Contributions published at Financial Economics (Thorsten Hens)

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Conventionally, agent-based models are specified in a combination of natural language and mathematical terms, and their implementation seen as an afterthought. I challenge this view and argue that it is the source code that represents the model best, with natural language and mathematical descriptions serving as documentation. This modeling paradigm is inspired by agile software development and adopting it leads to various - mostly beneficial - consequences. First, discrepancies between the specification documents and what the model actually does are eliminated by definition as the code becomes the specification. Second, replicability is greatly improved. Third, object-oriented programming is recognized as an integral part of a modeler?s skill set. Forth, tools and methods from software engineering can support the modeling process, making it more agile. Fifth, increased modularity allows to better manage complexity and enables the collaborative construction of large models. Sixth, the way models are published needs to be reconsidered, with source code ideally being part of the peer review. Seventh, the quality of source code in science is improved as it enjoys more importance, attention and scrutiny.

We propose a numerical optimization approach that can be used to solve portfolio selection problems including several assets and involving objective functions from cumulative prospect theory (CPT). Implementing the suggested algorithm, we compare asset allocations that are derived for CPT based on two different methods: maximizing CPT along the mean–variance efficient frontier so that simple mean–variance algorithms can be used, and maximizing CPT without this restriction. According to the theoretical literature, with normally distributed returns and unlimited short sales, these two approaches lead to the same optimal solutions. We find that for empirical finite discrete distributions obtained via sampling and subsequent clustering from a normal distribution, the difference between the two approaches remains negligible even if short sales are restricted. However, if standard asset allocation data for pension funds is considered, the difference is considerable. Moreover, for certain types of derivatives, such as call options, the restriction of asset allocations to the mean–variance efficient frontier produces sizable losses in various respects, including decreases in expected returns and expected utility. We are able to explain these differences by CPT’s preference for positive skewness, which is not accounted for by optimizing CPT along the mean–variance efficient frontier.

Identifying economic regimes is useful in a world of time-varying risk premia. We apply regime switching models to common factors proxying for the macroeconomic regime and show that the ensuing regime factor is relevant in forecasting the equity risk premium. Moreover, the relevance of this regime factor is preserved in the presence of fundamental variables and technical indicators which are known to predict equity risk premia. Based on multiple predictive regressions and pooled forecasts, the macroeconomic regime factor is deemed complementary relative to the fundamental and technical information sets. Finally, these forecasts exhibit significant out-of-sample predictability that ultimately translates into considerable utility gains in a mean-variance portfolio strategy.

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Bitcoin, Blockchain, Regulation

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In der Finanzwissenschaft bildet das Capital Asset Pricing Model (CAPM) von Sharpe (1964), Lintner (1965) und Mossin (1966) ein zentrales Modell. Das CAPM beschreibt den Zusammenhang zwischen erwarteter Rendite und Marktrisiko einer Aktie. Eine Implikation des Modells ist, dass die erwartete Rendite einer Aktie linear mit ihrem Marktrisiko, dem Beta, steigt. Fama und French (1992) untersuchten die Reliabilität des CAPM in ihrer Arbeit mittels einer empirischen Untersuchung. Dabei konnten sie lediglich einen schwachen Zusammenhang zwischen erwarteter Rendite und Marktrisiko nachweisen. Mit dieser Studie haben sie ein breites Echo in den Finanzwissenschaften ausgelöst. Seither bestehen Zweifel, ob der vom CAPM implizierte Zusammenhang in der Praxis wirklich Gültigkeit besitzt. In einer aktuellen Studie haben Antoniou, Doukas und Subrahmanyam (2016) mittels Daten des US-Aktienmarktes zeigen können, dass die Steigung der Wertpapiermarktlinie von der Stimmungslage der Investoren abhängt. Stimmungen werden basierend auf dem Sentiment Index von Baker und Wurgler (2006) als optimistisch oder pessimistisch charakterisiert. Während optimistischer Perioden sorgen unerfahrene Investoren für eine steigende Nachfrage nach Aktien mit hohem Marktrisiko, was zu steigenden Preisen und fallenden erwarteten Renditen führt. In diesen positiven Stimmungslagen ist die Steigung der Wertpapiermarktlinie entgegen den Implikationen des CAPM negativ. In Phasen mit pessimistischer Stimmungslage bleiben unerfahrene Investoren hingegen dem Aktienmarkt fern, sodass Aktien in diesen Perioden gemäss dem CAPM gepreist werden und die Relation zwischen erwarteter Rendite und Marktrisiko positiv ist. In der vorliegenden Arbeit wird die Studie „Investor Sentiment, Beta, and the Cost of Equity Capital“ von Antoniou, Doukas und Subrahmanyam (2016) im Detail vorgestellt und mittels einer empirischen Untersuchung analysiert, ob die Steigung der Wertpapiermarktlinie für den europäischen Aktienmarkt ebenfalls von der Stimmung der Investoren abhängt. Dafür werden zuerst in Form einer Literaturübersicht wesentliche Aspekte des CAPM diskutiert. In diesem Abschnitt werden die Annahmen des CAPM untersucht, eine kurze Herleitung vorgenommen, die Schwächen des Modells diskutiert und Widerlegungen bekannter Wissenschaftler präsentiert. Gleichzeitig werden Bezüge der zu beschreibenden Studie zur aktuellen Literatur dargestellt.

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Previous Currency Carry Trade (CCT) studies have looked at short-term trades, which are rebalanced every month. In this paper, we build on the claim that duration is a significant factor in CCT strategies. Given that duration risk is directly linked to monetary policy, we examine the predictability of monetary policy decisions in a selection of emerging markets. We focus on the Taylor rule (1993) as a monetary policy response, which became a popular tool for implementing an inflation targeting framework following the emerging market crisis of the 1990s. We examine the Taylor fundamentals for ten emerging market inflation-targeting countries using a Vector Autoregression and Forecast Combination framework. We assess the accuracy of interest rate predictions using dynamic regressions on the Taylor fundamental variables. Finally, we use these results to develop a Duration Carry Trade strategy, by predicting future interest rate changes. We use these signals to determine the duration of the carry instrument to invest in. The Duration Carry Trade strategy yields slightly higher returns, on average, compared to a simple CCT strategy, and a positive Sharpe Ratio. However, the returns are not statistically significant and the volatility of returns is approximately 50% higher than the benchmark.

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