Πλοήγηση ανά Συγγραφέα "Lykourgos, Alexiou"
Τώρα δείχνει 1 - 1 από 1
- Αποτελέσματα ανά σελίδα
- Επιλογές ταξινόμησης
Τεκμήριο Option-implied risk measures and the cross-sectional variation of stock returns(2021) Lykourgos, Alexiou; Αλεξίου, Λυκούργος; Athens University of Economics and Business, Department of Accounting and Finance; Κωστάκης, Αλέξανδρος; Λελεδάκης, Γεώργιος; Χαλαμανδάρης, Γεώργιος; Σπύρου, Σπυρίδων; Τσεκρέκος, Ανδριανός; Καβουσανός, Εμμανουήλ; Ρομπόλης, ΛεωνίδαςThis thesis focuses on examining the information contained in options about the valuation of equity securities. Options incorporate valuable information about investors’ expectations on future returns of their underlying securities. This stems from the fact that markets are imperfect due to constraints such as asymmetric information and barriers to short selling, making options non-redundant assets.Over the last decade there have been many studies deriving a measure from option contracts and examining whether it predicts future stock returns. For example, Guo and Qui (2014) find a negative relation between implied volatility and future stock returns and Stilger Kostakis and Poon (2017) show that risk-neutral skewness positively predicts future stock returns. The aforementioned studies use a measure based on a single property/moment of the risk-neutral distribution of stock returns and therefore may lose valuable information. In chapter 1 we propose a joint measure of the probability density function of stock returns. More specifically, we combine volatility, skewness and kurtosis implied by options in a score variable based on investors’ moment preferences, that is, a low score identifies a stock with high volatility, low skewness and high kurtosis. On the contrary, a high score identifies a stock with low volatility, high skewness and low kurtosis. Essentially, our measure can be interpreted as a defensiveness measure where the definition of defensiveness is expanded by incorporating skewness and kurtosis alongside with volatility.We sort stocks in portfolios based on our score measure and find that high score stocks have higher returns than low score stocks. This statistically significant relation between our score measure and future stock returns holds various robustness tests such as double sorts, Fama-MacBeth regressions and using a sample with larger cap stocks. We show that this relation is explained by the exposure to shocks in aggregate volatility and depends on investors’ sentiment. In periods of low sentiment, the intertemporal capital asset pricing model (ICAPM) fully explains this relation, while in periods of high sentiment the relation remains statistically significant and is attributed to mispricing.The literature has shown that jump risk is priced by investors in the options market. A part of the research has examined the impact of jump risk on equity and variance risk premiums, providing strong evidence that an important fraction of those premiums can be attributed to the jump risk premium (see Santa-Clara and Yan (2010) and Bollerslev and Todorov (2011)). Nevertheless, the way that jump risk impacts the cross-sectional variation of stock returns has received less attention in the literature. Therefore, in chapter 2 we examine if exposure to downside (left) and upside (right) jump shocks of the market are priced. We construct a theoretically consistent measure of jump risk through the S&P500 options. The simulation study we conduct shows that it provides reliable estimates as opposed to the JUMP risk factor of Cremers, Halling and Weinbaum (2015) which is a biased measure of jump risk. We find that betas to shocks in downside jumps produce a statistically significant risk premium of -11.52% contemporaneously in an annual basis, while betas on shocks to upside jumps do not. The statistically significant relation between betas to shocks in downside jumps and stock returns is not due to risk-neutral variance and skewness shocks. Additionally, we show that it produces statistically significant abnormal returns on the next month of the formation period while it is robust to different estimation period such as 9, 6 and 3 months and different holding periods such as 3 and 6 months.In chapter 3 we examine the implied volatility curves that are arise from option prices prior to earnings announcements days. We show that a portion of them becomes concave, taking unusual shapes such as W, S, and inverted U. This characteristic, which is mostly observed in short-term options, implies a bimodal risk-neutral density for the stock price. This means that investors predict a jump in the stock price at the earnings announcement day. We find that concave implied volatility curves do predict higher absolute stock returns at the earnings announcement day and higher volatility after the earnings announcement day. However, straddle returns of stocks with concave implied volatility curves are statistically significantly lower than those with non-concave implied volatility curves. This is attributed to the fact that at-the-money options of concave implied volatility curves are much more expensive and the jumps of the stock price at the earnings announcement day are not large enough to offset the substantial cost of these straddles. Therefore, investors identify earnings announcements that make stock prices jump and pay a substantially higher premium to hedge against this risk.