A neural biomarker of psychological vulnerability to future life stress.

We all experience a host of common life stressors such as the death of a family member, medical illness, and financial uncertainty. While most of us are resilient to such stressors, continuing to function normally, for a subset of individuals, experiencing these stressors increases the likelihood of developing treatment-resistant, chronic psychological problems, including depression and anxiety. It is thus paramount to identify predictive markers of risk, particularly those reflecting fundamental biological processes that can be targets for intervention and prevention. Using data from a longitudinal study of 340 healthy young adults, we demonstrate that individual differences in threat-related amygdala reactivity predict psychological vulnerability to life stress occurring as much as 1 to 4 years later. These results highlight a readily assayed biomarker, threat-related amygdala reactivity, which predicts psychological vulnerability to commonly experienced stressors and represents a discrete target for intervention and prevention.

Optimal Capital Requirements in Financial Networks with Fire Sales

I explore and analyze a problem of finding the socially optimal capital requirements for financial institutions considering two distinct channels of contagion: direct exposures among the institutions, as represented by a network and fire sales externalities, which reflect the negative price impact of massive liquidation of assets.These two channels amplify shocks from individual financial institutions to the financial system as a whole and thus increase the risk of joint defaults amongst the interconnected financial institutions; this is often referred to as systemic risk. In the model, there is a trade-off between reducing systemic risk and raising the capital requirements of the financial institutions. The policymaker considers this trade-off and determines the optimal capital requirements for individual financial institutions. I provide a method for finding and analyzing the optimal capital requirements that can be applied to arbitrary network structures and arbitrary distributions of investment returns.

In particular, I first consider a network model consisting only of direct exposures and show that the optimal capital requirements can be found by solving a stochastic linear programming problem. I then extend the analysis to financial networks with default costs and show the optimal capital requirements can be found by solving a stochastic mixed integer programming problem. The computational complexity of this problem poses a challenge, and I develop an iterative algorithm that can be efficiently executed. I show that the iterative algorithm leads to solutions that are nearly optimal by comparing it with lower bounds based on a dual approach. I also show that the iterative algorithm converges to the optimal solution.

Finally, I incorporate fire sales externalities into the model. In particular, I am able to extend the analysis of systemic risk and the optimal capital requirements with a single illiquid asset to a model with multiple illiquid assets. The model with multiple illiquid assets incorporates liquidation rules used by the banks. I provide an optimization formulation whose solution provides the equilibrium payments for a given liquidation rule.

I further show that the socially optimal capital problem using the ``socially optimal liquidation" and prioritized liquidation rules can be formulated as a convex and convex mixed integer problem, respectively. Finally, I illustrate the results of the methodology on numerical examples and

discuss some implications for capital regulation policy and stress testing.