A central aspect of time series analysis is the modeling of dependence over time. Workhorse time series models such as the autoregressive moving average (ARMA) model popularized by Box and Jenkins (1970), the generalized autoregressive conditional heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986), or the autoregressive conditional duration (ACD) model of Engle and Russell (...

Through empirical research is identified that the hypothesis of normal distribution of returns is no longer observed while verifying the existence of heavy tails and asymmetries in the distribution. Thus, the article has aimed empirically apply copula models using techniques of realized volatility (HAR) with high-frequency data and perform the calculation of Value at Risk for different periods....

In this research we introduce a new class of multivariate probability models to the marketing literature. Known as “copula models”, they have a number of attractive features. First, they permit the combination of any univariate marginal distributions that need not come from the same distributional family. Second, a particular class of copula models, called “elliptical copula”, have the property...

In order to capture the dependency among exchange rates we construct semiparametric multivariate copula models with ARMA-GARCH margins. As multivariate copula models we utilize pair-copula constructions (PCC) such as regular and canonical vines. As building blocks of the PCC’s we use bivariate t-copulas for different tail dependence between pairs of exchange rates. Alternatively we also conside...

The demand for an accurate financial risk management involving larger numbers of assets is strong not only in view of the financial crisis of 2007-2009. Especially dependencies among assets have not been captured adequately. While standard multivariate copulas have added some flexibility, this flexibility is insufficient in higher dimensional applications. Vine copulas can fill this gap by bene...