Prior literature shows that implied volatility spreads between call and put options are positively related to future underlying stock returns. In this paper, however, we demonstrate that the volatility spreads are negatively related to future out-of-the-money call option returns. Using unique data on option volumes, we reconcile the two pieces of evidence by showing that option demand by sophisticated, firm investors drives the positive stock return predictability based on volatility spreads, while demand by less sophisticated, customer investors drives the negative call option return predictability. Overall, our evidence suggests that volatility spreads contain information about both firm fundamentals and option mispricing.

In this paper, we demonstrate the need for a negative market price of volatility risk to recover the difference between Black–Scholes [Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81, 637–654]/Black [Black, F., 1976. Studies of stock price volatility changes. In: Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, pp. 177–181] implied volatility and realized-term volatility. Initially, using quasi-Monte Carlo simulation, we demonstrate numerically that a negative market price of volatility risk is the key risk premium in explaining the disparity between risk-neutral and statistical volatility in both equity and commodity-energy markets. This is robust to multiple specifications that also incorporate jumps. Next, using futures and options data from natural gas, heating oil and crude oil contracts over a 10year period, we estimate the volatility risk premium and demonstrate that the premium is negative and significant for all three commodities. Additionally, there appear distinct seasonality patterns for natural gas and heating oil, where winter/withdrawal months have higher volatility risk premiums. Computing such a negative market price of volatility risk highlights the importance of volatility risk in understanding priced volatility in these financial markets.

This paper shows that a New Year's gambling preference of individual investors impacts prices and returns of assets with lottery features. January call options, especially the out-of-the-money calls, have higher retail demand and are the most expensive and actively traded. Lottery-type stocks outperform their counterparts in January but tend to underperform in other months. Retail sentiment is more bullish in lottery-type stocks in January than in other months. Furthermore, lottery-type Chinese stocks outperform in the Chinese New Year's Month but not in January. This New Year effect provides new insights into the broad phenomena related to the January effect.

Prior studies find that the CBOE volatility index (VIX) predicts returns on stock market indices, suggesting implied volatilities measured by VIX are a risk factor affecting security returns or an indicator of market inefficiency. We extend prior work in three important ways. First, we investigate the relationship between future returns and current implied volatility levels and innovations. Second, we examine portfolios sorted on book-to-market equity, size, and beta. Third, we control for the four Fama and French [Fama, E., French, K., 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3–56.] and Carhart [Carhart, M., 1997. On persistence in mutual fund performance. Journal of Finance, 52, 57–82.] factors. We find that VIX-related variables have strong predictive ability.

We use option prices to examine whether changes in stock return skewness and kurtosis preceding earnings announcements provide information about subsequent stock and option returns. We demonstrate that changes in jump risk premiums can lead to changes in implied skewness and kurtosis and are also associated with the mean and variability of the stock price response to the earnings announcement. We find that changes in both moments have strong predictive power for future stock returns, even after controlling for implied volatility. Additionally, changes in both moments predict call returns, while put return predictability is primarily linked to changes in skewness.

Quarterly earnings conference calls are becoming a more pervasive tool for corporate disclosure. However, the extent to which the market embeds information contained in the tone (i.e. sentiment) of conference call wording is unknown. Using computer aided content analysis, we examine the incremental informativeness of quarterly earnings conference calls and the corresponding market reaction. We find that conference call linguistic tone is a significant predictor of abnormal returns and trading volume. Furthermore, conference call tone dominates earnings surprises over the 60 trading days following the call. The question and answer portion of the call has incremental explanatory power for the post-earnings-announcement drift and this significance is primarily concentrated in firms that do not pay dividends, illustrating differences in investor behavior based on the level of cash flow uncertainty. Additionally, we find that a context specific linguistic dictionary is more powerful than a more widely used general dictionary (Harvard IV-4 Psychosocial).