A new approach to volatility modeling : the factorial hidden Markov volatility model

Authors: Augustyniak, Maciej; Bauwens, Luc; Dufays, Arnaud
Abstract: A new process — the factorial hidden Markov volatility (FHMV) model — is proposed to model financial returns or realized variances. Its dynamics are driven by a latent volatility process specified as a product of three components: a Markov chain controlling volatility persistence, an independent discrete process capable of generating jumps in the volatility, and a predictable (data-driven) process capturing the leverage effect. An economic interpretation is attached to each one of these components. Moreover, the Markov chain and jump components allow volatility to switch abruptly between thousands of states, and the transition matrix of the model is structured to generate a high degree of volatility persistence. An empirical study on six financial time series shows that the FHMV process compares favorably to state-of-the-art volatility models in terms of in-sample fit and out-of-sample forecasting performance over time horizons ranging from one to one hundred days.
Document Type: Article de recherche
Issue Date: 23 February 2018
Open Access Date: 23 February 2019
Document version: AM
Permalink: http://hdl.handle.net/20.500.11794/19622
This document was published in: Journal of business and economic statistics, (2018)
http://doi.org/10.1080/07350015.2017.1415910
Alternative version: 10.1080/07350015.2017.1415910
Collection:Articles publiés dans des revues avec comité de lecture

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