by Shang C. Chiou and Ruey S. Tsay

Journal of Data Science, v.6, no.3, 273-301

Abstract

Copulas are useful tools to study the relationship between random variables. In financial applications, they can separate the marginal distributions from the dynamic dependence of asset prices. The marginal distributions may assume some univariate volatility models whereas the dynamic dependence can be time-varying and depends on some explanatory variables. In this paper, we consider applications of copulas in finance. First, we combine the risk-neutral representation and copula-based models to price multivariate exotic derivatives. Second, we show that copula-based models can be used to assess value at risk of multiple assets. We demonstrate the applications using daily log returns of two market indices and compare the proposed method with others available in the literature.

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