This article is concerned with the study of the tail correlation among equity indices by means of dynamic copula functions. The main idea is to consider the impact of the use of copula functions in the accuracy of the model’s parameters and in the computation of Value-at-Risk (VaR). Results show that copulas provide more sophisticated results in terms of the accuracy of the forecasted VaR, in particular, if they are compared with the results obtained from Dynamic Conditional Correlation (DCC) model

Cortese, F. (2019). Tail Dependence in Financial Markets: A Dynamic Copula Approach. RISKS, 7(4) [10.3390/risks7040116].

Tail Dependence in Financial Markets: A Dynamic Copula Approach

Cortese, FP
2019

Abstract

This article is concerned with the study of the tail correlation among equity indices by means of dynamic copula functions. The main idea is to consider the impact of the use of copula functions in the accuracy of the model’s parameters and in the computation of Value-at-Risk (VaR). Results show that copulas provide more sophisticated results in terms of the accuracy of the forecasted VaR, in particular, if they are compared with the results obtained from Dynamic Conditional Correlation (DCC) model
Articolo in rivista - Articolo scientifico
copula functions; Monte Carlo simulation techniques; risk measures
English
2019
7
4
116
open
Cortese, F. (2019). Tail Dependence in Financial Markets: A Dynamic Copula Approach. RISKS, 7(4) [10.3390/risks7040116].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/254226
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