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Copula Models Comparison for Portfolio Risk Assessment

In: Global Economics and Management: Transition to Economy 4.0

Author

Listed:
  • Mikhail Semenov

    (Tomsk Polytechnic University)

  • Daulet Smagulov

    (Tomsk Polytechnic University)

Abstract

This paper presents the results of copula-based variable dependence analysis in short financial time series (253 observations). We propose the algorithm of risk measure computation using copula models. Using the optimal mean-CVaR portfolio, we compute portfolio’s Profit & Loss series and corresponded risk measures curves. Value-at-riskValue-at-risk and Conditional-Value-at-riskValue-at-risk curves were simulated by three copula models: full Gaussian, Student’s t and regular vine copulaVine copula. Amongst many interesting findings, we discover that regular vine copulaVine copula model is the most conservative one, and it does not underestimate the risk. We found that the portfolio Profit & Loss curve movements through a copula line based on CVaR models twice only while VaR models breaks—five times. We have established that the regular vine copulaVine copula model has superior forecasting ability than the Gaussian and the Student’s t one.

Suggested Citation

  • Mikhail Semenov & Daulet Smagulov, 2019. "Copula Models Comparison for Portfolio Risk Assessment," Springer Proceedings in Business and Economics, in: Mikhail Kaz & Tatiana Ilina & Gennady A. Medvedev (ed.), Global Economics and Management: Transition to Economy 4.0, chapter 0, pages 91-102, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-26284-6_9
    DOI: 10.1007/978-3-030-26284-6_9
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    Cited by:

    1. Domino, Krzysztof, 2020. "Multivariate cumulants in outlier detection for financial data analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).

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