Using Conditional Copula to Estimate Value at Risk

Value at Risk (VaR) plays a central role in risk management. There are several approaches for the estimation of VaR, such as historical simulation, the variance-covariance (also known as analytical), and the Monte Carlo approaches. Whereas the first approach does not assume any distribution, the last two approaches demand the joint distribution to be known, which in the analytical approach is frequently the normal distribution. The copula theory is a fundamental tool in modeling multivariate distributions. It allows the definition of the joint distribution through the marginal distributions and the dependence between the variables. Recently the copula theory has been extended to the conditional case, allowing the use of copulae to model dynamical structures. Time variation in the first and second conditional moments is widely discussed in the literature, so allowing the time variation in the conditional dependence seems to be natural. This work presents some concepts and properties of copula functions and an application of the copula theory in the estimation of VaR of a portfolio composed by Nasdaq and S&P500 stock indices.

並列關鍵字

Copula ； multivariate distribution function ； value-at-risk

被引用紀錄

鄧秋鈺（2010）。Copulas在尾部風險值之模擬與計算〔碩士論文，中原大學〕。華藝線上圖書館。https://doi.org/10.6840/cycu201000464

Iqbal, R., Sorwar, G., & Choudhry, T. (2022). VINE COPULA APPROACH FOR MULTIVARIATE AND MULTI-DAY AHEAD VALUE AT RISK AND EXPECTED SHORTFALL FORECASTING. Advances in Financial Planning and Forecasting, (10), 163-196. https://doi.org/10.6292/AFPF.202212_(10).0007

國際替代計量

Using Conditional Copula to Estimate Value at Risk

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