Risk Matters: The Real Effects of Volatility Shocks

Abstract

This paper shows how changes in the volatility of the real interest rate at which small open emerging economies borrow have a quantitatively important e¤ect on real variables like output, consumption, investment, and hours worked. To motivate our investigation, we document the strong evidence of time-varying volatility in the real interest rates faced by a sample of four emerging small open economies: Argentina, Ecuador, Venezuela, and Brazil. We postulate a stochastic volatility process for real interest rates using T-bill rates and country spreads and estimate it with the help of the Particle …lter and Bayesian methods. Then, we feed the estimated stochastic volatility process for real interest rates in an otherwise standard small open economy business cycle model. We calibrate eight versions of our model to match basic aggregate observations, two versions for each of the four countries in our sample. We …nd that an increase in real interest rate volatility triggers a fall in output, consumption, investment, and hours worked, and a notable change in the current account of the economy.

Why Do Emerging Economies Borrow Short Term?

“…31 31 See Neumeyer and Perri (2005), Oviedo (2005), Uribe and Yue (2006), Chang and Fernández (2010), and Fernández‐Villaverde et al (2011). …”

A Moment‐matching Method for Approximating Vector Autoregressive Processes by Finite‐state Markov Chains

“…The normality (and log‐normality, in the case of modeling shocks with stochastic volatility as in Fernandez‐Villaverde et al ., , and Caldara et al ., ) assumption is routinely used in describing the properties of the shocks in macroeconomic models. Nevertheless, in some applications, the normality assumption of the error term in equation may seem restrictive.…”

Non‐linear Dsge Models and the Central Difference Kalman Filter

“…The approach relies on the Kalman filter to evaluate the log‐likelihood function in closed form when shocks and measurement errors are Gaussian. However, a linearized solution may not always be sufficiently accurate, for instance, if one is interested in welfare comparison across policy regimes, time‐varying risk premia, or effects of uncertainty shocks where more accurate higher‐order approximations are needed (see Kim and Kim, ; Fernández‐Villaverde et al ., ; Rudebusch and Swanson, ; among others). Unfortunately, a closed‐form solution for the log‐likelihood function does not exist when DSGE models are solved with non‐linear terms or have non‐Gaussian shocks.…”