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Forecasting Market Risk of Portfolios: Copula-Markov Switching Multifractal Approach

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  • Mawuli Segnon
  • Mark Trede

Abstract

This paper proposes a new methodology for modeling and forecasting market risks of portfolios. It is based on a combination of copula functions and Markov switching multifractal (MSM) processes. We assess the performance of the copula-MSM model by computing the value at risk of a portfolio composed of the NASDAQ composite index and the S&P 500. Using the likelihood ratio (LR) test by Christofferrsen (1998), the GMM duration-based test by Candelon et al. (2011) and the superior predictive ability (SPA) test by Hansen (2005) we evaluate the predictive ability of the copula-MSM model and compare it to other common approaches such as historical simulation, variance-covariance, Risk-Metrics, copula-GARCH and constant conditional correlation GARCH (CCCGARCH) models. We find that the copula-MSM model is more robust, provides the best fit and outperforms the other models in terms of forecasting accuracy and VaR prediction.

Suggested Citation

  • Mawuli Segnon & Mark Trede, 2017. "Forecasting Market Risk of Portfolios: Copula-Markov Switching Multifractal Approach," CQE Working Papers 6617, Center for Quantitative Economics (CQE), University of Muenster.
    • Handle: RePEc:cqe:wpaper:6617
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    References listed on IDEAS

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    Cited by:

    1. Gaete, Michael & Herrera, Rodrigo, 2023. "Diversification benefits of commodities in portfolio allocation: A dynamic factor copula approach," Journal of Commodity Markets, Elsevier, vol. 32(C).
    2. Amaro, Raphael & Pinho, Carlos, 2022. "Energy commodities: A study on model selection for estimating Value-at-Risk," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 5-27.
    3. Lin, Edward M.H. & Sun, Edward W. & Yu, Min-Teh, 2020. "Behavioral data-driven analysis with Bayesian method for risk management of financial services," International Journal of Production Economics, Elsevier, vol. 228(C).
    4. Xingyu Dai & Dongna Zhang & Chi Keung Marco Lau & Qunwei Wang, 2023. "Multiobjective portfolio optimization: Forecasting and evaluation under investment horizon heterogeneity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2167-2196, December.
    5. Rewat Khanthaporn, 2022. "Analysis of Nonlinear Comovement of Benchmark Thai Government Bond Yields," PIER Discussion Papers 183, Puey Ungphakorn Institute for Economic Research.
    6. Wang, Yi & Sun, Qi & Zhang, Zilu & Chen, Liqing, 2022. "A risk measure of the stock market that is based on multifractality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).

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    More about this item

    Keywords

    Copula; Multifractal processes; GARCH; VaR; Backtesting; SPA;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics

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