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TEDAS - Tail Event Driven ASset Allocation

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  • Härdle, Wolfgang Karl
  • Nasekin, Sergey
  • Lee, David Kuo Chuen
  • Fai, Phoon Kok

Abstract

Portfolio selection and risk management are very actively studied topics in quantitative finance and applied statistics. They are closely related to the dependency structure of portfolio assets or risk factors. The correlation structure across assets and opposite tail movements are essential to the asset allocation problem, since they determine the level of risk in a position. Correlation alone is not informative on the distributional details of the assets. By introducing TEDAS -Tail Event Driven ASset allocation, one studies the dependence between assets at different quantiles. In a hedging exercise, TEDAS uses adaptive Lasso based quantile regression in order to determine an active set of negative non-zero coefficients. Based on these active risk factors, an adjustment for intertemporal correlation is made. Finally, the asset allocation weights are determined via a Cornish-Fisher Value-at-Risk optimization. TEDAS is studied in simulation and a practical utility-based example using hedge fund indices.

Suggested Citation

  • Härdle, Wolfgang Karl & Nasekin, Sergey & Lee, David Kuo Chuen & Fai, Phoon Kok, 2014. "TEDAS - Tail Event Driven ASset Allocation," SFB 649 Discussion Papers 2014-032, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2014-032
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    1. Bhandari, Rishabh & Das, Sanjiv R., 2009. "Options on portfolios with higher-order moments," Finance Research Letters, Elsevier, vol. 6(3), pages 122-129, September.
    2. J. Cvitanic & A. Lazrak & L. Martellini & F. Zapatero, 2003. "Optimal allocation to hedge funds: an empirical analysis," Quantitative Finance, Taylor & Francis Journals, vol. 3(1), pages 28-39.
    3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    4. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    5. Giamouridis, Daniel & Vrontos, Ioannis D., 2007. "Hedge fund portfolio construction: A comparison of static and dynamic approaches," Journal of Banking & Finance, Elsevier, vol. 31(1), pages 199-217, January.
    6. Hans Bystrom, 2004. "Orthogonal GARCH and covariance matrix forecasting: The Nordic stock markets during the Asian financial crisis 1997-1998," The European Journal of Finance, Taylor & Francis Journals, vol. 10(1), pages 44-67.
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    Cited by:

    1. Giovanni Bonaccolto & Massimiliano Caporin & Sandra Paterlini, 2018. "Asset allocation strategies based on penalized quantile regression," Computational Management Science, Springer, vol. 15(1), pages 1-32, January.
    2. Wolfgang Karl Härdle & David Kuo Chuen Lee & Sergey Nasekin & Alla Petukhina, 2018. "Tail Event Driven ASset allocation: evidence from equity and mutual funds’ markets," Journal of Asset Management, Palgrave Macmillan, vol. 19(1), pages 49-63, January.
    3. Chen, Cathy Yi-Hsuan & Chiang, Thomas C. & Härdle, Wolfgang Karl, 2016. "Downside risk and stock returns: An empirical analysis of the long-run and short-run dynamics from the G-7 Countries," SFB 649 Discussion Papers 2016-001, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. repec:hum:wpaper:sfb649dp2016-001 is not listed on IDEAS

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    More about this item

    Keywords

    Portfolio optimization; asset allocation; adaptive lasso; quantile regression; value-at-risk;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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