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 negatively predict announcement day returns. 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 partly driven by noise, and therefore negatively predict returns on announcement days when noise is revealed and the market corrects itself.
Presented at: American Economic Association 2021, Western Finance Association 2020, Financial Intermediation Research Society Conference 2021, Northern Finance Association 2021, Society World Congress 2020, European Economic Association 2020, European Finance Association Poster 2020, China International Risk Forum 2020, American Finance Association Poster 2021 (accepted), University of Hong Kong, 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 Jiao Tong University, 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 negatively predict announcement day returns. 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 partly driven by noise, and therefore negatively predict returns on announcement days when noise is revealed and the market corrects itself.
Presented at: American Economic Association 2021, Western Finance Association 2020, Financial Intermediation Research Society Conference 2021, Northern Finance Association 2021, Society World Congress 2020, European Economic Association 2020, European Finance Association Poster 2020, China International Risk Forum 2020, American Finance Association Poster 2021 (accepted), University of Hong Kong, 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 Jiao Tong University, WHU.
Ambiguity, Information Processing, and Financial Intermediation (with Kenneth Kasa and Yulei Luo) [Online Appendix] [Slides]
Abstract: This paper incorporates ambiguity and information processing constraints into a model of intermediary asset pricing. Financial intermediaries are assumed to possess greater information processing capacity. Households purchase this capacity, and then delegate their investment decisions to intermediaries. As in He and Krishnamurthy (2012), the delegation contract is constrained by a moral hazard problem, which gives rise to a minimum capital requirement. Both households and intermediaries have a preference for robustness, reflecting ambiguity about asset returns (Hansen and Sargent (2008)). We show that ambiguity aversion tightens the capital constraint, and amplifies its effects. Detection error probabilities are used to discipline the degree of ambiguity aversion. The model can explain both the unconditional moments of asset returns and their state dependence, even with DEPs in excess of 20%.
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 incorporates ambiguity and information processing constraints into a model of intermediary asset pricing. Financial intermediaries are assumed to possess greater information processing capacity. Households purchase this capacity, and then delegate their investment decisions to intermediaries. As in He and Krishnamurthy (2012), the delegation contract is constrained by a moral hazard problem, which gives rise to a minimum capital requirement. Both households and intermediaries have a preference for robustness, reflecting ambiguity about asset returns (Hansen and Sargent (2008)). We show that ambiguity aversion tightens the capital constraint, and amplifies its effects. Detection error probabilities are used to discipline the degree of ambiguity aversion. The model can explain both the unconditional moments of asset returns and their state dependence, even with DEPs in excess of 20%.
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, investor's incentives to acquire information are particularly strong in days ahead of announcements. Information acquisition partially resolves the uncertainty for uninformed traders, and under generalized risk sensitive preferences (Ai and Bansal (2018)), lowers the discount rate and results in a stock market run-up. Because our theory does not rely on the leakage of information, it can simultaneously explain the low realized volatility during the pre-FOMC announcement period and the lack of predictability of announcement return by pre-announcement drifts.
Presented at: 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, investor's incentives to acquire information are particularly strong in days ahead of announcements. Information acquisition partially resolves the uncertainty for uninformed traders, and under generalized risk sensitive preferences (Ai and Bansal (2018)), lowers the discount rate and results in a stock market run-up. Because our theory does not rely on the leakage of information, it can simultaneously explain the low realized volatility during the pre-FOMC announcement period and the lack of predictability of announcement return by pre-announcement drifts.
Presented at: 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: Modern asset pricing theory predicts an unambiguously positive relation between volatility and expected returns. Empirically, however, realized volatility often predicts expected returns with a negative sign, as exemplified by the volatility-managed portfolios of Moreira and Muir (2017). We show that information-driven volatility induces negatively correlation between past realized volatility and future volatility and future expected returns. We develop a simple asset pricing model based on this intuition and demonstrate that our model can account for several volatility-related asset pricing puzzles such as the return on volatility managed portfolios, the “variance risk premium” return predictability (Bollerslev, Tauchen, and Zhou, 2009), and the predictability of returns by implied volatility reduction on macroeconomic announcement days.
Presented at: Midwest Finance Association 2022(scheduled), 2021 Canadian Derivatives Institute, 8th SAFE Asset Pricing Workshop, 2021 Conference and JEDC Special Issue on Markets and Economies with Information Frictions, China International Risk Forum 2021, University of Washington*, UT Dallas*, University of Minnesota*, University of Wisconsin–Madison*, Tsinghua University PBCSF*, University of Oklahoma*, University of Manitoba*.
Abstract: Modern asset pricing theory predicts an unambiguously positive relation between volatility and expected returns. Empirically, however, realized volatility often predicts expected returns with a negative sign, as exemplified by the volatility-managed portfolios of Moreira and Muir (2017). We show that information-driven volatility induces negatively correlation between past realized volatility and future volatility and future expected returns. We develop a simple asset pricing model based on this intuition and demonstrate that our model can account for several volatility-related asset pricing puzzles such as the return on volatility managed portfolios, the “variance risk premium” return predictability (Bollerslev, Tauchen, and Zhou, 2009), and the predictability of returns by implied volatility reduction on macroeconomic announcement days.
Presented at: Midwest Finance Association 2022(scheduled), 2021 Canadian Derivatives Institute, 8th SAFE Asset Pricing Workshop, 2021 Conference and JEDC Special Issue on Markets and Economies with Information Frictions, China International Risk Forum 2021, 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)