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A new multivariate nonlinear time series model for portfolio risk measurement: the threshold copula-based TAR approach

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  • Wong, Shiu Fung
  • Tong, Howell
  • Siu, Tak Kuen
  • Lu, Zudi

Abstract

We propose a threshold copula-based nonlinear time series model for evaluating quantitative risk measures for financial portfolios with a flexible structure to incorporate nonlinearities in both univariate (component) time series and their dependent structure. We incorporate different dependent structures of asset returns over different market regimes, which are manifested in their price levels. We estimate the model parameters by a two-stage maximum likelihood method. Real financial data and appropriate statistical tests are used to illustrate the efficacy of the proposed model. Simulated results for sampling distribution of parameters estimates are given. Empirical results suggest that the proposed model leads to significant improvement of the accuracy of value-at-risk forecasts at the portfolio lev

Suggested Citation

  • Wong, Shiu Fung & Tong, Howell & Siu, Tak Kuen & Lu, Zudi, 2017. "A new multivariate nonlinear time series model for portfolio risk measurement: the threshold copula-based TAR approach," LSE Research Online Documents on Economics 78515, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:78515
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    1. King, Mervyn A & Wadhwani, Sushil, 1990. "Transmission of Volatility between Stock Markets," The Review of Financial Studies, Society for Financial Studies, vol. 3(1), pages 5-33.
    2. Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation of copula-based semiparametric time series models," Journal of Econometrics, Elsevier, vol. 130(2), pages 307-335, February.
    3. Taimur Baig & Ilan Goldfajn, 1999. "Financial Market Contagion in the Asian Crisis," IMF Staff Papers, Palgrave Macmillan, vol. 46(2), pages 1-3.
    4. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    5. Bertero, Elisabetta & Mayer, Colin, 1990. "Structure and performance: Global interdependence of stock markets around the crash of October 1987," European Economic Review, Elsevier, vol. 34(6), pages 1155-1180, September.
    6. Jondeau, Eric & Rockinger, Michael, 2006. "The Copula-GARCH model of conditional dependencies: An international stock market application," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 827-853, August.
    7. Calvo, Sara & Reinhart, Carmen, 1996. "Capital flows to Latin America : Is there evidence of contagion effects?," Policy Research Working Paper Series 1619, The World Bank.
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    Cited by:

    1. Zaichao Du & Pei Pei, 2020. "Backtesting portfolio value‐at‐risk with estimated portfolio weights," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(5), pages 605-619, September.
    2. Shulin Zhang & Qian M. Zhou & Huazhen Lin, 2021. "Goodness-of-fit test of copula functions for semi-parametric univariate time series models," Statistical Papers, Springer, vol. 62(4), pages 1697-1721, August.

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    More about this item

    Keywords

    quantitative risk measures; copulas; multivariate nonlinear time series; threshold principle;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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