Working Papers

• Targeted Testing of Dynamic Stochastic General Equilibrium Models, (with Denis Tkachenko), October 2023.
  Abstract: This paper presents targeted tests for assessing the specifications of Dynamic Stochastic General Equilibrium (DSGE) models, focusing on specific aspects such as a model’s steady state properties, dynamic properties, and properties in selected frequency bands, such as business cycle frequencies. The proposed tests can help identify variables that contribute to misspecification. The framework addresses issues of indeterminacy and weak identification. Results show that a small-scale DSGE model is rejected over the period of 1960-2007, indicating issues related to inflation dynamics and comovements between variables over business cycle frequencies, but is not rejected in subsamples when a regime change is allowed in 1979. The Smets-Wouters model is not rejected over the same period with limitations in capturing inflation dynamics. Additionally, a medium-scale model with news shocks is rejected, and issues related to hours worked are reported. The proposed methods are applicable to Gaussian (factor-augmented) Vector Autoregressions.
  
  
• Estimating State Price Densities Implied by American Options, (with Guang Zhang), November 2023.
  Abstract: We introduce a new method to estimate state price densities implicit in American-style options, which are financial contracts granting holders the right to exercise the option at any time before expiration. The method involves estimating the parameters of a Gauss-Hermite series expansion and solving a sequence of recursive equations for the early exercise premium. The resulting estimator can capture sudden shifts in density that may occur during financial crises or in response to significant policy events. It also provides an estimate of the early exercise premium that is of independent interest. We illustrate the proposed method using calibrated simulations and empirical applications. Our findings indicate that the state price densities implied by S&P 500 ETF options can predict future returns up to a one-year horizon for the period 2009-2023.