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http://dx.doi.org/10.5351/KJAS.2021.34.6.875

Value at Risk calculation using sparse vine copula models  

An, Kwangjoon (Department of Statistics, Sungkyunkwan University)
Baek, Changryong (Department of Statistics, Sungkyunkwan University)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.6, 2021 , pp. 875-887 More about this Journal
Abstract
Value at Risk (VaR) is the most popular measure for market risk. In this paper, we consider the VaR estimation of portfolio consisting of a variety of assets based on multivariate copula model known as vine copula. In particular, sparse vine copula which penalizes too many parameters is considered. We show in the simulation study that sparsity indeed improves out-of-sample forecasting of VaR. Empirical analysis on 60 KOSPI stocks during the last 5 years also demonstrates that sparse vine copula outperforms regular copula model.
Keywords
value at risk; sparse vine copula; lasso; dependence structure;
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