Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2018 038

Tail-Risk Protection Trading Strategies

Natalie Packham
Jochen Papenbrock
Peter Schwendner
Fabian Woebbeking

Starting from well-known empirical stylised facts of nancial time series, we develop
dynamic portfolio protection trading strategies based on econometric methods. As a criterion
for riskiness we consider the evolution of the value-at-risk spread from a GARCH
model with normal innovations relative to a GARCH model with generalised innovations.
These generalised innovations may for example follow a Student t, a generalised
hyperbolic (GH), an alpha-stable or a Generalised Pareto (GPD) distribution. Our
results indicate that the GPD distribution provides the strongest signals for avoiding
tail risks. This is not surprising as the GPD distribution arises as a limit of tail behaviour
in extreme value theory and therefore is especially suited to deal with tail risks.
Out-of-sample backtests on 11 years of DAX futures data, indicate that the dynamic
tail-risk protection strategy eectively reduces the tail risk while outperforming traditional
portfolio protection strategies. The results are further validated by calculating
the statistical signicance of the results obtained using bootstrap methods. A number of
robustness tests including application to other assets further underline the eectiveness
of the strategy. Finally, by empirically testing for second order stochastic dominance,
we nd that risk averse investors would be willing to pay a positive premium to move
from a static buy-and-hold investment in the DAX future to the tail-risk protection

tail-risk protection, portfolio protection, extreme events, tail distributions

JEL Classification:
C15, G11, G17.