forecasting: A Systematic Review | 0.644 | 2 | 2 | 100% |
| 8 | Goodfellow I, Bengio Y, Courville A (2016) Deep Learning | 0.644 | 2 | 2 | 100% |
| 9 | Hu Y, Ni J, Wen L (2020) A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction | 0.644 | 2 | 2 | 100% |
| 10 | Karkus P, Hsu D, Lee WS (2018) Particle Filter Networks with Application to Visual Localization | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 51 scored citations.