Multivariate high-frequency financial data via semi-Markov processes
AbstractIn this paper we propose a bivariate generalization of a weighted indexed semi-Markov chains to study the high frequency price dynamics of traded stocks. We assume that financial returns are described by a weighted indexed semi-Markov chain model. We show, through Monte Carlo simulations, that the model is able to reproduce important stylized facts of financial time series like the persistence of volatility and at the same time it can reproduce the correlation between stocks. The model is applied to data from Italian stock market from 1 January 2007 until the end of December 2010.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1305.0436.
Date of creation: May 2013
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-05-05 (All new papers)
- NEP-ETS-2013-05-05 (Econometric Time Series)
- NEP-MST-2013-05-05 (Market Microstructure)
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