This dissertation is composed of three chapters on financial economics. The first chapterassesses the relationship between optimal deposit insurance financing and banking fragility. The second chapter examines how expansionary monetary policy affects financial sector wages. The third chapter evaluates the performance of multiple econometric models in estimating the covariance matrix of returns in the context of portfolio choice. Chapter 1 present a quantitative model of deposit insurance. We characterize the policymaker’soptimal choices of coverage for depositors and premiums raised from banks. Premiums contribute to a deposit insurance fund that lowers taxpayers’ resolution cost of bank failures. We find that risk-adjusted premiums reduce moral hazard, enabling the policymaker to increase deposit insurance coverage by 3 percentage points and decrease the share of expected annual bank failures from 0.66% to 0.16%. The model predicts a fund-to-covered-deposits ratio that matches the data and declines in taxpayers’ income due to taxpayers’ risk aversion. Chapter 2 investigates the relation between monetary growth and compensation in thefinancial industry since the end of the Bretton Woods system. Estimating local projections, we find that the growth of the monetary base positively associates with a higher differential between financial and average wages. Our findings indicate that the effects are short lived, lending support to the temporary non-neutrality of money argued by David Hume and against the more permanent non-neutrality argued by Richard Cantillon. Our results help clarify debates on the non-neutrality of money going back to the eighteenth century. Chapter 3 investigates whether sophisticated volatility estimation improves the out-of-sampleperformance of mean-variance portfolio strategies relative to the naive 1/N strategy. The portfolio strategies rely solely upon second moments. Using a diverse group of portfolios and econometric models across multiple datasets, most models achieve higher Sharpe ratios and lower portfolio volatility that are statistically and economically significant relative to the naive rule, even after controlling for turnover costs. Our results suggest benefits to employing more sophisticated econometric models than the sample covariance matrix, and that mean-variance strategies often outperform the naive portfolio across multiple datasets and assessment criteria.