A new approach to volatility modeling: the High-Dimensional Markov model

Arnaud Dufays, Maciej Augustyniak and Luc Bauwens

Cahiers de recherche from Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques

Abstract: A new model -the high-dimensional Markov (HDM) model - is proposed for financial returns and their latent variances. It is also applicable to model directly realized variances. Volatility is modeled as a product of three components: a Markov chain driving volatility persistence, an independent discrete process capable of generating jumps in the volatility, and a predictable (data-driven) process capturing the leverage effect. The Markov chain and jump components allow volatility to switch abruptly between thousands of states. The transition probability matrix of the Markov chain is structured in such a way that the multiplicity of the second largest eigenvalue can be greater than one. This distinctive feature generates a high degree of volatility persistence. The statistical properties of the HDM model are derived and an economic interpretation is attached to each component. In-sample results on six financial time series highlight that the HDM model compares favorably to the main existing volatility processes. A forecasting experiment shows that the HDM model significantly outperforms its competitors when predicting volatility over time horizons longer than five days.

Keywords: Volatility; Markov-switching; Persistence; Leverage effect. (search for similar items in EconPapers)
JEL-codes: C22 C51 C58 (search for similar items in EconPapers)
Date: 2016
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: A New Approach to Volatility Modeling: The High-Dimensional Markov Model (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:lvl:crrecr:1609

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