Working papers

This paper proposes a bootstrapping procedure for estimating conditional option risk premiums and conditional option return variance and covariance. The procedure scales the daily security return with the option implied volatility to filter out the stochastic volatility effect and extract the return innovation. It estimates a nonlinear mean-reverting dynamics to extract the volatility innovation on each floating implied volatility series. Jointly bootstrapping the return and implied volatility innovations captures their co-movements and constructs the security price and implied volatility paths for option excess return calculation over any investment horizon.  Applying the procedure to the history of over-the-counter currency options shows that the bootstrapped option return risk and risk premium vary strongly both across contracts and over time. The bootstrapped option risk premium predicts the future realized option excess return in accordance with the expectation hypothesis. Mean-variance investment in the option contracts based on the bootstrapped option risk premium and covariance generates high risk-adjusted returns. 

This paper strives to identify value-based systematic investment opportunities in the U.S. corporate bond market through the joint construction of a bond valuation model and a return factor model. The valuation model explains the cross-sectional variation of bond yields with a flexible local linear functional form in bond risk characteristics. The return factor model embeds the residual yield from the valuation model as a mispricing factor, while accounting for stronger co-movements between bonds from the same industry, similar rating classes, and similar duration segments, as well as differential market pricing for bond return risk, liquidity cost, and optionality exposure. The slope coefficient on the mispricing factor captures the ex post excess return on the value-investing portfolio that targets a unit exposure to the identified mispricing opportunities while being neutral to all systematic risk exposures. 

Investors are averse to risk but love optionality. When a security's embedded optionality increases with its risk level, the entanglement,  combined with the opposite investor preferences, can generate seemingly abnormal market pricing behaviors. This paper frames the bond and stock return behavior within a structural framework and disentangles their directional risk exposure from their optionality exposure via a joint stock-bond return factor model. The factor portfolio targeting a unit exposure to market risk but zero exposure to optionality generates a significantly positive average excess return, consistent with investor risk aversion. By contrast, the factor portfolio targeting a unit exposure to optionality but without directional exposure to firm value variation generates a significantly negative average excess return, reflecting investor penchant for optionality. The separation of risk from optionality sheds light on the distress puzzle in the stock and bond market and helps explain the bet-against-beta and volatility premiums in the stock market.


This paper introduces the concept of cross-sectional mean-variance efficiency in corporate risk-taking decisions. We construct mean-variance ratio forecasts on the asset return of US public companies and examine how much the forecasts can explain the cross-sectional risk-taking variation with a common proportionality coefficient. Estimation shows that cross-sectional mean-variance efficiency explains a large proportion of corporate risk-taking variation at the firm level and almost completely explains the variation at the industry level. Over time, corporations target mean-variance efficient allocations to maintain stable financial leverage. When a firm deviates from mean-variance efficiency, the firm rebalances toward it at a slow speed.

The paper identities the historical variance term structure as a key conditioning variable that differentiates the different phases of a company's information cycle and shows that stock variance dynamics vary strongly through the different phases of the cycle. To predict stock variance over a large and ever-changing universe, the paper replaces time-series dynamics specification per each name with a cross-sectional forecasting relation at each date and develop a two-dimensional conditional pooling estimation that balances the needs for reducing estimation errors and capturing dynamics variation across the cycle. We quantify the economic significance of the approach through an option investment analysis and highlight the classic asset pricing relation variations across the information cycle.

Option prices commonly differ from the Black-Scholes formula along two dimensions: implied volatilities vary by strike price (volatility smiles) and maturity. We account for both using Gram-Charlier expansions to approximate the conditional distribution of the logarithm of the price of the underlying security. In this setting, volatility is approximately a quadratic function of moneyness, a result we use to infer skewness and kurtosis from volatility smiles.