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|Title||Risk and Return Replication of Trend Following Strategies|
|Institution||University of Zurich|
|Faculty||Faculty of Business, Economics and Informatics|
|Number of Pages||65|
|Abstract Text||This thesis aims to explore and develop approaches to infer the asset positions of managed futures funds and to further replicate their risk proles. The approaches studied can be cat- egorized into two classes, namely a regression-based, top-down approach, and a trend signal- based, bottom-up approach. The replication problem for return time series is of general theoretical and practical interest. This requires, in particular, the specication of trading models and risk models to determine how the daily positions are adjusted based on market prices, risk and diversication indicat- ors. On the one hand, our top-down approach aims to replicate a given daily return series within a pre-specied investment universe by using regression methods to estimate position weights of individual instruments. The idea is to regress the time series of the strategy's returns against a collection of the returns of the pre-dened investment universe instruments by employing dierent rolling regressions. Then one can assess the results of varying regression methods and choose the optimal model based on the robustness of the estimators. On the other hand, our bottom-up approach aims to construct a generic trend following strategy that captures the return and risk characteristics of the same benchmark. The method consists of three components. The rst step concerns the trend signal generation using lter techniques. The second step is portfolio construction using a risk budgeting approach. Finally, the last step applies volatility targeting for each asset class and the entire portfolio. Similar to the rst approach, there are a few alternative models available, and the parameters which best feature the performance of the given trend following strategy would be chosen as the optimal one.|