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From Deep Filtering to Deep Econometrics

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  • Robert Stok
  • Paul Bilokon

Abstract

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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  • Robert Stok & Paul Bilokon, 2023. "From Deep Filtering to Deep Econometrics," Papers 2311.06256, arXiv.org.
    • Handle: RePEc:arx:papers:2311.06256
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    References listed on IDEAS

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    1. Carol Alexander, "undated". "Principal Component Analysis of Volatility Smiles and Skews," ICMA Centre Discussion Papers in Finance icma-dp2000-10, Henley Business School, University of Reading, revised Jun 2000.
    2. Dinghai Xu & Yuying Li, 2010. "Empirical Evidence of the Leverage Effect in a Stochastic Volatility Model: A Realized Volatility Approach," Working Papers 1002, University of Waterloo, Department of Economics, revised May 2010.
    3. Stephen J. Taylor, 1994. "Modeling Stochastic Volatility: A Review And Comparative Study," Mathematical Finance, Wiley Blackwell, vol. 4(2), pages 183-204, April.
    4. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2023. "A Machine Learning Approach to Volatility Forecasting," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1680-1727.
    5. Malik, S. & Pitt, M. K., 2011. "Modelling Stochastic Volatility with Leverage and Jumps: A Simulated Maximum Likelihood Approach via Particle Filtering," Working papers 318, Banque de France.
    6. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    7. Michael Pitt & Sheheryar Malik & Arnaud Doucet, 2014. "Simulated likelihood inference for stochastic volatility models using continuous particle filtering," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 527-552, June.
    8. Taylor, Stephen J., 1987. "Forecasting the volatility of currency exchange rates," International Journal of Forecasting, Elsevier, vol. 3(1), pages 159-170.
    9. Sandmann, Gleb & Koopman, Siem Jan, 1998. "Estimation of stochastic volatility models via Monte Carlo maximum likelihood," Journal of Econometrics, Elsevier, vol. 87(2), pages 271-301, September.
    10. Neil Shephard & Ole E. Barndorff-Nielsen, 1998. "Incorporation of a Leverage Effect in a Stochastic Volatility Model," Economics Series Working Papers 1998-W14, University of Oxford, Department of Economics.
    11. Malik, Sheheryar & Pitt, Michael K., 2009. "Modelling Stochastic Volatility with Leverage and Jumps: A Simulated Maximum Likelihood Approach via Particle Filtering," Economic Research Papers 271302, University of Warwick - Department of Economics.
    12. Malik, Sheheryar & Pitt, Michael K, 2009. "Modelling Stochastic Volatility with Leverage and Jumps : A Simulated Maximum Likelihood Approach via Particle Filtering," The Warwick Economics Research Paper Series (TWERPS) 897, University of Warwick, Department of Economics.
    13. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
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