Behavioral Economics: Past, Present, Future

Behavioral economics increases the explanatory power of economics by providing it with more realistic psychological foundations. This book consists of representative recent articles in behavioral economics. This chapter is intended to provide an introduction to the approach and methods of behavioral economics, and to some of its major findings, applications, and promising new directions. It also seeks to fill some unavoidable gaps in the chapters’ coverage of topics

Improving Data Quality, Model Functionalities and Optimizing User Interfaces in Decision Support Systems

This dissertation contributes to the research on three core elements of decision support systems for managers and consumers: data management, model management and user interface. With respect to data management this dissertation proposes an approach for reducing unobserved product heterogeneity in online transaction data sets. The example of an online auction data set is used to investigate the approach’s ability to improve data quality. In the area of model management this dissertation contributes an approach to elicit consumer product preferences for exponential (beside linear) utility functions aiming at predicting consumers’ utilities and willingness-to-pay for individual products. The question which utility function (linear or exponential) is better suited for predicting product utilities and the willingness to pay is evaluated using a laboratory experiment. Further, in the area of user interfaces this dissertation deals with information visualization. Focusing on coordinate systems, a laboratory experiment is used to investigate which visualization format (two or three dimensional) is better suited for supporting simple vs. complex decision making scenarios and which criteria matter when choosing a visualization format for a particular level of decision making complexity

Longevity Risk and Capital Markets: The 2018-19 Update

This Special Issue of the Annals of Actuarial Science contains 12 contributions to the academic literature all dealing with longevity risk and capital markets. Draft versions of the papers were presented at Longevity 14: The Fourteenth International Longevity Risk and Capital Markets Solutions Conference that was held in Amsterdam on 20-21 September 2018. It was hosted by the Pensions Institute at Cass Business School and the Netspar Network for Studies on Pensions, Ageing and Retirement

Essays in asset pricing

My dissertation consists of three chapters, each of which focuses on a different area of research in asset pricing. The first chapter's focal point is the measurement of the premium for jump risks in index option markets. The second chapter is devoted to non- parametric measurement of pricing kernel dispersion. The third chapter contributes to the literature on latent state variable recovery in option pricing models. In the first chapter, "Big risk", I show how to replicate a large family of high-frequency measures of realised return variation using dynamically rebalanced option portfolios. With this technology investors can generate optimal hedging payoffs for realised variance and several measures of realised jump variation in incomplete option markets. These trading strategies induce excess payoffs that are direct compensation for second- and higher order risk exposure in the market for (index) options. Sample averages of these excess payoffs are natural estimates of risk premia associated with second- and higher order risk exposures. In an application to the market for short-maturity European options on the S&P500 index, I obtain new important evidence about the pricing of variance and jump risk. I find that the variance risk premium is positive during daytime, when the hedging frequency is high enough, and negative during night-time. Similarly, for an investor taking long variance positions, daytime profits are grater in absolute value than night-time losses. Compensation for big risk is mostly available overnight. The premium for jump skewness risk is positive, while the premium for jump quarticity is negative (contrary to variance, also during the trading day). The risk premium for big risk is concentrated in states with large recent big risk realisations. In the second chapter, "Arbitrage free dispersion", co-authored with Andras Sali and Fabio Trojani, we develop a theory of arbitrage-free dispersion (AFD) which allows for direct insights into the dependence structure of the pricing kernel and stock returns, and which characterizes the testable restrictions of asset pricing models. Arbitrage-free dispersion arises as a consequence of Jensen's inequality and the convexity of the cumulant generating function of the pricing kernel and returns. It implies a wide family of model-free dispersion constraints, which extend the existing literature on dispersion and co-dispersion bounds. The new techniques are applicable within a unifying approach in multivariate and multiperiod settings. In an empirical application, we find that the dispersion of stationary and martingale pricing kernel components in a benchmark long-run risk model yields a counterfactual dependence of short- vs. long- maturity bond returns and is insufficient for pricing optimal portfolios of market equity and short-term bonds. In the third chapter, "State recovery from option data through variation swap rates in the presence of unspanned skewness", I show that a certain class of variance and skew swaps can be thought of as sufficient statistics of the implied volatility surface in the context of uncovering the conditional dynamics of second and third moments of index returns. I interpret the slope of the Cumulant Generating Function of index returns in the context of tradable swap contracts, which nest the standard variance swap, and share its fundamental linear pricing property in the class of Affine Jump Diffusion models. Equipped with variance- and skew-pricing contracts, I investigate the performance of a range of state variable filtering setups in the context of the stylized facts uncovered by the recent empirical option pricing literature, which underlines the importance of decoupling the drivers of stochastic volatility from those of stochastic (jump) skewness. The linear pricing structure of the contracts allows for an exact evaluation of the impact of state variables on the observed prices. This simple pricing structure allows me to design improved low-dimensional state-space filtering setups for estimating AJD models. In a simulated setting, I show that in the presence of unspanned skewness, a simple filtering setup which includes only prices of skew and variance swaps offers significant improvements over a high-dimensional filter which treats all observed option prices as observable inputs

Information integration in perceptual and value-based decisions

Research on the psychology and neuroscience of simple, evidence-based choices has led to an impressive progress in capturing the underlying mental processes as optimal mechanisms that make the fastest decision for a specified accuracy. The idea that decision-making is an optimal process stands in contrast with findings in more complex, motivation-based decisions, focussed on multiple goals with trade-offs. Here, a number of paradoxical and puzzling choice behaviours have been revealed, posing a serious challenge to the development of a unified theory of choice. These choice anomalies have been traditionally attributed to oddities at the representation of values and little is known about the role of the process under which information is integrated towards a decision. In a series of experiments, by controlling the temporal distribution of the decision-relevant information (i.e., sensory evidence or value), I demonstrate that the characteristics of this process cause many puzzling choice paradoxes, such as temporal, risk and framing biases, as well as preference reversal. In Chapter 3, I show that information integration is characterized by temporal biases (Experimental Studies 1-2, Computational Studies 1-3). In Chapter 4, I examine the way the integration process is affected by the immediate decision context (Experimental Studies 3-4, Computational Study 4), demonstrating that prior to integration, the momentary ranking of a sample modifies its magnitude. This principle is further scrutinized in Chapter 5, where a rank-dependent accumulation model is developed (Computational Study 5). The rank-dependent model is shown to underlie preference reversal in multi-attribute choice problems and to predict that choice is sensitive, not only to the mean strength of the information, but also to its variance, favouring riskier options (Computational Study 6). This prediction is further confirmed in Chapter 6, in a number of experiments (Experimental Studies 5-7) while the direction of risk preferences is found to be modulated by the cognitive perspective induced by the task framing (Experimental Study 8). I conclude that choice arises from a deliberative process which gathers samples of decision-relevant information, weighs them according to their salience and subsequently accumulates them. The salience of a sample is determined by i) its temporal order and ii) its local ranking in the decision context, while the direction of the weighting is controlled by the task framing. The implications of this simple, microprocess model are discussed with respect to choice optimality while directions for future research, towards the development of a unified theory of choice, are suggested