Contributions published at Quantitative Business Administration

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Smart beta exchange traded funds, also known as strategic or alternative beta ETFs, have been a hot topic in financial press in recent years. Broadly defined, these funds track indices that are not based upon market capitalization weighting or screening scheme. They have gotten very popular in recent years and everyone is trying to explain this trend. Surely, as one of the reasons for this recent gained popularity is the claim of smart beta ETF managers, that their products can outperform traditional ETFs at the market. This paper analyses this hypothesis by testing the performance of 38 US domiciled alternatively weighted ETFs and indices. The analysis with done on three different regressions: l) Smart Beta ETFs versus Benchmark Indices 2) Smart Beta ETFs versus Benchmark Indices ETFs 3) Smart Beta Indices versus Benchmark Indices The paper didn't decide on which benchmarks should be taken for the analysis, the market capitalization weighted benchmark indices are taken from smart beta ETFs' fact sheets. By assessing the performance of the 38 smart beta funds over market capitalization weighted indices and traditional ETFs, this paper cannot find evidence of smart beta's outperformance. Only one alternatively weighted fund outperformed its self-declared market capitalization weighted benchmark index. Due to high rebalancing costs, tracking an alternatively weighted index gets more costly than tracking a traditional one. Therefore this paper also assessed the performances of smart beta indices over market capitalization indices and found again no evidence of outperformance. Since the inception date of the funds, none of the index achieved an outperformance over its market capitalization weighted benchmark index. All the regressions were also run on different business cycles without finding different results. However, by expanding the time horizon to the last 15 years, an outperforrnance of alternatively weighted indices over their cap-weighted benchmark indices was detected. More than the half of the analyzed smart beta indices achieved positive alpha over their benchmarks. This implies that an outperformance of alternatively weighted strategies over market capitalization weighted strategies is possible in the longer run. However, this paper doesn't find evidence that this outperformance can be carried on ETFs that track these indices, which in contrary to an index, can be bought and sold. By expanding the analysis and by adding additional risk factors, the paper concludes that the most smart beta ETFs load positively on the common factors such as size, value and momentum. The risk-adjusted alpha is however not different from zero. Besides the analysis of outperformance, this paper finds evidence that the total expense ratio is a negative indicator for performance. In contrary to logical beliefs, the cheaper smart beta ETFs are associated with higher performance than the more expensive ones.

One of the major potential consequences of climate change is damage to earth’s ecosystems, damage which could manifest itself in the form of tipping risks. We establish an economic growth model of ecosystem tipping risks, set in the context of possible forest dieback. We consider different specifications of impacts arising from the forest dieback tipping point, specifications such as changes in the system dynamics of the forests, changes in the forest mass, and impacts on economic output. We also consider endogenous and exogenous tipping point probabilities. For each specification we compute the optimal policies for forest management and emission control. Our results show qualitative differences in patterns of post-tipping event, optimal forest harvest, and either precautionary or aggressive pre-tipping event harvest patterns, a feature consistent with the findings of the existing literature. Optimal control of deforestation and carbon dioxide emission reduction also exhibits varied patterns of post- and pre-tipping levels depending on the nature of the tipping risk. Still, today’s optimal policy is one of more stringent emissions control in presence of a potential forest dieback tipping point.

Theoretically optimal portfolios based on the mean-variance framework often show poor out-of-sample performances. This is mainly due to errors occurring in the estimation of the input parameters to the portfolio optimization, i.e. the means, variances and covariances of the asset returns. Since especially the mean estimator proofs to be particularly error prone, some of the most promising approaches in terms of out-of-sample performance improvement can be found in models including an enhanced estimation of the covariance matrix for minimum variance portfolios. This thesis focuses on the findings that weight constraints can improve the stability and performance of minimum variance portfolios. It presents and tests a model introduced by Behr et al. (2013) that combines shrinkage properties of weight constraints described by Jagannathan and Ma (2003) with the shrinkage theory of Ledoit and Wolf (2003, 2004), in order to find optimal weight constraints. The thesis investigates the impact of these optimized constraints on the out-of-sample performance of equity portfolios, and tests the model against alternative portfolio strategies. Furthermore the impact of heuristic and optimized weight constraints on the variance-covariance characteristics of asset returns is tested by assessing the implicit beliefs inherent in the constrained portfolio optimization problem. The results show that the tightness of constraints imposed by the model of Behr et al. (2013) is dependent on the number of assets included in the investment universe. While the constraints are loose for low numbers of assets, they impose a strong structure for larger portfolios, especially if the available amount of return observations is limited. The resulting portfolios yield lower out-of-sample variances and higher out-of-sample Sharpe ratios than the equally weighted portfolio and minimum variance portfolios including none or heuristic weight constraints on average, but do not consistently outperform each of the portfolio strategies for the individual data sets.

This paper presents an analysis of the higher-order dynamics of key financial quantities in asset-pricing models with recursive preferences. For this purpose, we first describe a projection-based algorithm for solving such models. The method outperforms common methods like discretization and log-linearization in terms of effciency and accuracy. Our algorithm allows us to document the presence of strong nonlinear effects in the modern long-run risks models which cannot be captured by the common methods. For example, for a prominent recent calibration of a popular long-run risks model, the log-linearization approach overstates the equity premium by 100 basis points or 22.5%. The increasing complexity of state-of-the-art asset-pricing models leads to complex nonlinear equilibrium functions with considerable curvature which in turn have sizable economic implications. Therefore, these models require numerical solution methods, such as the projection methods presented in this paper, that can adequately describe the higher- order equilibrium features.

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