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.

Portfolio optimization, asset allocation, adaptive lasso, quantile regression, value-at-risk