Essays in Risk Management and Asset Pricing with High Frequency Option Panels

Abstract

The thesis investigates the information gains from high frequency equity option data with applications in risk management and empirical asset pricing. Chapter 1 provides the background and motivation of the thesis and outlines the key contributions. Chapter 2 describes the high frequency equity option data in detail. Chapter 3 reviews the theoretical treatments for Recovery Theorem. I derive the formulas for extracting risk neutral central moments from option prices in Chapter 4.

In Chapter 5, I specify a perturbation theory on the recovered discount factor, pricing kernel, and the physical probability density. In Chapter 6, a fast and fully-identified sequential programming algorithm is built to apply the Recovery Theorem in practice with noisy market data. I document new empirical evidence on the recovered physical probability distributions and empirical pricing kernels extracted from both index and single-name equity options. Finally, I build a left tail index from the recovered physical probability densities for the S&P 500 index options and show that the left tail index can be used as an indicator of market downside risk.

In Chapter 7, I uniquely introduce the higher dimensional option-implied average correlations and provide the procedures for estimating the higher dimensional option-implied average correlations from high frequency option data. In Chapter 8, I construct a market average correlation factor by sorting stocks according to their risk exposures to the option-implied average correlations. I find that (a) the market average correlation factor largely enhances the model-fitting of existing risk-adjusted asset pricing models. (b) the market average correlation factor yields persistent positive risk premiums in cross-sectional stock returns that cannot be explained by other existing risk factors and firm characteristic variables. Chapter 9 concludes the thesis.