Publikationen

This study presents a systematic comparison of portfolio insurance strategies. We implement a bootstrap-based hypothesis test to assess statistical significance of the differences in a variety of downside-oriented risk and performance measures for pairs of portfolio insurance strategies. Our comparison of different strategies considers the following distinguishing characteristics: static versus dynamic protection; initial wealth versus cumulated wealth protection; model-based versus model-free protection; and strong floor compliance versus probabilistic floor compliance. Our results indicate that the classical portfolio insurance strategies synthetic put and constant proportion portfolio insurance (CPPI) provide superior downside protection compared to a simple stop-loss trading rule and also exhibit a higher risk-adjusted performance in many cases (dependent on the applied performance measure). Analyzing recently developed strategies, neither the TIPP strategy (as an ‘improved’ CPPI strategy) nor the dynamic VaR-strategy provides significant improvements over the more traditional portfolio insurance strategies.

We compare the risk-adjusted performance of stock–bond portfolios between rebalancing and buy-and-hold across different asset allocations by reporting statistical significance levels. Our investigation is based on a 30-year dataset and incorporates the financial markets of the United States, the United Kingdom and Germany. To draw useful recommendations to investment management, we implement a history-based simulation approach which enables us to mimic realistic market conditions. Even if the portfolio weight of stocks is very low, our empirical results show that a frequent rebalancing significantly enhances risk-adjusted portfolio performance for all analysed countries and all risk-adjusted performance measures.

This study compares the performance of different rebalancing strategies under realistic market conditions by reporting statistical significance levels. Our analysis is based on historical data from the United States, the United Kingdom, and Germany and comprises three different classes of rebalancing (periodic, threshold, and range rebalancing). Despite cross-country differences, our history-based simulation results show that all rebalancing strategies outperform a buy-and-hold strategy in terms of Sharpe ratios, Sortino ratios, and Omega measures. The differences in risk-adjusted performance are not only statistically significant, but also economically relevant. However, the choice of a particular rebalancing strategy is of only minor economic importance.

This study examines whether shocks from macroeconomic variables or asymmetric effects are more suitable for explaining the time-varying volatility in the dry bulk and tanker freight markets or whether both effects should be incorporated simultaneously. Using Baltic Exchange indices during the sample period from March 1999 to October 2011 on a daily basis, we separately analyse the impact of macroeconomic shocks and asymmetric effects on the conditional volatility of freight rates by using a GARCH-X model and an EGARCH model, respectively. Furthermore, we simultaneously investigate both effects by specifying an EGARCH-X model. Assuming not only a normal distribution but also a t-distribution in order to better capture the fat tails of error terms, three important conclusions emerge for modelling the conditional volatility of freight rates: (i) The assumption of a t-distribution is better suited than a normal distribution is. (ii) Macroeconomic factors should be incorporated into the conditional variance equation rather than into the conditional mean equation. In addition, the number of macroeconomic factors that exhibit explanatory power decreases under a t-distribution. (iii) While there seem to be no asymmetric effects in the dry bulk freight market, these effects are strongly pronounced in the tanker freight market. Our empirical findings have important implications for freight rate risk management.