Essays in Financial Econometrics and Machine Learning
Abstract
Financial econometrics is a highly interdisciplinary field that integrates finance, economics, probability, statistics, and applied mathematics. Machine learning is a growing area in finance that is particularly suitable for studying problems with many variables. My thesis contains three chapters that explore financial econometrics and machine learning in the fields of asset pricing and risk management.
Chapter 2 studies the implications of the new Basel 3 regulations. In 2019, the BCBS finalized the Basel 3 regulatory regime, which changes the regulatory measure of market risk and adds new complex calculations based on liquidity and risk factors. This chapter is motivated by these changes and seeks to answer the question of how regulation affects banks’ choice of risk-management models, whether it incentivizes them to use correctly specified models, and if it results in more stable capital requirements.
Chapter 3 conducts, to our knowledge, the largest study ever of five-minute equity market returns using state-of-the-art machine learning models trained on the cross-section of lagged market index constituent returns, where we show that regularized linear models and nonlinear tree-based models yield significant market return predictability. Ensemble models perform the best across time and their predictability translates into economically significant Sharpe ratios of 0.98 after transaction costs. These results provide strong evidence that intraday market returns are predictable during short time horizons.
Chapter 4 studies the idiosyncratic tail risk premium and common factor. Stocks in the highest idiosyncratic tail risk decile earn 8% higher average annualized returns than in the lowest. I propose a risk-based explanation for this premium, in which shocks to intermediary funding cause idiosyncratic tail risk to follow a strong factor structure, and the factor, common idiosyncratic tail risk (CITR), comoves with intermediary funding. Consequently, firms with high idiosyncratic tail risk have high exposure to CITR shocks, and command a risk premium due to their low returns when intermediary constraints tighten. To test my explanation, I create a novel measure of idiosyncratic tail risk. Consistent with my explanation, CITR shocks are procyclical, are correlated to intermediary factors, are priced in assets, and explain the idiosyncratic tail risk premium.