Monetary Policy, Real Activity, and Credit Spreads: Evidence from Bayesian Proxy SVARs

Abstract

This paper studies the interaction between monetary policy, financial markets, and the real economy. We develop a Bayesian framework to estimate proxy structural vector autoregressions (SVARs) in which monetary policy shocks are identified by exploiting the information contained in high frequency data. For the Great Moderation period, we find that monetary policy shocks are key drivers of fluctuations in industrial output and corporate credit spreads, explaining about 20 percent of the volatility of these variables. Central to this result is a systematic component of monetary policy characterized by a direct and economically significant reaction to changes in credit spreads. We show that the failure to account for this endogenous reaction induces an attenuation bias in the response of all variables to monetary shocks.

The Interplay between Financial Conditions and Monetary Policy Shocks

“…While monetary policy is slightly more powerful on output (a one standard-deviation FFR shock is equivalent to a 1.15 FC shock), the reverse is true for investment (where a standardized FC shock is 30% more powerful). 9 This discrepancy is consistent with the view that the excess bond premium captures conditions that are particularly relevant for the corporate sector and thus impacts 8 Impulse response functions for the other variables in Y are in Figure 2. For log-GDP, log-BFI, logconsumption, and the excess market return, we cumulate the responses over time.…”

“…Their paper is concerned about monetary policy shocks alone and does not identify financial shocks. In a recent working paper, Caldara and Herbst [9] use Gertler and Karadi's approach in a Bayesian SVAR that includes financial shocks; similarly to us, they find that there is a systematic response of monetary policy to financial shocks, but they do not consider the potential implications when monetary policy is not allowed to respond. 5 See e.g.…”

The Time-Varying Effect of Monetary Policy on Asset Prices

“…Gertler and Karadi (2015) and Caldara and Herbst (2016) apply this method in the context of monetary policy identification. Both approaches consistently estimate the true relative impulse responses.…”

Inferring the Shadow Rate from Real Activity

“…7 Papers using high frequency identification have found insignificant contemporaneous effects on real variables, validating the use of Cholesky identification (Gertler and Karadi, 2015;Caldara and Herbst, 2018).…”

“…19 In addition, the shadow rate estimate is somewhat more accommodative than in the baseline estimation. This is not surprising given the findings of Caldara and Herbst (2018). If there is a systematic (negative) response of monetary policy to financial spreads, not accounting for this could bias the estimated response of the real sector to the federal funds rate, and thus the estimated shadow rate, confounding the effects of higher than usual spreads with contractive monetary policy.…”