Essays in Financial Econometrics

This dissertation consists of three essays. In the first essay, I analyze the performance of five different classes of integrated variance estimators when applied to various stocks of differing market capitalization in an attempt to discover the circumstances under which one estimator should be chosen over another. In recent years, there has been an explosion of research on the volatility of stock returns. As high frequency stock price data became more readily available, there have been many proposed estimators of integrated variance which attempt to take advantage of the informational gains of high-frequency data while minimizing any potential biases that arise from sampling at such a fine scale. These estimators rely on various assumptions about the price process which can make them difficult to compare theoretically. I find that across several stocks in different size deciles, the truncation estimator outperforms the other estimators of integrated variance. Furthermore, I find that choosing a truncation parameter of 2-3 standard deviations leads to the most accurate estimates on average.

In the second essay, I estimate latent factor models of liquidity and volatility. Common liquidity and volatility factors are extracted using multiple liquidity and volatility measures. Additionally, latent factors are extracted by aggregating across both liquidity and volatility resulting in what we will call the common ``uncertainty'' factors. This underlying uncertainty factor is correlated with the individual and common liquidity and volatility factors as well as returns. I find that the underlying uncertainty risk factor is significantly priced in the cross section of expected returns, while the risks associated solely with liquidity and volatility are not. These results suggest that the liquidity risk and volatility risk may both proxy for an underlying uncertainty risk which drives the significant results when considering them individually.

The third essay further explores the ``uncertainty'' factor and links it to the macroeconomy with the hope of accurately forecasting real GDP growth, growth in industrial production, and growth in the unemployment rate. I show that shocks to the uncertainty factor have both in- and out-of-sample predictability for real GDP growth as well as growth for both industrial production and unemployment rate. While the uncertainty factor significantly improves forecast performance over an AR(1) model, there is no indication that the forecasts based on our uncertainty factor significantly outperform forecasts based on an aggregate liquidity measure.