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A key solution for public good provision is the voluntary formation of institutions that commit players to cooperate. Such institutions generate inequality if some players decide not to participate but cannot be excluded from cooperation benefits. Prior research with small groups emphasizes the role of fairness concerns with positive effects on cooperation. We show that effects do not generalize to larger groups: if group size increases, groups are less willing to form institutions generating inequality. In contrast to smaller groups, however, this does not increase the number of participating players, thereby limiting the positive impact of institution formation on cooperation.
Market risks account for an integral part of insurers' risk profiles. We explore market risk sensitivities of insurers in the United States and Europe. Based on panel regression models and daily market data from 2012 to 2018, we find that sensitivities are particularly driven by insurers' product portfolio. The influence of interest rate movements on stock returns is 60% larger for US than for European life insurers. For the former, interest rate risk is a dominant market risk with an effect that is five times larger than through corporate credit risk. For European life insurers, the sensitivity to interest rate changes is only 44% larger than toward credit default swap of government bonds, underlining the relevance of sovereign credit risk.
The SVB case is a wake-up call for Europe’s regulators as it demonstrates the destructive power of a bank-run: it undermines the role of loss absorbing capital, elbowing governments to bailout affected banks. Many types of bank management weaknesses, like excessive duration risk, may raise concerns of bank losses – but to serve as a run-trigger, there needs to be a large enough group of bank depositors that fails to be fully covered by a deposit insurance scheme. Latent run-risk is the root cause of inefficient liquidations, and we argue that a run on SVB assets could have been avoided altogether by a more thoughtful deposit insurance scheme, sharply distinguishing between loss absorbing capital (equity plus bail-in debt) and other liabilities which are deemed not to be bail-inable, namely demand deposits. These evidence-based insights have direct implications for Europe’s banking regulation, suggesting a minimum and a maximum for a banks’ loss absorption capacity.
This cumulative dissertation contains four self-contained chapters on stochastic games and learning in intertemporal choice.
Chapter 1 presents an experiment on value learning in a setting where actions have both immediate and delayed consequences. Subjects make a series of choices between abstract options, with values that have to be learned by sampling. Each option is associated with two payoff components: One is revealed immediately after the choice, the other with one round delay. Objectively, both payoff components are equally important, but most subjects systematically underreact to the delayed consequences. The resulting behavior appears impatient or myopic. However, there is no inherent reason to discount: All rewards are paid simultaneously, after the experiment. Elicited beliefs on the value of options are in accordance with choice behavior. These results demonstrate that revealed impatience may arise from frictions in learning, and that discounting does not necessarily reflect deep time preferences. In a treatment variation, subjects first learn passively from the evidence generated by others, before then making a series of own choices. Here, the underweighting of delayed consequences is attenuated, in particular for the earliest own decisions. Active decision making thus seems to play an important role in the emergence of the observed bias.
Chapter 2 introduces and proves existence of Markov quantal response equilibrium (QRE), an application of QRE to finite discounted stochastic games. We then study a specific case, logit Markov QRE, which arises when players react to total discounted payoffs using the logit choice rule with precision parameter λ. We show that the set of logit Markov QRE always contains a smooth path that leads from the unique QRE at λ = 0 to a stationary equilibrium of the game as λ goes to infinity. Following this path allows to solve arbitrary finite discounted stochastic games numerically; an implementation of this algorithm is publicly available as part of the package sgamesolver. We further show that all logit Markov QRE are ε-equilibria, with a bound for ε that is independent of the payoff function of the game and decreases hyperbolically in λ. Finally, we establish a link to reinforcement learning, by characterizing logit Markov QRE as the stationary points of a game dynamic that arises when all players follow the well-established reinforcement learning algorithm expected SARSA.
Chapter 3 introduces the logarithmic stochastic tracing procedure, a homotopy method to compute stationary equilibria for finite and discounted stochastic games. We build on the linear stochastic tracing procedure (Herings and Peeters 2004), but introduce logarithmic penalty terms as a regularization device, which brings two major improvements. First, the scope of the method is extended: it now has a convergence guarantee for all games of this class, rather than just generic ones. Second, by ensuring a smooth and interior solution path, computational performance is increased significantly. A ready-to-use implementation is publicly available. As demonstrated here, its speed compares quite favorable to other available algorithms, and it allows to solve games of considerable size in reasonable times. Because the method involves the gradual transformation of a prior into equilibrium strategies, it is possible to search the prior space and uncover potentially multiple equilibria and their respective basins of attraction. This also connects the method to established theory of equilibrium selection.
Chapter 4 introduces sgamesolver, a python package that uses the homotopy method to compute stationary equilibria of finite discounted stochastic games. A short user guide is complemented with discussion of the homotopy method, the two implemented homotopy functions logit Markov QRE and logarithmic tracing, and the predictor-corrector procedure and its implementation in sgamesolver. Basic and advanced use cases are demonstrated using several example games. Finally, we discuss the topic of symmetries in stochastic games.
We examine whether the uncertainty related to environmental, social, and governance (ESG) regulation developments is reflected in asset prices. We proxy the sensitivity of firms to ESG regulation uncertainty by the disparity across the components of their ESG ratings. Firms with high ESG disparity have a higher option-implied cost of protection against downside tail risk. The impact of the misalignment across the different dimensions of the ESG score is distinct from that of ESG score level itself. Aggregate downside risk bears a negative price for firms with low ESG disparity.
In this study, we introduce a novel entity matching (EM) framework. It com-bines state-of-the-art EM approaches based on Artificial Neural Networks (ANN) with a new similarity encoding derived from matching techniques that are preva-lent in finance and economics. Our framework is on-par or outperforms alternative end-to-end frameworks in standard benchmark cases. Because similarity encod-ing is constructed using (edit) distances instead of semantic similarities, it avoids out-of-vocabulary problems when matching dirty data. We highlight this property by applying an EM application to dirty financial firm-level data extracted from historical archives.
We study the interplay of capital and liquidity regulation in a general equilibrium setting by focusing on future funding risks. The model consists of a banking sector with long-term illiquid investment opportunities that need to be financed by shortterm debt and by issuing equity. Reliance on refinancing long-term investment in the middle of the life-time is risky, since the next generation of potential short-term debt holders may not be willing to provide funding when the return prospects on the long-term investment turn out to be bad. For moderate return risk, equilibria with and without bank default coexist, and bank default is a self-fulfilling prophecy. Capital and liquidity regulation can prevent bank default and may implement the first-best. Yet the former is more powerful in ruling out undesirable equilibria and thus dominates liquidity regulation. Adding liquidity regulation to optimal capital regulation is redundant.
Gradient capital allocation, also known as Euler allocation, is a technique used to redistribute diversified capital requirements among different segments of a portfolio. The method is commonly employed to identify dominant risks, assessing the risk-adjusted profitability of segments, and installing limit systems. However, capital allocation can be misleading in all these applications because it only accounts for the current portfolio composition and ignores how diversification effects may change with a portfolio restructuring. This paper proposes enhancing the gradient capital allocation by adding “orthogonal convexity scenarios” (OCS). OCS identify risk concentrations that potentially drive portfolio risk and become relevant after restructuring. OCS have strong ties with principal component analysis (PCA), but they are a more general concept and compatible with common empirical patterns of risk drivers being fat-tailed and increasingly dependent in market downturns. We illustrate possible applications of OCS in terms of risk communication and risk limits.
In recent years, European regulators have debated restricting the time an online tracker can track a user to protect consumer privacy better. Despite the significance of these debates, there has been a noticeable absence of any comprehensive cost-benefit analysis. This article fills this gap on the cost side by suggesting an approach to estimate the economic consequences of lifetime restrictions on cookies for publishers. The empirical study on cookies of 54,127 users who received ∼128 million ad impressions over ∼2.5 years yields an average cookie lifetime of 279 days, with an average value of €2.52 per cookie. Only ∼13 % of all cookies increase their daily value over time, but their average value is about four times larger than the average value of all cookies. Restricting cookies’ lifetime to one year (two years) could potentially decrease their lifetime value by ∼25 % (∼19 %), which represents a potential decrease in the value of all cookies of ∼9 % (∼5%). Most cookies, however, would not be affected by lifetime restrictions of 12 or 24 months as 72 % (85 %) of the users delete their cookies within 12 (24) months. In light of the €10.60 billion cookie-based display ad revenue in Europe, such restrictions would endanger €904 million (€576 million) annually, equivalent to €2.08 (€1.33) per EU internet user. The article discusses these results' marketing strategy challenges and opportunities for advertisers and publishers.