Authors: K. Aretz, M-T. Lin & S-H. Poon Discussant: Y. E. Arisoy

1. Discussion of:Moneyness, Total, Systematic, and Idiosyncratic Volatility and the Cross-Section of European Option Returns Authors: K. Aretz, M-T. Lin & S-H. Poon Discussant: Y. E. Arisoy 10th Financial Risks International Forum Paris, March 28, 2017

2. The paper • Uses a two-periodcontinuouspayoff SDF frameworkwith a riskyassetwithpayoff and a continuum of plain-vanillaEuropean call and put options writtenon the underlyingasset. • Using the standard properties of the SDF framework, the paperderives the expressions for the expected return on the underlyingasset as well as the European call options written on the asset. • The expression for the call option return yields five testable predictionsregarding the moneyness (and strikes) of the option (as posit by Coval and Shumway, 2001) as well as the systematic and idiosyncraticvolatility of the underlyingasset. • The major contribution of the paperisitsdemonstration of how systematicvolatilityinteractswithexpected option returns.

3. In a nutshell... • As opposed to Hu and Jacobs (2015), decomposing total volatility into its systematic and idiosyncratic components is a nice idea, as it offers further insights with respect to how the two components of total volatility interact with option returns. • What we know: IVOL has a negative (positive) relation with expected call (put) returns (Hu and Jacobs, 2015) • What we learn new: SVOL increases expected call and put returns at low strikes and decreases expected call and put returns at high strikes. Hence, SVOL and IVOL affect expected returns of ITM and ATM options in the opposite direction, but OTM options in the same direction. • Can we learn more?

4. Some thoughts • Is it the underlying asset volatility or the call/put implied volatilities (or maybe both) that is driving the results? • An et al. (2014) decompose call and put implied volatilities into systematic and idiosyncratic components and find that it is the idiosyncratic implied volatility that is more important in the joint cross-section of stock and option returns. • Could higher order moments be also priced in the cross-section? • Shaliastovich (2016) show that vol-of-vol is priced in the cross-section of option returns. • Given the interaction between IVOL and ISKEW (Boyer et al., 2011) could ISKEW or kurtosis (similar to vol-of-vol) factors be driving the results? • Can jump risk also explain expected call/put returns? • By decomposing total return volatility into its diffusive and jump components in the spirit of Ait-Sahalia (2004) and Andersen, Bollerslev and Diebold (2007), can we learn more?

5. Further thoughts • The paper use several controls (such as mispricing factors, option illiquidity and stock illiquidity factors) in FM regressions to rule out liquidity and mispricing effects that might be present in non-ATM options. • An et al. (2014) show that there are certain stock characteristics that can explain changes in call and put implied volatilities. • High past month abnormal returns (alphas) high call/put IV • High B/M low call/put IV • High momentum high call/put IV • High QSKEW high call IV low put IV • Option returns exhibit marked skewness and have pronounced non-linearities as a result of leverage making statistical inference difficult in an OLS setting (Broadie, Chernovand Johannes, 2008; Chaudhriand Schroeder, 2009) • Do the result also hold for change in call and put IV’s?

6. Further thoughts • Barberis and Huang (2001) and Goyal and Saretto (2009) behavioral story: • Stocks with high past abnormal returns (hence with high total volatility) lead agents to become more uncertain of the future prospects of the stock, hence agents overestimate future volatility, which in turn might imply lower call returns. • Controlling for DISP (analysts’ earnings forecast dispersion) as in Diether, Malloy, and Scherbina (2002) or An et al. (2014) might alleviate the behavioral story concern. • SVOL and IVOL are estimated using monthly data over 60-month rolling windows. • Are the estimation methodology and results robust to using daily data over 1-month (or 12-month) rolling windows? • The paper claims that the elasticity of underlying asset’s return to its IVOL is zero, hence the call option sensitivity to IVOL is driven solely by the option’s elasticity. • However, in a world with underdiversified investors (Malkiel and Xu, 2006) a stock’s return can depend on its IVOL (Ang et al., 2006).

7. Overall • The idea of decomposing total volatility into its systematic and idiosyncratic components and testing its implications on the cross-section of option returns is a nice idea. • It contributes to a growing literature that looks into the determinants of option returns in the cross-section. • The paper did a significant amount of work (theoretically and empirically), it is clear, and well-written. • With the addition of several robustness checks, the paper can be further polished and strengthened.

8. Discussion of:Flight to Gold: Extreme Weather Events and Stock Returns Authors: M. G. Lanfear, A. Lioui & M. G. Siebert Discussant: Y. E. Arisoy 10th Financial Risks International Forum Paris, March 28, 2017

9. The paper • Uses an eventstudyapproach to test the impact of North Atlantic hurricanes on U.S. stock returns. • Documents that Gold industryexperiencesubstantial positive abnormalreturns over differenteventwindowsfrom formation of the hurricane to 30 days post-landfall. • The outperformance of CAR’s in the Gold industryis not matched by anyotherindustry and isattributed to investors’ flight-to-safetyduringuncertainepisodes. • Whatmakes the paperdifferentfrompriorstudiesisthat : • It looks at the performance of Gold-related stocks traded in the U.S. market, not the gold’sounceprice per se. • It looks at the impact of a meteorologicalphenomenonwhichmightincreasemarket’s perception of uncertainty and henceinduceflight-to-safety.

10. In a nutshell... • What makes gold different than other asset classes has been debated for a long time. • What we know: Gold is an asset with average return close to risk-free rate of return but a very high volatility, which does not correlate with per capita consumption growth or inflation, and which has limited diversification and inflation hedging capacity, but correlates negatively with T-Bill rates (Barsky and Summers, 1988; Erb and Harvey, 2013; Barro and Misra 2016). • What we learn new: Gold-related stocks earn statistically significant portfolio average CAR’s and average CAR’s over different event windows ranging from the hurricane formation period up to 30 days after the first landfall in the U.S. • Can we learn more?

11. Some thoughts • Would the results hold for gold price as well? Do we observe similar increases in gold price following hurricane formation? One would expect a similar result. • If not, why do we observe it only for stocks, but not for gold itself? • The authors acknowledge the potential impact of confounding events over long event windows as used in the paper, and argue that averaging returns over stocks and over time for each industry can mitigate the potential confounding events over long event windows. • Are the results robust to the omission of stocks with confounding events during the event window? • The results do not seem to hold for [0,+1] window, which is the candidate event window for having potentially the least confounding effect. • On the other hand, are the results persistent over event windows longer than +30 days after the event? • What makes Shipping, Computer Hardware, Software, Banks, Financials and Gold different than other industries?

12. Further thoughts • The paper does quite a significant number of robustness checks to see if the results are sensitive or not to alternative factor models, alternative weighting schemes, portfolio level analysis and to exclusion of stocks for which the model has limited explanatory power. • One thing that is not mentioned is whether stocks with prices < $5 are excluded or not. If not, the authors can also look at if the results continue to hold when low-priced stocks (which are more prone to illiquidity) are excluded from the sample. • Do the results hold for other extreme events such as periods of extreme cold weather, or other environmental disasters? • Do the results hold at periods of increased aggregate uncertainty (i.e. increases in VIX)? • “Hurricane” effect or “Sell in May and Go Away” effect?

13. Overall • I liked reading the paper. • Well-written and quite polished with plenty of robustness checks. • The paper documents an interesting phenomenon regarding the performance of gold-related stocks following hurricanes. A nice contribution. • A longer discussion on the economic rationale on why a handful of industries also exhibit a similar pattern would further benefit the reader to understand the dynamics between extreme weather events and certain industries’ positive price reaction to those events. • “Sell in May and Go Away” effect could be confounding the results.