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Communication Dans Un Congrès Année : 2016

Unsupervised learning of Markov-switching stochastic volatility with an application to market data

Résumé

We introduce a new method for estimating the regime-switching stochastic volatility models from the historical prices. Our methodology is based on a novel version of the assumed density filter (ADF). We estimate the switching model by maximizing the quasi-likelihood function of our ADF. The simulation experiments show the efficiency of our method. Then we analyze different market price histories for consistency with a regime-shifting model
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Dates et versions

hal-01548330 , version 1 (27-06-2017)

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Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski. Unsupervised learning of Markov-switching stochastic volatility with an application to market data. MLSP 2016 : 26th International Workshop on Machine Learning for Signal Processing, Sep 2016, Salerno, Italy. pp.1 - 6, ⟨10.1109/MLSP.2016.7738821⟩. ⟨hal-01548330⟩
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