Analyzing volatility and policy changes in the financial market

Claremont Colleges Scholarship @ Claremont CGU Theses & Dissertations CGU Student Scholarship Spring 2021 Analyzing Volatility and Policy Changes in the Financial Market: Three Essays in Applied Finance Rebecca Anne Bommarito Claremont Graduate University Follow this and additional works at: https://scholarship.edu/cgu_etd Recommended Citation Bommarito, Rebecca Anne. Analyzing Volatility and Policy Changes in the Financial Market: Three Essays in Applied Finance. CGU Theses & Dissertations, 201.edu/cgu_etd/ 201.5642/cguetd/201 This Open Access Dissertation is brought to you for free and open access by the CGU Student Scholarship at Scholarship @ Claremont. It has been accepted for inclusion in CGU Theses & Dissertations by an authorized administrator of Scholarship @ Claremont.

For more information, please contact scholarship@cuc. Analyzing Volatility and Policy Changes in the Financial Market: Three Essays in Applied Finance By Rebecca Bommarito Claremont Graduate University 2021 Approval of the Dissertation Committee This dissertation has been duly read, reviewed, and critiqued by the Committee listed be- low, which hereby approves the manuscript of Rebecca Bommarito as fulfilling the scope and quality requirements for meriting the degree of Doctor of Philosophy in Economics. Pierangelo DePace, Chair Claremont Graduate University and Pomona College Associate Professor of Economics Tom Willet Claremont Graduate University and Claremont McKenna College Director, Claremont Institute for Economic Policy Studies Horton Professor of Economics Clemens Kownatzki Pepperdine Graziadio Business School Department Chair of Accounting, Finance & Real Estate Assistant Professor of Finance Hisam Sabouni Claremont Graduate University Clairvoyant Financial Abstract Analyzing Volatility and Policy Changes in the Financial Market: Three Essays in Applied Finance By Rebecca Bommarito Claremont Graduate University: 2021 Volatility in financial markets make forecasting, or in other words estimating what will happen in the future, a difficult task. Too often forecasts are made but hardly ever revisited to see how accurate the forecast was and if not, why? The three chapters of my dissertation are focused on examining volatility in financial markets from changes in investors’ trading behavior as well as studying the characteristics of forecast error of various financial securities.

Often, the accuracy of these forecasts rely on the estimates made for future volatility. In my first chapter, we1 analyze the predictive power of the Black-Scholes-Merton (BSM) model on a data set of options on the SPDR SP 500 Trust ETF (SPY). We leverage the full options chain to analyze the full forecasted distribution of prices through N(d2), which we compare to the distribution of prices of SPY. Using non-parametric GOF tests, such as the Kolmogorov–Smirnov and Anderson-Darling tests, we are able to analyze whether two different distributions come from the same underlying population distribution.

We find that BSM tends to overestimate the tails in the implied probability distribution when further away in expiration, compared to the empirical price path of SPY. The resulting comparison gives way to visualizing and testing the ability for the BSM to predict the likelihood of options expiring in-the-money. Our findings suggest the BSM, in most cases, correctly estimates the underlying risk adjusted probabilities only a few days out from expiration, which may be attributed to the uncertainty in traders to foresee market movements until an option is close to expiration. However, this behavior is more pronounced during crisis periods, where the BSM tends to correctly estimate the likelihood of tail events occurring more often than during periods of market normalcy.

In my second chapter, my co-authors and I2 study the characteristics of error in economic forecasts over time. We focus on explaining the variation errors of the survey of professional economic forecasts (SPF) across three financial securities by isolating the effects of changes in fiscal and monetary policy as well as changes in various macroeconomic indicators. We 1 This is joint work with Nasser Khalil, Clemens Kownatzki and Hisam Sabouni 2 This is joint work with Hisam Sabouni examine if it is changes in government policy or changes in macroeconomic indicators (or market conditions) that is primarily responsible for increases in SPF forecast error. We use a principal component analysis to first perform orthogonal dimension reduction of our macroe- conomic indicator variables and use the first two principal components as an overall measure for market conditions.

We then use a linear regression to test whether market conditions or monetary and/or fiscal policy is primarily responsible for increases in SPF forecast error of three securities’ yields: the three month Treasury bill, Moody’s AAA corporate bond and the Ten year Treasury bill. We find increases in monetary policy via the EFFR affects the short-term security in our analysis to a large magnitude, but increases in overall market conditions affect all securities in our analysis to a smaller but significant degree. In my third chapter, I explore an anomaly that exists in the U. equity market that has not been documented before; investors’ reactions to earnings announcements are not only asymmetric, but seasonal.

Knowing which months experience larger variation than others, investors may incorporate financial derivatives such an options to hedge downside risk. Using a fixed effects linear regression, I first examine the effect an earnings beat and earnings miss have on abnormal returns; which are calculated by a CAPM-GARCH model. I find an earnings beat on average has large significant increases in firms’ abnormal returns while an earnings miss, or a negative earnings surprise, has limited downside impacts. Examining this effect further at the month level, I find investor’s reactions are extremely large to earnings beats announced in months June and to earnings misses announced in December compared to other months.

Acknowledgments I would like to start by first thanking the faculty and administration of Claremont Grad- uate University (CGU) for their continued support and encouragement throughout my re- search studies and experience in the doctoral program. I feel extremely grateful for this opportunity and to have met the professors and colleagues that I did along the way, for this would not have been possible without them. I am very thankful to my two academic advisors, Greg DeAngelo and Hisam Sabouni, for guiding my research ideas and providing a collaborative environment. Greg brought me into the Computational Justice Lab (CJL) during my second year of CGU and introduced me to several other members on campus who could assist me in my research endeavors within applied finance.

Another member of the CJL lab, Hisam Sabouni, was really the catalyst for my interest in applied finance and one of my main motivators during the doctoral program. Hisam is a co-author on two of my dissertation chapters and member of my dissertation committee. I view Hisam as my mentor and am truly grateful for all the opportunities he has provided for me and time he has spent to improve my work. Another huge motivator of my research studies as well as the chair of my dissertation committee is Pierangelo DePace.

Pierangelo was incredibly supportive and would frequently meet with me to discuss research, the Ph. process, or anything else going on in my life. I developed by understanding of econometrics largely through Pierangelo’s classes and feel extremely blessed to have had such a thoughtful and thorough professor. I am grateful to other members of my committee, Tom Willet and Clemens Kownatzki who both provided invaluable comments to the papers in my dissertation.

Tom has been a pioneer in the areas of International Money & Finance and Public Policy and is a well respected figure across our community. His brilliant insights and thought provoking questions have truly elevated each of my research papers. I was introduced to Clemens through Hisam and he quickly became someone I would turn to for advice on my papers. Clemens helped edit and one of the papers in my dissertation as is a coauthor on a second, which was a wonderful collaborative experience for me.

I would also like to acknowledge two faculty members from my undergraduate studies, Brian Jenkins and Kim Makuch, whose continued support and guidance helped lead me to where I am today. Brian was one of my economic lectures and hired me as an economic tutor. It was through Brian’s classes that I developed a passion for economics and the desire to continue my education in economics at the graduate level. Kim was a TA of mine and someone I developed a professional and close personal relationship with.

At the time v Kim was towards the end of finishing her Ph. and guided me through the entire process of applying to grad school, edited papers for my applications and kept it real about the challenges I would face if admitted. I am beyond grateful to have had such a role model and friend during that process and stage of my life. I want to thank another coauthor, Nasser Khalil, who was a pleasure to work with on one of the papers in my dissertation.

Nasser and I had a very fluid writing style while working together and complemented each others skill sets quite which helped to elevate our paper. Two members of the CJL lab and close personal friends I would also like to thank are Maryah Garner and Minjae Yun. I first met Maryah as a TA for one of my classes and over the years has become my best friend and big sister in the graduate program. Countless times Maryah would read over my papers and edit them, discuss questions I had from class or research, or hype me up before a big test.

Minjae was the first person I met at CGU. We bonded instantaneously and eventually became roommates. We did everything together and it really made the graduate process a lot more fun. I truly do not know how I would have gotten through all those classes, tests and qualifying exams without Minjae and her hilarious nature.

Most of all, I would like to thank my significant other Keenan. I appreciate how Keenan has believed in me throughout every step of the Ph. From long exhausting days to anxiety inducing tests, he was always by my side. Keenan put my dreams above his own to support me on this journey, and I am forever grateful.

Lastly, I would like to thank both of my parents for believing in every dream of mine and encouraging me throughout my graduate studies. vi Contents 1 Chapter 1: Analyzing the Predictive Power of Black-Scholes 1 1. 21 2 Chapter 2: Forecast Error: A Cause of Government Intervention or Mar- ket Conditions? 23 2.2 Government Policy’s Impact in Financial Markets .1 Dimension Reduction with PCA. 34 3 Chapter 3: Seasonal Decomposition of Abnormal Market Returns 35 3.3 Calculating Abnormal Returns .1 Asymmetric Effect of Earnings Surprises on Abnormal Returns .2 Seasonal Effects of Earnings Surprises on Abnormal Returns.

47 4 Conclusion 50 5 Appendix 58 vii 1 Chapter 1: Analyzing the Predictive Power of Black- Scholes Coauthored with Nasser Khalil, Clemens Kownatzki and Hisam Sabouni 1.1 Introduction For some time, economists have utilized options pricing models, such as the Black-Scholes Merton (BSM) model, to not only estimate an option’s fair value, but to forecast the volatility of the underlying asset derived from the implied volatility in options. One way to examine this is to back out of BSM what the option is “implying” about the underlying future expected volatility, also known as “implied volatility”. The price of an option is dependent on the breadth of possible scenarios that may occur over the life of an option. Much of the attraction of the BSM model has been on modeling the risk and sensitivity of the options price of the underlying security.

A natural product of the BSM, however, is the underlying risk-adjusted probabilities used to price options. With an options chain, probabilities are assigned as to the likelihood the underlying asset will be above the strike price at expiration. Little, if any research, has focused on the implications of risk-adjusted probabilities derived by the BSM model and the weight given to “deep-in’ and “out of’ the money contracts.