We develop and implement a new method for maximum likelihood estimation in closed-form of stochastic volatility models … unobservable volatility state, to an approximate likelihood procedure where the volatility state is replaced by the implied … volatility of a short dated at-the-money option. We find that the approximation results in a negligible loss of accuracy. We …

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 …

The estimation of racial disparities in various fields is often hampered by the lack of individual-level racial … Instrumental Regression for Disparity Estimation (BIRDiE), that take BISG probabilities as inputs and produce racial disparity … estimates by using surnames as an instrumental variable for race. Our estimation method is scalable, making it possible to …

At the zero lower bound, the central bank's inability to offset shocks endogenously generates volatility. In this …-contingent optimal monetary and fiscal policies can attenuate this endogenous volatility by stabilizing the distribution of future … outcomes. Fluctuations in uncertainty and the zero lower bound help our model match the unconditional and stochastic volatility …

the mean and volatility of equity returns. Our model assumes a small risk of a rare disaster that is calibrated based on … the international data on large consumption declines. We allow the risk of this rare disaster to be stochastic, which … turns out to be crucial to the model's ability to explain both equity volatility and option prices. We explore different …