Contributions in Merlin

To date, most attempts at modelling implied volatility rely on a priori assumptions about its dynamics. Machine learning methods, however, are not constrained to a pre-determined functional form. In this paper, machine learning methods are employed to forecast changes in the implied volatility of options written on S&P 500 futures. These models outperform naive predictions assessed on both standardised volatility surfaces and market quoted options, obtaining comparable performance with a benchmark deterministic model. The XGBoost model shows particularly promising results when incorporated into a minimum variance hedging strategy. In addition, the paper presents a robust adaption of the anchored eSSVI volatility surface parameterisation.

Diese Arbeit prüft die Anwendung des modifizierten Dividendenwachstumsmodells nach Balschun und Schindler (2015) zur Portfoliokonstruktion innerhalb nordischer Aktienmärkte. Dabei werden die Portfolios auf risikobereinigte Überschussrenditen untersucht, um den Nutzen des neuartigen Modells für das Portfoliomanagement zu analysieren. Das Modell unterscheidet sich durch die zugrundeliegende lineare Wachstumsannahme von dem traditionellen Dividendenwachstumsmodell (Gordon Growth Model). Die Ergebnisse zeigen, dass die Portfolios des modifizierten Dividendenwachstumsmodells, nach Abzug berechneter Transaktionskosten, über 14% annualisierte Alphas erzielen können. Auch bei einem Anstieg der Transaktionskosten oder der Portfoliogrösse bleiben die beobachteten Ergebnisse robust, jedoch zeigen sich signifikante Veränderungen bei einem abweichendem Stichprobenzeitraum oder einer Anpassung von Variablenmassen.

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In a national sample of 5087 Spaniards, we examine the prevalence of 10 specific misperceptions over five separate science and health domains (climate change, 5G technology, genetically modified foods, vaccines, and homeopathy). We find that misperceptions about genetically modified foods and general health risks of 5G technology are particularly widespread. While we find that partisan affiliation is not strongly associated with any of the misperceptions aside from climate change, we find that two distinct dimensions of an anti-elite worldview—anti-expert and conspiratorial mindsets—are better overall predictors of having science and health misperceptions in the Spanish context. These findings help extend our understanding of polarization around science beyond the most common contexts (e.g. the United States) and support recent work suggesting anti-elite sentiments are among the most important predictors of factual misperceptions.

We propose a model of basic self-regulatory mechanisms that integrates descriptive-structural and dynamic-explanatory approaches to personality. Using a structural way of thinking and based on a structural model of personality (the Circumplex of Personality Metatraits), we deduced two orthogonal (distinct) but interactive mechanisms: (a) Impulse Control responsible for controlling automatically activated impulses, urges, and affective reactions and (b) Self-Motivation responsible for regulating intentions in goal-oriented behaviors. Their operation depends on both situational and dispositional factors and optimal functioning of both mechanisms is needed for effective regulation of behavior. People can also be characterized by relatively stable levels of Impulse Control and Self-Motivation as dispositions, which depend on temperamental predispositions but can develop incoherently with them due to the impact of environmental factors. Combinations of Impulse Control and Self-Motivation as dispositions result in personality types, which differ in their adaptiveness. Importantly, Impulse Control and Self-Motivation mechanisms reveal substantial similarities to other self-regulatory constructs described in the literature, particularly those included in Block and Block's as well as Kuhl's theories. The contribution of our paper may serve as an example of how to apply the descriptive-structural approach to develop a dynamic-explanatory model of personality.

When does collective memory influence behavior? We highlight two conditions under which the memory of past events comes to matter for the present: the associative nature of memory and institutionalized acts of commemoration by the state. During World War II, German troops occupying Greece perpetrated numerous massacres. Memories of those events resurfaced during the 2009 Greek debt crisis, leading to a drop in German car sales in Greece, especially in areas affected by German reprisals. Differential economic performance did not drive this divergence. Multiple pieces of evidence suggest that current events reactivated past memories, creating a backlash against Germany. This backlash also manifested in political behavior, with vote shares of anti-German parties increasing in reprisal areas after the start of the debt crisis. Using quasi-random variation in public recognition of victim status, we show that institutionalized collective memory amplifies the effects of political conflict on economic and political behavior.

We use a quantitative economic geography model to explore subsidy competition among U.S. states. We ask what motivates state governments to subsidize firm relocations and quantify how strong their incentives are. We also characterize fully non-cooperative and cooperative subsidy choices and assess how far away we are from these extremes. We find that states have strong incentives to subsidize firm relocations in order to gain at the expense of other states. We also find that observed subsidies are closer to cooperative than non-cooperative subsidies but the potential losses from an escalation of subsidy competition are large.

The paper is devoted to the calmness from below/from above for the optimal value function of parametric optimization problems, where we are specifically interested in perturbed semi-infinite programs. A main intention is to revisit classical results and to derive refinements of them. In particular, we show in the context of semi-infinite optimization that calmness from below for j holds under quasiconvexity of the data functions and compactness of the solution set, which extends results on the lower semicontinuity of Illustrative examples are given, which demonstrate the significance of the imposed assumptions even in the case of linear and quadratic programs.

This study investigates the relationship between the motive of greed and various asset market indicators, such as trading activity and bubble formation (i.e., mispricing, overpricing, and price amplitude). We ran experimental asset markets that allowed us to measure individuals’ greed in order to create markets populated with greedy individuals and markets with non-greedy individuals. Regarding trading activity, we found that greedier individuals had higher trading activity on the individual level but not on the market level. On the market level, high-greed markets exhibited less frequent and smaller price bubbles than markets with less greedy traders. If our findings translate to actual markets, greed itself might not contribute to asset market bubbles.

We present ManuKnowVis, the result of a design study, in which we contextualize data from multiple knowledge repositories of a manufacturing process for battery modules used in electric vehicles. In data-driven analyses of manufacturing data, we observed a discrepancy between two stakeholder groups involved in serial manufacturing processes: Knowledge providers (e.g., engineers) have domain knowledge about the manufacturing process but have difficulties in implementing data-driven analyses. Knowledge consumers (e.g., data scientists) have no first-hand domain knowledge but are highly skilled in performing data-driven analyses. ManuKnowVis bridges the gap between providers and consumers and enables the creation and completion of manufacturing knowledge. We contribute a multi-stakeholder design study, where we developed ManuKnowVis in three main iterations with consumers and providers from an automotive company. The iterative development led us to a multiple linked view tool, in which, on the one hand, providers can describe and connect individual entities (e.g., stations or produced parts) of the manufacturing process based on their domain knowledge. On the other hand, consumers can leverage this enhanced data to better understand complex domain problems, thus, performing data analyses more efficiently. As such, our approach directly impacts the success of data-driven analyses from manufacturing data. To demonstrate the usefulness of our approach, we carried out a case study with seven domain experts, which demonstrates how providers can externalize their knowledge and consumers can implement data-driven analyses more efficiently.