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.

Maruotti, A., Punzo, A., Bagnato, L., Hidden Markov and Semi-Markov Models with Multivariate Leptokurtic-Normal Components for Robust Modeling of Daily Returns Series, <<JOURNAL OF FINANCIAL ECONOMETRICS>>, 2019; 17 (1): 91-117. [doi:10.1093/jjfinec/nby019] [http://hdl.handle.net/10807/126371]

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

Bagnato, Luca
Ultimo
2019

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.
Inglese
Maruotti, A., Punzo, A., Bagnato, L., Hidden Markov and Semi-Markov Models with Multivariate Leptokurtic-Normal Components for Robust Modeling of Daily Returns Series, <<JOURNAL OF FINANCIAL ECONOMETRICS>>, 2019; 17 (1): 91-117. [doi:10.1093/jjfinec/nby019] [http://hdl.handle.net/10807/126371]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10807/126371
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