Contributions published at Quantitative Business Administration

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Global warming might push elements of the climate system past tipping points, leading to irreversible impacts. Most analyses of the costs of climate change only allow for simple and scientifically unrealistic representations of climate tipping points, ignoring their differences with respect to likelihood, transitional timescales, and impacts. Furthermore, there are multiple tipping points that interact. In the worst-case scenario, tipping one element could trigger a cascade of others. We incorporate a multidimensional system of climate tipping points into the stochastic integrated assessment model DSICE to study the optimal management of such an interacting system and the risks it poses.

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This paper presents a new method for the analysis of moral hazard principal-agent problems. The new approach avoids the stringent assumptions on the distribution of outcomes made by the classical first-order approach and instead only requires the agent's expected utility to be a rational function of the action. This assumption allows for a reformulation of the agent's utility maximization problem as an equivalent system of equations and inequalities. This reformulation in turn transforms the principal's utility maximization problem into a nonlinear program. Under the additional assumptions that the principal's expected utility is a polynomial and the agent's expected utility is rational in the wage, the final nonlinear program can be solved to global optimality. The paper also shows how to first approximate expected utility functions that are not rational by polynomials, so that the polynomial optimization approach can be applied to compute an approximate solution to non-polynomial problems. Finally, the paper demonstrates that the polynomial optimization approach extends to principal-agent models with multi-dimensional action sets.

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Many assets derive their value not only from future cash flows but also from their ability to serve as collateral. In this paper, we investigate this collateral premium and its impact on asset returns in an infinite-horizon general equilibrium model with heterogeneous agents facing col- lateral constraints for borrowing. We document that borrowing against collateral substantially increases the return volatility of long-lived assets. Moreover, otherwise identical assets with different degrees of collateralizability exhibit substantially different return dynamics because their prices contain a sizable collateral premium that varies over time. This premium can be positive even for assets that never pay dividends.

An infinite-horizon asset-pricing model with heterogeneous agents and collateral constraints can explain why adjustments in stock market margins under US Regulation T had an economically insignificant impact on market volatility. In the model, raising the margin requirement for one asset class may barely affect its volatility if investors have access to another, unregulated class of collateralizable assets. Through spillovers, however, the volatility of the other asset class may substantially decrease. A very strong dampening effect on all assets׳ return volatilities can be achieved by a countercyclical regulation of all markets.

Most current cost-benefit analyses of climate change policies suggest an optimal global climate policy that is significantly less stringent than the level required to meet the internationally agreed 2 °C target. This is partly because the sum of estimated economic damage of climate change across various sectors, such as energy use and changes in agricultural production, results in only a small economic loss or even a small economic gain in the gross world product under predicted levels of climate change. However, those cost-benefit analyses rarely take account of environmental tipping points leading to abrupt and irreversible impacts on market and nonmarket goods and services, including those provided by the climate and by ecosystems. Here we show that including environmental tipping point impacts in a stochastic dynamic integrated assessment model profoundly alters cost-benefit assessment of global climate policy. The risk of a tipping point, even if it only has nonmarket impacts, could substantially increase the present optimal carbon tax. For example, a risk of only 5% loss in nonmarket goods that occurs with a 5% annual probability at 4 °C increase of the global surface temperature causes an immediate two-thirds increase in optimal carbon tax. If the tipping point also has a 5% impact on market goods, the optimal carbon tax increases by more than a factor of 3. Hence existing cost-benefit assessments of global climate policy may be significantly underestimating the needs for controlling climate change.

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This thesis compiles three papers on different topics in computational economics: First, we present a new method to recursively integrate expectations over serially correlated latent variables with continuous support in maximum likelihood estimations, using highly efficient quadrature rules and interpolation; we apply the method to the dynamic discrete choice model of Rust (1987). Second, we present a method to use the constrained optimization approach to the estimation of dynamic models (MPEC; Su and Judd, 2012) in conjunction with grid adaption for state variables with continuous support; we use grid adaption by node movement, and derive sufficient optimality conditions for the underlying function approximation from the equioscillation theorem, which enables us to solve the estimation and the approximation problem simultaneously. Third, we present a simulation study using agent-based modelling to investigate whether a model of monopolistic competition can reach its equilibrium without common knowledge of aggregate demand and sophisticated utility optimization capabilities of the agents.