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Extracting volatility signal using maximum a posteriori estimation

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  • Neto, David

Abstract

This paper outlines a methodology to estimate a denoised volatility signal for foreign exchange rates using a hidden Markov model (HMM). For this purpose a maximum a posteriori (MAP) estimation is performed. A double exponential prior is used for the state variable (the log-volatility) in order to allow sharp jumps in realizations and then log-returns marginal distributions with heavy tails. We consider two routes to choose the regularization and we compare our MAP estimate to realized volatility measure for three exchange rates.

Suggested Citation

  • Neto, David, 2016. "Extracting volatility signal using maximum a posteriori estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 788-794.
    • Handle: RePEc:eee:phsmap:v:461:y:2016:i:c:p:788-794
    DOI: 10.1016/j.physa.2016.05.065
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    References listed on IDEAS

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    5. Sardy, Sylvain & Tseng, Paul, 2004. "On the Statistical Analysis of Smoothing by Maximizing Dirty Markov Random Field Posterior Distributions," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 191-204, January.
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