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Bayesian estimation of a dynamic conditional correlation model with multivariate Skew-Slash innovations

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  • García de la Fuente, Cristina
  • Galeano San Miguel, Pedro
  • Wiper, Michael Peter

Abstract

Financial returns often present a complex relation with previous observations, along with a slight skewness and high kurtosis. As a consequence, we must pursue the use of flexible models that are able to seize these special features: a financial process that can expose the intertemporal relation between observations, together with a distribution that can capture asymmetry and heavy tails simultaneously. A multivariate extension of the GARCH such as the Dynamic Conditional Correlation model with Skew-Slashinnovations for financial time series in a Bayesian framework is proposed in the present document, and it is illustrated using an MCMC within Gibbs algorithm performed onsimulated data, as well as real data drawn from the daily closing prices of the DAX,CAC40, and Nikkei indices

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  • García de la Fuente, Cristina & Galeano San Miguel, Pedro & Wiper, Michael Peter, 2014. "Bayesian estimation of a dynamic conditional correlation model with multivariate Skew-Slash innovations," DES - Working Papers. Statistics and Econometrics. WS ws141711, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws141711
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    Cited by:

    1. Saker Sabkha & Christian de Peretti, 2018. "On the performances of Dynamic Conditional Correlation models in the Sovereign CDS market and the corresponding bond market," Working Papers hal-01710398, HAL.
    2. Saker Sabkha & Christian de Peretti, 2022. "On the performances of Dynamic Conditional Correlation models in the Sovereign CDS market and the corresponding bond market," Post-Print hal-01710398, HAL.

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