Abstracts

We derive robust portfolio protection trading strategies by taking into account different aspects of time-variation and dynamics of distributional parameters of financial time series. To model financial time series we first account for the time-dependent dynamics of financial time series via a GARCH(1,1) process, which allows to incorporate volatility clustering and autoregressive behavior in volatility, both of which are well-documented stylized facts of financial time series. Second, we fit the GARCH residual (innovations) to different families of distributions including the Generalised Hyperbolic (GH) distribution and tempered stable distributions. Because of the their ability to incorporate a wide range of empirical stylized facts, both distribution families are popular in modelling financial data. In particular, the GH distribution contains the normal and Student-t-distributions as special cases.
Aside from examining the time-varying behavior of the distributional parameters, we study the spread of the value-at-risk (VaR) between a non-normal and normal GARCH-innovation process. Because of the GARCH component, the magnitude of VaR, when viewed as a process over time, adapts quickly to changes in volatility. The distributional properties of the innovation process on the other hand provides information on skewness, excess kurtosis and in particular on the heaviness of the tails present in the data. The resulting VaR spread can therefore be used to derive an expectation of the frequency of extreme events which in turn generates signals of the temporal presence of tail risks.
This information, in particular the information about potential ‘tail-risks’ contained in the short-term VaR is used to generate trading signals with the intention to protect against extreme losses and at the same time to not miss the upside. This portfolio protection trading strategy is compared to CPPI and protective put trading strategies, which are popular portfolio insurance strategies. Based on DAX returns from 1996 to 2013 we find that the tail-risk protection strategy outperforms the classical strategies, for example in terms of a higher Sharpe ratio and when comparing excess returns relative to maximum drawdown (Calmar ratio). These results are backed by robustness tests, e.g. by comparing the trading strategy to a randomly generated trading strategy.