# Xiaoxiao Tang

Assistant Professor of Finance

Jindal School of Management, University of Texas at Dallas

Phone: (972) 883-4736

E-Mail: xiaoxiao.tang@utdallas.edu

**Research Interest**

**Research Interest**

Theoretical and Empirical Asset Pricing

Options

Recovery

Disaster Risk

**Education**

**Education**

PhD in Finance, Washington University in St Louis, 2018

PhD in Statistics, University of Virginia, 2014

BS, Tsinghua University, 2009

**Publications**

**Publications****Recovering the FOMC Risk Premium**

**Journal of Financial Economics***, 2022,** *with Hong Liu and Guofu Zhou

The Federal Open Market Committee (FOMC) meetings have significant impact on market returns. We propose a methodology to recover the risk premium associated with FOMC meetings from option prices. We also predict the sizes of upward/downward market price jumps after an imminent FOMC meeting. In our empirical analysis, with observed price data for 83 meetings and with data backed out via machine learning for the remaining 109 meetings from 1996 to 2019, we find that the risk premium varies from 2 to 299 basis points (bps), with an average of 41 bps which is consistent with the average realized returns documented in the literature. The average predicted upward jump size is 101 bps, and the average predicted downward jump size is 129 bps.

**Management Sc****ience,***forthcoming*. with Ohad Kadan and Fang Liu

We propose a methodology for estimating option-implied forward-looking variances and covariances of assets and portfolios, which may not possess actively-traded options. Our approach relies on the observation that any factor structure for stock returns naturally induces a factor structure for return volatility. We implement the methodology empirically and show that our forward-looking moment estimates provide useful implications for the prediction of jumps and for portfolio choice.

**A Bound on Expected Stock Returns**

* Review of Financial Studies*, 2020, with Ohad Kadan.

We present a sufficient condition under which the prices of options written on a particular stock can be aggregated to calculate a lower bound on the expected returns of that stock. The sufficient condition imposes a restriction on a combination of the stock's systematic and idiosyncratic risk. The lower bound is forward-looking and can be calculated on a high-frequency basis. We estimate the lower bound empirically and study its cross-sectional properties. We find that the bound increases with beta and book-to-market ratio and decreases with size and momentum. The bound also provides an economically meaningful signal on future stock returns.

**Volatility-Managed Portfolio: Does It Really Work?**

* Journal of Portfolio Management*, 2019, with Guofu Zhou and Fang Liu.

In this article, the authors find that a typical application of volatility-timing strategies to the stock market suffers from a look-ahead bias, despite existing evidence on successes of the strategies at the stock level. After correcting the bias, the strategy becomes very difficult to implement in practice as its maximum drawdown is 68--93% in almost all cases. Moreover, the strategy outperforms the market only during the financial crisis period. The authors also consider three alternative volatility-timing strategies and find that they do not outperform the market either. Their results show that one cannot easily beat the market via timing the market alone.

**Out-of-sample equity premium prediction: A scenario analysis approach**

* Journal of Forecasting*, 2018, with Peiming Wang and Feifang Hu.

In this article, the authors find that a typical application of volatility-timing strategies to the stock market suffers from a look-ahead bias, despite existing evidence on successes of the strategies at the stock level. After correcting the bias, the strategy becomes very difficult to implement in practice as its maximum drawdown is 68--93% in almost all cases. Moreover, the strategy outperforms the market only during the financial crisis period. The authors also consider three alternative volatility-timing strategies and find that they do not outperform the market either. Their results show that one cannot easily beat the market via timing the market alone.

**Working Papers**

**Working Papers****Variance Asymmetry Managed Portfolios**

I propose a forward-looking measure of the asymmetry in the variance of asset returns and introduce a way to estimate it from option prices. This measure is model free and it serves as a close approximation for the asset expected excess return. I provide an empirically supported sufficient condition under which the risk-neutral variance asymmetry ranks stocks based on their physical expected returns. Empirically, I find strong cross-sectional correlation between this measure and future stock returns. Variance asymmetry managed portfolios yield economically large average returns and Sharpe ratios. Crash risk and standard asset pricing factors do not explain this abnormal performance.

**The Information Content of The Implied Volatility Surface: Can Option Prices Predict Jumps?**

with Yufeng Han and Fang Liu

We ask whether option prices contain information on the likelihood and direction of jumps in the underlying stock prices. Applying the partial least squares (PLS) approach to the entire surface of the implied volatilities (IV), we show that option prices can successfully predict downward jumps in stock prices, but not upward jumps. The PLS estimated downward jump factor can predict stock returns with a spread of 1.53% per month between stocks predicted to have the lowest probability of downward jumps and stocks predicted to have the highest probability of downward jumps. Both put and call option prices, and options of both short and long maturity contribute to the predictability. Furthermore, the predictability of the downward jump is robust to many firm characteristics as well as options related variables. Consistent with the notion that informed investors trade in the options markets to profit from negative information in order to circumvent the short-sale constraint, we find that stronger predictability is associated with tighter short-sale constraints in the equity market, and in periods when the market has poor performance.

**Stock Option Predictability for the Cross-Section**

with Andreas Neuhierl, Rasmus Tangsgaard Varneskov and Guofu Zhou

We provide the first comprehensive analysis of the information content from options markets for predicting the cross-section of stock returns. We jointly examine an extensive set of firm characteristics and an exhaustive set of option predictors, filling the void between two largely disjoint literatures. Using both portfolio sorts and machine learning methods, we find that options have strong predictive power for the cross-section of returns after controlling for firm characteristics. A structural analysis shows that the strongest predictors are associated with tail risk premia and leverage. Our findings imply that these risks are estimated more accurately from options data, providing annualized Sharpe ratios in excess 1.5.

**Recovering Conditional Factor Risk**** **

with Ohad Kadan and Fang Liu

We offer an approach for recovering option-implied time-varying forward-looking risk premia of systematic factors---even if they do not possess actively-traded options. We apply this approach to the market, size, value, and momentum factors. We find that factor premia are highly volatile. Both the market and the value premia tend to be higher during slowdowns and recessions and during turbulent times. By contrast, the momentum premium is higher during periods of high economic growth and low volatility. We use the recovered factor premia to construct trading strategies, which mitigate market and momentum crash risk and to predict returns of individual stocks even if they do not possess traded options.

**Heterogeneous Response: An Extension of the Fama-MacBeth Regression**

** ** with Xiwei Tang and Guofu Zhou

We propose an extension of the Fama-MacBeth regression by allowing stock returns to respond differently to firm characteristics. This heterogeneous model can potentially capture non-linearity and interactions. Empirical, applying it to a common set of fifteen firm characteristics, we find that the value-weighted long-short portfolio has an annualized Sharpe ratio of 0.97, doubling that of the usual homogenous model, and our model is not only easier to understand, but also performs better than existing machine learning models. We also propose a test detecting which risk exhibits heterogeneous reactions, and find that heterogeneity is significant for firm size, momentum, and stock volatility. Furthermore, we find that heterogeneous reactions are more pronounced during recession periods.