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Empirical studies in a multivariate non-stationary, nonparametric regression model for financial returns

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  • Gürtler, Marc
  • Rauh, Ronald

Abstract

In this paper we analyze a multivariate non-stationary regression model empirically. With the knowledge about unconditional heteroscedasticty of financial returns, based on univariate studies and a congruent paradigm in Gürtler and Rauh (2009), we test for a time-varying covariance structure firstly. Based on these results, a central component of our non-stationary model is a kernel regression for pairwise covariances and the covariance matrix. Residual terms are fitted with an asymmetric Pearson type VII distribution. In an extensive study we estimate the linear dependence of a broad portfolio of equities and fixed income securities (including credit and currency risks) and fit the whole approach to provide distributional forecasts. Our evaluations verify a reasonable approximation and a satisfactory forecasting quality with an out performance against a traditional risk model.

Suggested Citation

  • Gürtler, Marc & Rauh, Ronald, 2013. "Empirical studies in a multivariate non-stationary, nonparametric regression model for financial returns," Working Papers IF43V1, Technische Universität Braunschweig, Institute of Finance.
    • Handle: RePEc:zbw:tbsifw:if43v1
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    References listed on IDEAS

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    1. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    2. Silvennoinen, Annastiina & Teräsvirta, Timo, 2007. "Multivariate GARCH models," SSE/EFI Working Paper Series in Economics and Finance 669, Stockholm School of Economics, revised 18 Jan 2008.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    5. 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.).
    6. Gürtler, Marc & Rauh, Ronald, 2012. "Challenging traditional risk models by a non-stationary approach with nonparametric heteroscedasticity," Working Papers IF41V1, Technische Universität Braunschweig, Institute of Finance.
    7. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    8. Cătălin Stărică & Clive Granger, 2005. "Nonstationarities in Stock Returns," The Review of Economics and Statistics, MIT Press, vol. 87(3), pages 503-522, August.
    9. Gürtler, Marc & Rauh, Ronald, 2009. "Shortcomings of a parametric VaR approach and nonparametric improvements based on a non-stationary return series model," Working Papers IF32V2, Technische Universität Braunschweig, Institute of Finance.
    10. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    11. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    12. Choi, Pilsun & Nam, Kiseok, 2008. "Asymmetric and leptokurtic distribution for heteroscedastic asset returns: The SU-normal distribution," Journal of Empirical Finance, Elsevier, vol. 15(1), pages 41-63, January.
    13. Bougerol, Philippe & Picard, Nico, 1992. "Stationarity of Garch processes and of some nonnegative time series," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 115-127.
    14. Thomas Mikosch & Catalin Starica, 2004. "Non-stationarities in financial time series, the long range dependence and the IGARCH effects," Econometrics 0412005, University Library of Munich, Germany.
    15. Gürtler, Marc & Kreiss, Jens-Peter & Rauh, Ronald, 2009. "A non-stationary approach for financial returns with nonparametric heteroscedasticity," Working Papers IF31V2, Technische Universität Braunschweig, Institute of Finance.
    16. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
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    More about this item

    Keywords

    heteroscedasticity; non-stationarity; nonparametric regression; volatility; covariance matrix; innovation modeling; asymmetric heavy-tails; multivariate distributional forecast; empirical studies;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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