Essays in Asset Pricing:

Thesis advisor: Ronnie SadkaChapter 1 studies volatility tail risk and its asset pricing implications. Motivated by dynamic models featuring jumps in stochastic volatility, I examine the economic behaviors and the pricing of volatility tail risk in the cross-section of asset prices, including stocks and options. Using intraday option dataset, I construct a novel non-parametric volatility tail risk measure from high-frequency implied volatility data and find a strong negative effect associated with volatility tail risk: stocks with high volatility tail risk robustly underperform stocks with low volatility tail risk. In particular, the negative price of volatility tail risk is driven systematic component through decomposition. The volatility tail risk measure is strongly related to jump components in the volatility process identified using non-parametric approach, and is the best predictor of future average jump intensity and jump size. Furthermore, the volatility tail risk measure plays an important role in predicting future economic variables: it positively and robustly predicts future equity return jump risk, volatility risk premium, idiosyncratic volatility, stock return high-order moments, illiquidity measure and volatility skew. Finally, it predicts future risk-neutral option straddle return as well as volatility risk premium, measured as the difference between implied volatility and realized volatility. Chapter 2 (joint with Dimitry Muravyev) studies the seasonality of option returns. We show that average returns for S&P 500 index options are negative and large: -0.7% per day. Strikingly, when we decompose these delta-hedged option returns into intraday (open-to-close) and overnight (close-to-open) components, we find that average overnight returns are -1%, while intraday returns are actually positive, 0.3% per day. A similar return pattern holds for all maturity and moneyness categories, and equity options. Rational theories struggle to explain positive intraday returns. However, our results are consistent with option prices failing to account for the well-known fact that stock volatility is substantially higher intraday than overnight. These results help us better understand the price formation in the options market. Chapter 3 (joint with Dimitry Muravyev) studies option informed trades and the connections with stock return predictability. We show that option order imbalances predict the cross-section of equity returns. We show that a large part of this predictability can be attributed as one-day announcement effect. Predictability of option order imbalances declines as forecasting horizon prolongs. In particular, we show that, the predictability of long-horizon predictability depends on the privacy of information. Public disclosure of option trades information has a crucial and negative impact on the predictability of option order imbalances. Furthermore, using identification algorithms, we can imprecisely distinguish between investor’s trades and option market maker’s trades and find that, the order imbalances from non-option market makers contain almost all information relevant for predicting future stock returns. Our results are consistent with theories implying that option trading volume reflects the actions of informed traders, and the action of disclosing this information can facilitate asset price movements.Thesis (PhD) — Boston College, 2018.Submitted to: Boston College. Carroll School of Management.Discipline: Finance