Earnings announcement timing,uncertainty, and volatility risk premiums

We examine the relationship between firms’ quarterly earnings report timing and uncertainty before quarterly earnings announcements. Prior research provides conflicting predictions on how investor uncertainty and report timing are related. Using implied volatilities from equity options and the realized returns to straddle positions, we find evidence that uncertainty and volatility risk premiums are higher for firms that report later in the quarter. Further tests show that the increase in option premiums is unexplained by risk factors suggesting a mispricing by investors. These results are not associated with static firm-level factors and our findings are concentrated in high growth firms.     

Multifactor Index Variance: The Case of the SPX 2000 to 2010

Recent work in volatility modeling and options pricing has suggested that index variance may be best described by multifactor models. We utilize Independent Component Analysis to produce independent factors from the SPX sector ETF returns from 2000 to 2010. We find that the independent factors are quite different from the factors produced with Principal Components. After fitting asymmetric GARCH models to each of the factors and applying the sector weightings of the SPX we show that, on average, SPX variance is best modeled with two factors. In addition, we also find that the secondary factor of SPX variance has switched from being technology based to being financial in nature. © 2011 Wiley Periodicals, Inc. Jrl Fut Mark 33:158–182, 2013     

Independent component analysis for realized volatility: Analysis of the stock market crash of 2008

This paper investigates the factors that drove the U.S. equity market returns from 2007 to early 2010. The period was highlighted by volatile energy and commodity prices, the collapse of insurance and banking firms, extreme implied volatility and a subsequent rally in the overall market. To extract the driving factors, we decompose the returns of the S&P500 sector ETFs into statistically independent signals using independent component analysis. We find that the generated factors have interesting financial interpretations and are consistent with the major economic themes of the period. We find that there are two sets of general market betas during the period along with a dominant factor for energy and materials sector. In addition, we find that the EGARCH model which accommodates asymmetric responses between returns and volatility can plausibly fit the high levels of variance during the crash. Finally, estimated correlations dropped when commodity prices moved higher, but then spiked when the S&P500 crashed in late 2008.