Hidden Markov and Semi-Markov Models with Multivariate Leptokurtic-Normal Components for Robust Modeling of Daily Returns Series

Antonello Maruotti, Antonio Punzo and Luca Bagnato

Journal of Financial Econometrics, 2019, vol. 17, issue 1, 91-117

Abstract: We introduce multivariate models for the analysis of stock market returns. Our models are developed under hidden Markov and semi-Markov settings to describe the temporal evolution of returns, whereas the marginal distribution of returns is described by a mixture of multivariate leptokurtic-normal (LN) distributions. Compared to the normal distribution, the LN has an additional parameter governing excess kurtosis and this allows us a better fit to both the distributional and dynamic properties of daily returns. We outline an expectation maximization algorithm for maximum likelihood estimation which exploits recursions developed within the hidden semi-Markov literature. As an illustration, we provide an example based on the analysis of a bivariate time series of stock market returns.

Keywords: daily returns; elliptical distributions; EM algorithm; hidden Markov model; hidden semi-Markov model; kurtosis; multivariate time series (search for similar items in EconPapers)
JEL-codes: C32 C51 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (8)

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