We introduce a new multivariate model of multiple asset returns. Our model is based on weighted in- dexed semi-Markov chains to describe the single (marginals) asset returns, whereas the dependence structure among the considered assets is described by introducing copula functions. A real application of the proposed multivariate model is presented based on the evolution of 6 stocks from the Italian Stock Exchange. We provide empirical evidence that the model is able to correctly reproduce statistical regular- ities of multivariate real data such as the cross-correlation function, value-at-risk, marginal value-at-risk and conditional value-at-risk. The model is also used for volatility forecasting of each stock.

Copula based multivariate semi-Markov models with applications in high-frequency finance

Guglielmo D’Amico;
2018

Abstract

We introduce a new multivariate model of multiple asset returns. Our model is based on weighted in- dexed semi-Markov chains to describe the single (marginals) asset returns, whereas the dependence structure among the considered assets is described by introducing copula functions. A real application of the proposed multivariate model is presented based on the evolution of 6 stocks from the Italian Stock Exchange. We provide empirical evidence that the model is able to correctly reproduce statistical regular- ities of multivariate real data such as the cross-correlation function, value-at-risk, marginal value-at-risk and conditional value-at-risk. The model is also used for volatility forecasting of each stock.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/685569
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