Asset Pricing with Learning about Disaster Risk (with Michael Siemer)
(NEW! January, 2012)
Abstract:
This paper studies asset pricing in an endowment economy with rare disasters. Existing literature on rare disaster models generally assumes complete information about disasters. This literature is able to match a large range of asset pricing moments but can only generate time-varying risk premia under the assumption of exogenous variation in disaster probability. We extend this literature to allow for two sources of uncertainty about a rare disaster: (1) the lack of historical data for a rare disaster results in unknown parameters of the disaster process; (2) the occurrence of a rare disaster takes time to unfold and is thus unobservable directly. We show that when agents employ Bayesian learning rules, learning endogenously introduces time-varying risk premia: Time variation of beliefs generates time variation in returns and the model can hence better explain large stock market movements during recessions even in the absence of disasters. Feeding U.S. consumption data of the 20th century into the model shows that the model improves significantly on matching equity returns relative to a model without learning and illustrates how the disaster belief varies over time. The framework allows us to reconcile the widely held belief during the recent financial crisis that the economy might be at the onset of another great depression.
Optimal Policy with Credibility Concerns
(Technical Appendix)
(Updated! April 2012, Revised & Resubmitted to Journal of Economic Theory)
Abstract:
This paper considers a reputation model of optimal taxation in which the public is unsure about the government type. A long-lived government can be of the trustworthy type (that commits to its announced tax rate) or the opportunistic type (that retains the ability to change the tax rate after announcing it). This paper determines the optimal commitment strategy for the trustworthy government -- most of prior studies treat that strategy as exogenous -- and finds that allowing the committed strategy to be chosen optimally has significant consequences for the equilibrium dynamics. The optimal committed strategy is found to vary with the patience of the two government types, the initial reputation of the government, and the elasticity of household production. This formulation can thus explain differences in policy responses across governments in the face of similar credibility problems.
Managing Expectations (working paper version, May, 2008; with Robert G. King and Ernesto S. Pasten)
(Journal of Money, Credit and Banking, Vol 40, Issue 8, 1625-1666)
Abstract:
The idea that monetary policy is principally about "managing expectations" has taken hold in central banks around the world. Discussions of expectations management by central bankers, academics and by financial market participants frequently also include the idea that central bank credibility is imperfect. We adapt a familiar macroeconomic model so as to discuss key concepts in the area of expectations management. Our work also exemplifies a model construction approach to analyzing the dynamics of announcements, actions and credibility which we think makes feasible a wide range of future investigations concerning the management of expectations.
Coordinating Expectations and the Informational Role of Policy (June, 2009; with Ernesto S. Pasten)
Abstract:
An informational role of policy arises in economies where large
fluctuations are triggered by self-fulfilling expectation switches between
efficient "optimism" and inefficient "pessimism," a feature
that is common in many dynamic economies with coordination failures. Policy
affects the information about underlying fundamentals contained in aggregate
outcomes, and thus affects the timing of switches and expectations of future
switches. We use a problem of optimal taxation on labor income as a laboratory
to study this role of policy from a positive and a normative perspective. Our
main result is that a stabilization policy is ineffective after an expectation
switch. Instead, policy should anticipate switches with small permanent tax
cuts to extend "optimism" and severe transitory tax cuts to break
"pessimism." These tax cuts should be reverted once a switch is
triggered, when policy must focus on its short run objectives.
Modeling and Forecasting Stock Return Volatility Using a Random Level Shift Model (August, 2009; with Pierre Perron)
(Journal of Empirical Finance, Vol. 17, Issue 1, 138-156)
Abstract:
We consider the estimation of a random level shift model for which the series of interest is the sum of a short memory process and a jump or level shift component. For the latter component, we specify the commonly used simple mixture model such that the component is the cumulative sum of a process which is 0 with some probability (1-¦Á) and is some random variable with probability ¦Á. Our estimation method transforms such a model into a linear state space form with mixture of normal innovations, so that an extension of Kalman filter algorithm can be applied. We estimate this random level shifts models for volatility series, proxied by the logarithm of the absolute returns. We do this for the S&P 500, AMEX, Dow Jones and the NASDAQ stock market return indices. Our point estimates imply few level shifts for all series. But once these are taken into account, there is little evidence of serial correlation in the remaining noise and, hence, no evidence of long memory. Once the estimated shifts are introduced to a standard GARCH model, any evidence of GARCH effects disappears. We also produce rolling out-of-sample forecasts. In most cases, our simple random level shift model clearly outperforms a standard GARCH(1,1) model and, in many cases, it also provides better forecasts than a fractionally integrated GARCH model.