Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/53262
Authors: 
Bastianin, Andrea
Year of Publication: 
2009
Series/Report no.: 
Nota di lavoro // Fondazione Eni Enrico Mattei: Sustainable Development 24.2009
Abstract: 
In this paper I have used copula functions to forecast the Value-at-Risk (VaR) of an equally weighted portfolio comprising a small cap stock index and a large cap stock index for the oil and gas industry. The following empirical questions have been analyzed: (i) are there nonnormalities in the marginals? (ii) are there nonnormalities in the dependence structure? (iii) is it worth modelling these nonnormalities in risk- management applications? (iv) do complicated models perform better than simple models? As for questions (i) and (ii) I have shown that the data do deviate from the null of normality at the univariate, as well as at the multivariate level. When considering the dependence structure of the data I have found that asymmetries show up in their unconditional distribution, as well as in their unconditional copula. The VaR forecasting exercise has shown that models based on Normal marginals and/or with symmetric dependence structure fail to deliver accurate VaR forecasts. These findings confirm the importance of nonnormalities and asymmetries both in-sample and out-of-sample.
Subjects: 
Copula functions
Forecasting
Value-At-Risk
JEL: 
C32
C52
C53
G17
Q43
Document Type: 
Working Paper

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