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TEDAS - Tail Event Driven ASset Allocation

Author

Listed:
  • 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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    References listed on IDEAS

    as
    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. William Fung & David A. Hsieh, 2002. "Hedge-Fund Benchmarks: Information Content and Biases," Financial Analysts Journal, Taylor & Francis Journals, vol. 58(1), pages 22-34, January.
    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.
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    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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