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

Luca Bagnato, Antonello Maruotti, Antonio Punzo

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)91-117
Number of pages27
JournalJournal of Financial Econometrics
DOIs
Publication statusPublished - 2019

Keywords

  • Hidden Markov Models
  • Leptokurtic Normal Distribution

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