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Author ORCID Identifier

https://orcid.org/0000-0002-8244-2384

Document Type

Campus-Only Access for Five (5) Years

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Management

Year Degree Awarded

2019

Month Degree Awarded

May

First Advisor

Nikunj Kapadia

Second Advisor

Hossein B. Kazemi

Third Advisor

Matthew Linn

Fourth Advisor

Anna Liu

Subject Categories

Finance and Financial Management

Abstract

This dissertation focuses on empirical asset pricing, including stock and options pricing. In the first and third chapter, we examine the linkage between stock market and options market at firm level. In Chapter Two, we documents the impact that systematic variance risk has for option prices of individual stocks. In the first chapter, we study the relation between future stock returns and option-based measures. We find that the options-based measure - future stock return relation is strongest for relatively less liquid stocks. After taking transaction costs into consideration, the risk-adjusted returns of the long-short stock portfolios do not differ significantly between stock liquidity groups. This chapter provides better understanding on the options-based stock return predictability. In the second chapter, we construct novel factors to mimic variance risk related to firm characteristics using individual stocks' variance risk premium. We then document that market variance risk premium and variance risk mimicking factors have strong explanatory power for option prices. Our new analytic framework links the variance risk factors related to firm characteristics to the individual equity option price structure. In the third chapter, we provide additional empirical results on how stock price can affect option prices. Our preliminary results reveal a link between the informational inefficiency of stock price and option prices. We find that a greater departure from random walk leads to a lower level of implied volatility (compared to realized volatility) and a steeper implied volatility curve.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Sunday, May 10, 2020

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