Work detail

Multivariate Dependence Modeling Using Copulas

Author: Mgr. Marek Klaus
Year: 2012 - summer
Work type: Finance, Financial Markets and Banking
Language: English
Pages: 72
Awards and prizes:
Abstract: Multivariate volatility models, such as DCC MGARCH, are estimated under
assumption of multivariate normal distribution of random variables, while this
assumption have been rejected by empirical evidence. Therefore, the estimated
conditional correlation may not explain the whole dependence structure, since
under non-normality the linear correlation is only one of the dependency measures.
The aim of this thesis is to employ a copula function to the DCC MGARCH
model, as copulas are able to link non-normal marginal distributions to create
corresponding multivariate joint distribution. The copula-based MGARCH
model with uncorrelated dependent errors permits to model conditional correlation
by DCC-MGARCH and dependence by the copula function, separately
and simultaneously. In other words the model aims to explain additional dependence
not captured by traditional DCC MGARCH model due to assumption of
normality. In the empirical analysis we apply the model on datasets consisting
primarily of stocks of the PX Index and on the pair of S&P500 and NASDAQ100
in order to compare the copula-based MGARCH model to traditional
DCC MGARCH in terms of capturing the dependency structure.
Downloadable: Master Theses of Klaus




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