Drew D. Creal

University of Chicago
Booth School of Business
5807 South Woodlawn Ave
Chicago, IL 60637

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NBER Working Papers and Publications

April 2016Bond Risk Premia in Consumption-based Models
with Jing Cynthia Wu: w22183
Workhorse Gaussian affine term structure models (ATSMs) attribute time-varying bond risk premia entirely to changing prices of risk, while structural models with recursive preferences credit it completely to stochastic volatility. We reconcile these competing channels by introducing a novel form of external habit into an otherwise standard model with recursive preferences. The new model has an ATSM representation with analytical bond prices making it empirically tractable. We find that time variation in bond term premia is predominantly driven by the price of risk, especially, the price of expected inflation risk that co-moves with expected inflation itself.
October 2014Monetary Policy Uncertainty and Economic Fluctuations
with Jing Cynthia Wu: w20594
We investigate the relationship between uncertainty about monetary policy and its transmission mechanism, and economic fluctuations. We propose a new term structure model where the second moments of macroeconomic variables and yields can have a first-order effect on their dynamics. The data favors a model with two unspanned volatility factors that capture uncertainty about monetary policy and the term premium. Uncertainty contributes negatively to economic activity. Two dimensions of uncertainty react in opposite directions to a shock to the real economy, and the response of inflation to uncertainty shocks vary across different historical episodes.

Published: Drew D. Creal & Jing Cynthia Wu, 2017. "MONETARY POLICY UNCERTAINTY AND ECONOMIC FLUCTUATIONS," International Economic Review, vol 58(4), pages 1317-1354.

May 2014Estimation of Affine Term Structure Models with Spanned or Unspanned Stochastic Volatility
with Jing Cynthia Wu: w20115
We develop new procedures for maximum likelihood estimation of affine term structure models with spanned or unspanned stochastic volatility. Our approach uses linear regression to reduce the dimension of the numerical optimization problem yet it produces the same estimator as maximizing the likelihood. It improves the numerical behavior of estimation by eliminating parameters from the objective function that cause problems for conventional methods. We find that spanned models capture the cross-section of yields well but not volatility while unspanned models fit volatility at the expense of fitting the cross-section.

Published: Creal, Drew D. & Wu, Jing Cynthia, 2015. "Estimation of affine term structure models with spanned or unspanned stochastic volatility," Journal of Econometrics, Elsevier, vol. 185(1), pages 60-81. citation courtesy of

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