We first investigate the computational complexity for estimating quantile based risk measures, such as the widespread Value at Risk for banks and Solvency II capital requirements for insurance companies, via nested Monte Carlo simulations. The estimator is a conditional expectation type estimate where two stage simulations are required to evaluate the risk measure: an outer simulation is used to generate risk factor scenarios that govern price movements and an inner simulation is used to evaluate the future portfolio value based on each of those scenarios. The second essay considers the financial stability from a macro perspective. Measuring negative externalities of banks is a major challenge for financial regulators. We propose a new risk management approach to enhance the financial stability and to increase the fairness of financial transactions. The basic idea is that a bank should assume as much risk as it creates. Any imbalance in the tails of the distribution of profit and losses is a sign of the bank's failure to internalize its externalities or the social costs associated with its activities. The aim of the third essay is to find a theoretical justification toward the mutual benefits for members of a bonking union in the context of a strategic interaction model. We use a unique contagion dynamic that marries the rich literature of game theory, contagion in pandemic crisis and the study of collaboration between regulators. The model is focused toward regulating asset classes, not individual banks. This special design addresses moral hazard issues that could result from government intervention in the case of crisis.