[NEW]
A Theory of Equity and Bond Premia Implied by Derivatives (with Steven P. Clark and Weidong Tian) (Manuscript available soon)
Presentation: 2023 Wolfe Research
Market Risk Premium Expectation: Combining Option Theory with Traditional Predictors (with Hong Liu, Weike Xu and Guofu Zhou) (current Version: March 2024)
Presentations: 2024 AFA, 2023 CUHK-RAPS, 2023 CFEA, 2023 Wolfe Research
Abstract: In general, the slackness between the Martin lower bound (solely based on option prices) and the market risk premium depends on economic state variables. Empirically, we find that combining information from option prices and economic state variables yields forecasts of the market risk premium with greater out-of-sample performance compared to forecasts using option prices alone or economic state variables alone. Additionally, these combination-based forecasts can significantly increase investors’ utility by improving their portfolios’ Sharpe ratios. Our findings suggest the importance of combining information from option prices and economic state variables.
The Conditional Forward Return and Autocorrelation from VIX Derivatives (with Weidong Tian and Steven P. Clark) (current Version: January 2024)
Presentations: 2023 Wolfe Research, 2022 AFA (Poster), 2022 FMA, 2021 CICF (China International Conference in Finance)
Abstract: This paper develops a new methodology to express the conditional forward return of the S&P 500 index with VIX derivatives. Within this framework, we obtain time-varying market autocorrelation in real-time. The derivatives market information unveils a robust short-term reversal pattern on the S&P 500, thereby the month return predictability. Moreover, we employ signals extracted from the autocorrelation to implement market-timing strategies, demonstrating the substantial economic value of this approach. Lastly, we present a stylized pro-cyclical term structure of the conditionalforward returns.
Macroeconomic Trends and Equity Risk Premium Forecast (with Yufeng Han and Guofu Zhou) (current Version: February 2024)
Presentations: 2023 Hong Kong Conference for Fintech, AI and Big Data in Business, 2022 International Symposium on Forecasting, 2022 FMA
Abstract: In this paper, we find that macroeconomic trends, a tool for guiding monetary policies, also play a significant role in forecasting the equity risk premium. In a linear model that pools information from 14 macro variables to forecast market returns, the trends captured by moving averages perform both statistically and economically better than the current values of the macro variables. Moreover, machine learning methods, especially neural networks, can significantly improve the out-of-sample forecasting performance further. Due to volatility in macroeconomic variables, our empirical results show that macroeconomic trends play an important role in finance, consistent with and supporting the practice by the Federal Reserve of using trends in their forecasting models instead of just the lagged variables of the past period. Our study also demonstrates that, by accounting for both macro trends and nonlinearity, market return predictability is much greater than commonly believed.
Mispricing and Anomalies: An Exogenous Shock to Short Selling from JGTRRA (with Yufeng Han, Weike Xu and Guofu Zhou) (current Version: April, 2023)
Presentations: 2022 CICF, 2021 SFS Cavalcade North America, 2021 MFA, 2021 FMA