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Working Paper
Endogenous Uncertainty
We show that macroeconomic uncertainty can be considered as exogenous when assessing its effects on the U.S. economy. Instead, financial uncertainty can at least in part arise as an endogenous response to some macroeconomic developments, and overlooking this channel leads to distortions in the estimated effects of financial uncertainty shocks on the economy. We obtain these empirical findings with an econometric model that simultaneously allows for contemporaneous effects of both uncertainty shocks on economic variables and of economic shocks on uncertainty. While the traditional econometric ...
Working Paper
Sequential Bayesian Inference for Vector Autoregressions with Stochastic Volatility
We develop a sequential Monte Carlo (SMC) algorithm for Bayesian inference in vector autoregressions with stochastic volatility (VAR-SV). The algorithm builds particle approximations to the sequence of the model’s posteriors, adapting the particles from one approximation to the next as the window of available data expands. The parallelizability of the algorithm’s computations allows the adaptations to occur rapidly. Our particular algorithm exploits the ability to marginalize many parameters from the posterior analytically and embeds a known Markov chain Monte Carlo (MCMC) algorithm for ...
Working Paper
Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors
We develop uncertainty measures for point forecasts from surveys such as the Survey of Professional Forecasters, Blue Chip, or the Federal Open Market Committee?s Summary of Economic Projections. At a given point of time, these surveys provide forecasts for macroeconomic variables at multiple horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. Compared to constant-variance approaches, our stochastic-volatility model improves the accuracy of uncertainty measures for survey forecasts.
Working Paper
The Effects of Asymmetric Volatility and Jumps on the Pricing of VIX Derivatives
This paper proposes a new collection of affine jump-diffusion models for the valuation of VIX derivatives. The models have two distinctive features. First, we allow for a positive correlation between changes in the VIX and in its stochastic volatility to accommodate asymmetric volatility. Second, upward and downward jumps in the VIX are separately modeled to accommodate the possibility that investors react differently to good and bad surprises. Using the VIX futures and options data from July 2006 through January 2013, we find conclusive evidence for the benefits of including both asymmetric ...
Working Paper
A New Way to Quantify the Effect of Uncertainty
This paper develops a new way to quantify the effect of uncertainty and other higher-order moments. First, we estimate a nonlinear model using Bayesian methods with data on uncertainty, in addition to common macro time series. This key step allows us to decompose the exogenous and endogenous sources of uncertainty, calculate the effect of volatility following the cost of business cycles literature, and generate data-driven policy functions for any higherorder moment. Second, we use the Euler equation to analytically decompose consumption into several terms--expected consumption, the ex-ante ...
Working Paper
Modeling Time-Variation Over the Business Cycle (1960-2017): An International Perspective
In this paper, I explore the changes in international business cycles with quarterly data for the eight largest advanced economies (U.S., U.K., Germany, France, Italy, Spain, Japan, and Canada) since the 1960s. Using a time-varying parameter model with stochastic volatility for real GDP growth and inflation allows their dynamics to change over time, approximating nonlinearities in the data that otherwise would not be adequately accounted for with linear models (Granger et al. (1991), Granger (2008)). With that empirical model, I document a period of declining macro volatility since the 1980s, ...
Working Paper
Measuring Inflation Anchoring and Uncertainty : A US and Euro Area Comparison
We use several US and euro-area surveys of professional forecasters to estimate a dynamic factor model of inflation featuring time-varying uncertainty. We obtain survey-consistent distributions of future inflation at any horizon, both in the US and the euro area. Equipped with this model, we propose a novel measure of the anchoring of inflation expectations that accounts for inflation uncertainty. Our results suggest that following the Great Recession, inflation anchoring improved in the US, while mild de-anchoring occurred in the euro-area. As of our sample end, both areas appear to be ...
Working Paper
Measurement Errors and Monetary Policy: Then and Now
Should policymakers and applied macroeconomists worry about the difference between real-time and final data? We tackle this question by using a VAR with time-varying parameters and stochastic volatility to show that the distinctionbetween real-time data and final data matters for the impact of monetary policy shocks: The impact on final data is substantially and systematically different (in particular, larger in magnitude for different measures of real activity) from theimpact on real-time data. These differences have persisted over the last 40 years and should be taken into account when ...
Working Paper
Understanding the Aggregate Effects of Credit Frictions and Uncertainty
We examine the interaction of uncertainty and credit frictions in a New Keynesian framework. To do so, uncertainty is modeled as time-varying stochastic volatitlity - the product of monetary policy uncertainty, financial risk (micro-uncertainty), and macrouncertainty. The model is solved using a pruned third-order approximation and estimated by the Simulated Method of Moments. We find that: 1) Micro-uncertainty aggravates the information asymmetry between lenders and borrowers, worsens credit conditions, and has first-order effects on real economic activity. 2) When credit conditions are ...
Working Paper
Does Realized Volatility Help Bond Yield Density Prediction?
We suggest using "realized volatility" as a volatility proxy to aid in model-based multivariate bond yield density forecasting. To do so, we develop a general estimation approach to incorporate volatility proxy information into dynamic factor models with stochastic volatility. The resulting model parameter estimates are highly efficient, which one hopes would translate into superior predictive performance. We explore this conjecture in the context of density prediction of U.S. bond yields by incorporating realized volatility into a dynamic Nelson-Siegel (DNS) model with stochastic ...