Can We Use High-frequency Yield Data to Better Understand the Effects of Monetary Policy and Its Communication? Yes and No!

Abstract

Understanding the effects of monetary policy and its communication is crucial for a central bank. This paper explores a new approach to identifying the effects of monetary policy using high-frequency data around monetary policy decisions and other announcements that allows us to explore different facets of monetary policy, specifically: current policy action; signalling or forward guidance about future rates; and the effect on uncertainty and term premia. The approach provides an intuitive lens through which to understand how policy and its communication affected expectations for rates and risks during certain historical periods, and more generally. For example, it suggests that: (i) signalling/forward guidance shocks tended to raise expected future policy rates in the mid-2010s as the RBA highlighted rising risks in housing markets; (ii) COVID-19-era monetary policy worked mainly through affecting term premia rather than expectations for future policy rates, unlike pre-COVID-19 policy; and (iii) shocks to the expected path of rates are predictable based on data available at the time, which suggests that markets systematically misunderstand how the RBA reacts to data, highlighting the importance of clear communication. We also explore the macroeconomic effects of these different shocks. The effects of shocks to current policy are similar to those estimated in previous papers, and existing issues such as the 'price puzzle' remain, while the effects of other shocks are imprecisely estimated. Although the approach provides little new information on the macroeconomic effects of monetary policy, it does highlight the importance of these other facets of policy in moving interest rates and suggests additional work in this space could be valuable.

Does Monetary Policy Affect Non-mining Business Investment in Australia? Evidence from BLADE

“…As a final robustness check, we also consider a shock measure derived from high-frequency changes in yields around the time of the RBA's monetary policy announcements (30 minutes before and 90 minutes after) in our microdata regressions. In particular, we use the level shock from Hambur and Haque (2023), which focuses on high-frequency changes in the policy rate. 6 Doing so yields a similar profile for the response of investment to monetary policy, but the size of the response is much smaller and there is less evidence of a significant effect (Figure C7).…”

Do Monetary Policy and Economic Conditions Impact Innovation? Evidence from Australian Administrative Data

“…Interestingly, the evidence for the levels shock is qualitatively different, with some evidence of a decline in innovation for large firms. However, given the shortcomings with this measure discussed in Hambur and Haque (2023), the results from the Romer and Romer-style shocks remain our preferred estimates.…”

“…We adopt this measure as our preferred shock measure because previous research found that it is able to overcome the so-called price puzzle in Australian data: that contractionary monetary policy is often estimated to raise prices. This is not the case for simpler Romer and Romer-style shocks (Bishop and Tulip 2017) or measures based on high-frequency changes in bond yields over a 90minute window around announcements (Hambur and Haque 2023). We nonetheless consider these other measures for robustness in Tables B1-B4.…”

“…• A measure based on high-frequency changes in bond yields based on a 90-minute window around announcements (Hambur and Haque 2023), which we call 'levels shock'.…”