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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
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
Financial Shocks in an Uncertain Economy
The past 15 years have been eventful. The Global Financial Crisis (GFC) reminded us of the importance of a stable financial system to a well-functioning economy, one with low and stable inflation and maximum employment. Given the recent banking stress, we ponder this issue again. The pandemic was a huge shock surrounded by much uncertainty, making precise forecasts within traditional models difficult. And more recently, there has been continuous talk of a soft landing and recession risks.In this paper, I focus on some of the lessons we have learned over the years: (i) uncertainty and tail ...
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
A New Model of Inflation, Trend Inflation, and Long-Run Inflation Expectations
A knowledge of the level of trend inflation is key to many current policy decisions, and several methods of estimating trend inflation exist. This paper adds to the growing literature which uses survey-based long-run forecasts of inflation to estimate trend inflation. We develop a bivariate model of inflation and long-run forecasts of inflation which allows for the estimation of the link between trend inflation and the long-run forecast. Thus, our model allows for the possibilities that long-run forecasts taken from surveys can be equated with trend inflation, that the two are completely ...
Report
Rare shocks, great recessions
We estimate a DSGE model where rare large shocks can occur, by replacing the commonly used Gaussian assumption with a Student?s t distribution. Results from the Smets and Wouters (2007) model estimated on the usual set of macroeconomic time series over the 1964-2011 period indicate that 1) the Student?s t specification is strongly favored by the data even when we allow for low-frequency variation in the volatility of the shocks and 2) the estimated degrees of freedom are quite low for several shocks that drive U.S. business cycles, implying an important role for rare large shocks. This result ...
Working Paper
Assessing International Commonality in Macroeconomic Uncertainty and Its Effects
This paper uses a large vector autoregression (VAR) to measure international macroeconomic uncertainty and its effects on major economies, using two datasets, one with GDP growth rates for 19 industrialized countries and the other with a larger set of macroeconomic indicators for the U.S., euro area, and U.K. Using basic factor model diagnostics, we first provide evidence of significant commonality in international macroeconomic volatility, with one common factor accounting for strong comovement across economies and variables. We then turn to measuring uncertainty and its effects with a large ...
Working Paper
Measuring Uncertainty and Its Impact on the Economy
We propose a new framework for measuring uncertainty and its effects on the economy, based on a large VAR model with errors whose stochastic volatility is driven by two common unobservable factors, representing aggregate macroeconomic and financial uncertainty. The uncertainty measures can also influence the levels of the variables so that, contrary to most existing measures, ours reflect changes in both the conditional mean and volatility of the variables, and their impact on the economy can be assessed within the same framework. Moreover, identification of the uncertainty shocks is ...
Working Paper
Addressing COVID-19 Outliers in BVARs with Stochastic Volatility
The COVID-19 pandemic has led to enormous movements in economic data that strongly affect parameters and forecasts obtained from standard VARs. One way to address these issues is to model extreme observations as random shifts in the stochastic volatility (SV) of VAR residuals. Specifically, we propose VAR models with outlier-augmented SV that combine transitory and persistent changes in volatility. The resulting density forecasts for the COVID-19 period are much less sensitive to outliers in the data than standard VARs. Evaluating forecast performance over the last few decades, we find that ...
Working Paper
Measuring Uncertainty and Its Effects in the COVID-19 Era
We measure the effects of the COVID-19 outbreak on macroeconomic and financial uncertainty, and we assess the consequences of the latter for key economic variables. We use a large, heteroskedastic vector autoregression (VAR) in which the error volatilities share two common factors, interpreted as macro and financial uncertainty, in addition to idiosyncratic components. Macro and financial uncertainty are allowed to contemporaneously affect the macroeconomy and financial conditions, with changes in the common component of the volatilities providing contemporaneous identifying information on ...