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Institute
Over the last decades, commodity futures markets have grown significantly because, in addition to hedgers using futures for risk management, investors have discovered the potential of futures in investment products. Motivated by this growing importance, this thesis is concerned with the analysis of risk (in terms of the established risk measure Expected Shortfall) and investment strategies in commodity futures markets.
First, we compare popular non-parametric estimators of Expected Shortfall (i. e., different variants of historical, outlier-adjusted and kernel methods) to each other, selected parametric benchmarks and estimates based on the idea of forecast combination within a multidimensional simulation setup (spanned by different distributional settings, sample sizes and confidence levels). We rank the estimators on the basis of classic error measures as well as an innovative performance profile technique, which we adapt from the mathematical programming literature. Our rich set of results supports academics and practitioners in the search for an answer to the question of which estimators are preferable under which circumstances.
After that, we present a full-scale analysis of Expected Shortfall in commodity futures markets. Besides illustrating the dynamics of historic Expected Shortfall, we evaluate whether popular estimators are suitable for forecasting future Expected Shortfall. By implementing a new backtest, we find that the performance of estimators hinges on market stability. Estimators tend to fail when markets are in turmoil and accurate forecasts are urgently needed. Even though a kernel method performs best on average, our results advise against the use of established estimators for risk (and margin) prediction.
Third, motivated by the deteriorating performance of traditional cross-sectional momentum strategies in commodity futures markets, we propose to resurrect momentum by incorporating autocorrelation information into the asset selection process. Put differently, we introduce measures of short and long memory (variance ratios and Hurst coefficients, respectively) telling us whether past winners and losers are likely to persist or not. Our empirical findings suggest that a memory-enhanced momentum strategy based on variance ratios significantly outperforms traditional momentum in terms of reward and risk, effectively prevents momentum crashes and is not bound to the movement of the overall commodity market. Furthermore, strategy returns cannot be explained by typical factor portfolios and macroeconomic variables and are robust to various parametrization choices, alternative data sets, transaction costs and data mining. Finally, and in contrast to a newly emerging strand of literature promoting the benefits of long memory measures in portfolio management, we show that Hurst coefficients do not carry investment-relevant information in a commodity momentum context.