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EnglishWe 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.
| Titolo: | Hidden Markov and semi-Markov models with multivariate leptokurtic-normal components for robust modeling of daily returns series |
| Autori interni: | |
| Data di pubblicazione: | 2019 |
| Rivista: | |
| Handle: | http://hdl.handle.net/20.500.11769/361462 |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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http://hdl.handle.net/20.500.11769/361462
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