Tail Event Driven ASset allocation: evidence from equity and mutual funds’ markets
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. Recently introduced TEDAS—Tail Event Driven ASset allocation approach determines the dependence between assets at different tail measures. TEDAS uses adaptive Lasso-based quantile regression in order to determine an active set of negative coefficients. Based on these active risk factors, an adjustment for intertemporal correlation is made. In this research, authors aim to develop TEDAS, by introducing three TEDAS modifications differing in allocation weights’ determination: a Cornish–Fisher Value-at-Risk minimization, Markowitz diversification rule or naïve equal weighting. TEDAS strategies significantly outperform other widely used allocation approaches on two asset markets: German equity and Global mutual funds.
KeywordsAdaptive lasso Portfolio optimization Quantile regression Value-at-Risk Tail events
JEL ClassificationC00 C14 C50 C58
- Alexander, C. 2001. A Primer on the Orthogonal GARCH Model. Reading: ISMA Centre, University of Reading. Unpublished manuscript.Google Scholar
- Bender, J., Briand, R., Fachinotti, G., and S. Ramachandran. 2005. Small Caps? No Small Oversight: Institutional Investors and Global Small Cap Equities. MSCI Research Insight, March 2012. http://www.msci.com/www/research-paper/small-caps-no-small-oversight/014391548. Accessed Feb 2015.
- Borke, L., and W.K. Härdle. 2016. Q3-D3-LSA, Berlin: Humboldt Universität zu Berlin. SFB 649 Discussion Paper 2016-049.Google Scholar
- Crain, M. 2011. A Literature Review of the Size Effect. Working Paper, 29 October 2011. http://www.ssrn.com/abstract_id=1710076. Accessed Feb 2015.
- European Fund and Asset Management Association. 2016. Worldwide Regulated Open-Ended Fund Assets and Flows. Trends in the Fourth Quarter of 2015. European Fund and Asset Management association (EFAMA), March 2016. https://www.efama.org. Accessed June 2016.
- Franke, J., W.K. Härdle, and C.M. Hafner. 2015. Statistics of Financial Markets: An Introduction, 4th ed. Berlin: Springer.Google Scholar
- Härdle, W.K., Nasekin, S., Lee, D.K.C. and K.F. Phoon. 2014. TEDAS—Tail Event Driven Asset Allocation. Berlin: Humboldt Universität zu Berlin. SFB 649 Discussion Paper 2014-032.Google Scholar
- Investment Company Institute. 2016. Investment Company Fact Book: 2016. Investment company institute (ICI), May 2016. https://www.icifactbook.org. Accessed June 2016.
- Jobson, J.D., B. Korkie, and V. Ratti. 1979. Improved Estimation for Markowitz Portfolios Using James–Stein Type Estimators. Proceedings of the American Statistical Association, Business and Economics Statistics 41: 279–284.Google Scholar
- Kazemi, H. 2012. An Introduction to Risk Parity. Alternative Investment Analyst Review. Chartered Alternative Investment Analyst Association, April 2012. https://www.caia.org. Accessed Feb 2015.
- Markowitz, H. 1952. Portfolio Selection. The Journal of Finance 7(1): 77–91.Google Scholar
- Nathan, A. (ed.) 2013. Bond Bubble Breakdown. Commodities and Strategy Research, 22 April. http://www.nber.org. Accessed Feb 2015.
- Swensen, D.F. 2009. Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment, Fully Revised and Updated. New York: Free Press.Google Scholar
- Tibshirani, R. 1996. Regression Shrinkage and Selection via the Lasso. Journal of Royal Statistical Society 58(1): 267–288.Google Scholar
- Zou, H. 2006. The Adaptive Lasso and Its Oracle Properties. Journal of Statistical Planning and Inference 101(476): 1418–1429.Google Scholar