The Cross Section of Monetary Policy Announcement Premium (with Hengjie Ai, Xuhui Pan and Lai Xu) [Online Appendix] [Readme] [Data] [Codes][Slides], 2022, Journal of Financial Economics, 143(1), 247-276.
Abstract: We show that monetary policy announcements require a significant risk compensation in the cross-section of equity returns. Empirically, we use the expected reduction in implied variance upon FOMC announcements to measure the sensitivity of stock returns with respect to monetary policy announcement surprises. A long-short portfolio formed on our monetary policy sensitivity measure produces a statistically and economically significant average announcement-day return of 31.67 bps and this pattern is robust to controlling for standard risk factors. We develop an equilibrium model to account for the dynamics of implied variances and the cross-section of excess returns on expected variance reduction sorted portfolios around FOMC announcements.
Presented at: Tsinghua University PBCSF, University of Hong Kong, Federal Reserve Board*, University of Southern California*, University of Houston*, Tulane University*, Midwest Finance Association 2020, European Finance Association 2020*, Canadian Derivatives Institute Conference 2020*, Western Finance Association 2019*, Adam Smith Workshop 2020 (accepted), 6th University of Connecticut Finance 2020 (accepted).
Abstract: We show that monetary policy announcements require a significant risk compensation in the cross-section of equity returns. Empirically, we use the expected reduction in implied variance upon FOMC announcements to measure the sensitivity of stock returns with respect to monetary policy announcement surprises. A long-short portfolio formed on our monetary policy sensitivity measure produces a statistically and economically significant average announcement-day return of 31.67 bps and this pattern is robust to controlling for standard risk factors. We develop an equilibrium model to account for the dynamics of implied variances and the cross-section of excess returns on expected variance reduction sorted portfolios around FOMC announcements.
Presented at: Tsinghua University PBCSF, University of Hong Kong, Federal Reserve Board*, University of Southern California*, University of Houston*, Tulane University*, Midwest Finance Association 2020, European Finance Association 2020*, Canadian Derivatives Institute Conference 2020*, Western Finance Association 2019*, Adam Smith Workshop 2020 (accepted), 6th University of Connecticut Finance 2020 (accepted).
Announcements, Expectations, and Stock Returns with Asymmetric Information [Slides]
Awards: 2021 Northern Finance Association Meetings Best Ph.D Student Paper Award
2020 WFA Cubist Systematic Strategic Ph.D Candidate Award for Outstanding Research
Abstract: Revisions of consensus forecasts of macroeconomic variables positively predict announcement-day forecast errors, whereas stock market returns on forecast revision days predict announcement-day returns in the opposite direction. A dynamic noisy rational expectations model with periodic macroeconomic announcements quantitatively accounts for these findings. Under asymmetric information, average beliefs are not Bayesian: they underweight new information and positively predict subsequent belief errors. In addition, stock prices are partially driven by noise. Noise accumulates into stock prices on revision days but gets corrected upon announcements. Therefore, price changes on revision days negatively predict announcement-day returns.
Presented at: American Economic Association 2021, Western Finance Association 2020, Financial Intermediation Research Society Conference 2021, Northern Finance Association 2021, Econometric Society World Congress 2020, European Economic Association 2020, European Finance Association Poster 2020, China International Risk Forum 2020, Carnegie Mellon University, New York Fed, University of Toronto, University of California San Diego, University of Wisconsin–Madison, Boston University, University of Warwick, Central European University, Bank for International Settlements, Singapore Management University, New York University Shanghai, City University of London Cass Business School, Erasmus University Rotterdam, CEIBS, University of Exeter, Renmin University of China, Fudan University, Shanghai JiaoTong University, University of Hong Kong, WHU.
Awards: 2021 Northern Finance Association Meetings Best Ph.D Student Paper Award
2020 WFA Cubist Systematic Strategic Ph.D Candidate Award for Outstanding Research
Abstract: Revisions of consensus forecasts of macroeconomic variables positively predict announcement-day forecast errors, whereas stock market returns on forecast revision days predict announcement-day returns in the opposite direction. A dynamic noisy rational expectations model with periodic macroeconomic announcements quantitatively accounts for these findings. Under asymmetric information, average beliefs are not Bayesian: they underweight new information and positively predict subsequent belief errors. In addition, stock prices are partially driven by noise. Noise accumulates into stock prices on revision days but gets corrected upon announcements. Therefore, price changes on revision days negatively predict announcement-day returns.
Presented at: American Economic Association 2021, Western Finance Association 2020, Financial Intermediation Research Society Conference 2021, Northern Finance Association 2021, Econometric Society World Congress 2020, European Economic Association 2020, European Finance Association Poster 2020, China International Risk Forum 2020, Carnegie Mellon University, New York Fed, University of Toronto, University of California San Diego, University of Wisconsin–Madison, Boston University, University of Warwick, Central European University, Bank for International Settlements, Singapore Management University, New York University Shanghai, City University of London Cass Business School, Erasmus University Rotterdam, CEIBS, University of Exeter, Renmin University of China, Fudan University, Shanghai JiaoTong University, University of Hong Kong, WHU.
Ambiguity, Information Processing, and Financial Intermediation (with Kenneth Kasa and Yulei Luo) [Online Appendix] Revise & Resubmit at the Journal of Economic Theory
Abstract: This paper provides a micro-foundation for intermediation by incorporating ambiguity and information processing constraints into the He and Krishnamurthy (2012) model of intermediary asset pricing. Financial intermediaries possess greater information processing capacity than households, who optimally choose to delegate their investment decisions because they are less efficient in processing information. We show that investor ambiguity aversion tightens the capital constraint that arises from the intermediary's moral hazard problem, and amplifies the impact of financial intermediation on equilibrium asset prices. The calibrated model can quantitatively explain both the unconditional and time-varying moments of asset returns in the data.
Presented at: SFS Cavalcade Asia-Pacific 2019, China International Conference in Macroeconomics 2019, Summer Institute of Finance Conference 2019, American Economic Association Poster Session 2019, Asian Meeting of the Econometric Society 2019, 31st Australasian Finance and Banking Conference 2018, New York University, University of Hong Kong.
Abstract: This paper provides a micro-foundation for intermediation by incorporating ambiguity and information processing constraints into the He and Krishnamurthy (2012) model of intermediary asset pricing. Financial intermediaries possess greater information processing capacity than households, who optimally choose to delegate their investment decisions because they are less efficient in processing information. We show that investor ambiguity aversion tightens the capital constraint that arises from the intermediary's moral hazard problem, and amplifies the impact of financial intermediation on equilibrium asset prices. The calibrated model can quantitatively explain both the unconditional and time-varying moments of asset returns in the data.
Presented at: SFS Cavalcade Asia-Pacific 2019, China International Conference in Macroeconomics 2019, Summer Institute of Finance Conference 2019, American Economic Association Poster Session 2019, Asian Meeting of the Econometric Society 2019, 31st Australasian Finance and Banking Conference 2018, New York University, University of Hong Kong.
Information Acquisition and the Pre-Announcement Drift (with Hengjie Ai and Ravi Bansal) [Slides]
Abstract: We present a dynamic Grossman-Stiglitz model with endogenous information acquisition to explain the pre-FOMC announcement drift. Because FOMC announcements reveal substantial information about the economy, investors' incentives to acquire information are particularly strong days ahead of the announcements. Information acquisition partially resolves the uncertainty for uninformed traders. Under generalized risk sensitive preferences (Ai and Bansal, 2018), resolution of uncertainty is associated with realizations of risk premium, generating a pre-FOMC announcement drift. Because our theory does not rely on leakage of information, it can simultaneously explain the high average return and the low realized volatility during the pre-FOMC announcement period.
Presented at: NBER Summer Institute Capital Markets and the Economy, Adam Smith Workshop 2022, 6th Annual Young Scholars Finance Consortium, 7th Annual University of Connecticut Finance Conference, Western Finance Association 2021, European Finance Association 2021, Northern Finance Association 2021, Society for Economic Dynamics 2021*, China International Conference in Finance 2021, China International Conference in Macroeconomics 2021, Midwest Finance Association 2021*.
Abstract: We present a dynamic Grossman-Stiglitz model with endogenous information acquisition to explain the pre-FOMC announcement drift. Because FOMC announcements reveal substantial information about the economy, investors' incentives to acquire information are particularly strong days ahead of the announcements. Information acquisition partially resolves the uncertainty for uninformed traders. Under generalized risk sensitive preferences (Ai and Bansal, 2018), resolution of uncertainty is associated with realizations of risk premium, generating a pre-FOMC announcement drift. Because our theory does not rely on leakage of information, it can simultaneously explain the high average return and the low realized volatility during the pre-FOMC announcement period.
Presented at: NBER Summer Institute Capital Markets and the Economy, Adam Smith Workshop 2022, 6th Annual Young Scholars Finance Consortium, 7th Annual University of Connecticut Finance Conference, Western Finance Association 2021, European Finance Association 2021, Northern Finance Association 2021, Society for Economic Dynamics 2021*, China International Conference in Finance 2021, China International Conference in Macroeconomics 2021, Midwest Finance Association 2021*.
Information-Driven Volatility (with Hengjie Ai and Lai Xu) [Slides]
Abstract: Standard asset pricing models with stochastic volatility predict a robust positive relationship between past realized volatility and future expected returns. Empirical work typically finds this relationship to be negative. We develop an asset pricing model where stock market volatility dynamics are driven by information. We show that under strong generalized risk sensitivity of preferences, information-driven volatility induces a negative correlation between past realized volatility and future expected returns. We provide empirical evidence for the unique implications of the information-driven volatility channel and demonstrate that our model can quantitatively replicate the evidence.
Presented at: Western Finance Association 2022, Society for Economic Dynamics 2022*, Midwest Finance Association 2022, Canadian Derivatives Institute 2021*, 8th SAFE Asset Pricing Workshop*, 2021 Conference and JEDC Special Issue on Markets and Economies with Information Frictions, China International Risk Forum 2021, Sun Yat-sen University, University of Washington*, UT Dallas*, University of Minnesota*, University of Wisconsin–Madison*, Tsinghua University PBCSF*, University of Oklahoma*, University of Manitoba*.
Abstract: Standard asset pricing models with stochastic volatility predict a robust positive relationship between past realized volatility and future expected returns. Empirical work typically finds this relationship to be negative. We develop an asset pricing model where stock market volatility dynamics are driven by information. We show that under strong generalized risk sensitivity of preferences, information-driven volatility induces a negative correlation between past realized volatility and future expected returns. We provide empirical evidence for the unique implications of the information-driven volatility channel and demonstrate that our model can quantitatively replicate the evidence.
Presented at: Western Finance Association 2022, Society for Economic Dynamics 2022*, Midwest Finance Association 2022, Canadian Derivatives Institute 2021*, 8th SAFE Asset Pricing Workshop*, 2021 Conference and JEDC Special Issue on Markets and Economies with Information Frictions, China International Risk Forum 2021, Sun Yat-sen University, University of Washington*, UT Dallas*, University of Minnesota*, University of Wisconsin–Madison*, Tsinghua University PBCSF*, University of Oklahoma*, University of Manitoba*.
(*presented by co-authors)