RDP 2023-04: Can We Use High-frequency Yield Data to Better Understand the Effects of Monetary Policy and Its Communication? Yes and No! 1. Introduction

To set monetary policy appropriately, it is important for the central bank to understand how monetary policy affects the economy. However, it can be difficult to quantitatively assess the effects of monetary policy. One reason is that monetary policy is not set randomly, but rather in response to expected future economic outcomes. For instance, if the central bank forecasts that inflation and activity are going to increase, it will tend to raise rates to keep inflation from rising too far. As such, it could look like inflation is rising in response to higher interest rates, but in fact interest rates are responding to future (expected) inflation: the relationship is endogenous. A second complicating factor is that central banks not only affect the economy by changing current short-term interest rates, but also through their communication about current and potential future policy. However, quantifying the nature and effects of communication is difficult. Moreover, ‘unconventional’ policies such as the yield curve targeting and bond purchase programs used during the COVID-19 pandemic work by affecting longer-term interest rates and expectations, and not just current short-term interest rates.

To get around the first issue, economists often try to identify monetary policy ‘shocks’. These are ‘exogenous’ changes in policy that are unrelated to current and expected economic conditions. As such, these shocks should give a cleaner read of the causal effects of monetary policy on the economy compared to simply looking at rate changes. There is a large literature trying to identify policy shocks, and to quantify their effects, using various approaches (see Section 2 and Ramey (2016) for more details). This includes several papers looking at the Australian economy, including work by Bishop and Tulip (2017), Beckers (2020) and Hartigan and Morley (2020).

To get around the second issue, more recent work has begun incorporating information about longer-term interest rates when constructing measures of monetary policy shocks. In particular, the recent literature has looked at high-frequency changes in interest rates with different maturities just before and after a policy announcement. These high-frequency changes are interpreted as shocks to current and expected future interest rates as they are above and beyond what market participants expected based on their understanding of systematic monetary policy and the available information. This allows for a more holistic examination of the different facets of monetary policy and its communication, and how these different facets affect the economy.

In this paper we adopt one such approach put forward by Kaminska, Mumtaz and Šustek (2021) (KMS) and apply it to the Australian data. The approach involves combining high-frequency data on changes in yields around policy announcements with an affine term structure model (ATSM), which decomposes the yields into expected future interest rates and term premia. This allows us to further decompose monetary policy shocks into three different components: (i) shocks to the current short-term policy rate – ‘Action’ shocks; (ii) shocks to the expected path of rates due to communication about future conditions or policy intentions – ‘Path’ shocks; and (iii) changes in required term premia due, for example, to the effect of communication on uncertainty or, in the case of unconventional policy, on the supply of government bonds – ‘Premia’ shocks. We then examine the effects of these different shocks on the Australian economy, using them as instruments in a proxy structural vector autoregression (SVAR).

This approach offers three key advantages over previous work. First, as each facet of policy could affect the economy differently, this decomposition potentially allows us to get a cleaner read on each one, rather than lumping them all together. Second, the approach allows us to try to quantify the effects of the RBA's communication about the outlook for rates and the economy. And third, we can apply this approach to COVID-19-era monetary policy to better understand the channels through which it affected the economy.

We apply this approach to monetary policy announcements, while also considering other events, such as speeches, release of Board minutes, and release of the Statement on Monetary Policy. This makes our paper the first to identify the macroeconomic effects of monetary policy shocks around a broad set of different monetary policy-related announcements in Australia.

To preview the results, we find that the KMS approach provides an intuitive lens through which to examine monetary policy and its communication. It allows us to understand how policy and its communication affected expectations for rates and risk during certain historical periods, and more generally. For example:

• During the mid-2010s, Path shocks tended to raise expectations about future interest rates as the RBA communicated concerns around risks in the housing market.
• Monetary policy announcements are more likely to be associated with increased term premia during periods of uncertainty, like the global financial crisis (GFC).
• Changes in term premia were the key channel through which policy affected interest rates during the COVID-19 era, while changes in expected future interest rates played a smaller role. This contrasts with pre-COVID-19 policy announcements, which mainly affected expected interest rates. This finding is similar to overseas findings comparing conventional and non-conventional policy periods (e.g. Kaminska and Mumtaz 2022).
• Speeches and other events mainly contain information about future interest rates, though on average they have a much smaller effect on yields compared to policy announcements.

We also find some evidence that shocks to the path of interest rates were predictable based on information at the time. This suggests that markets systematically misunderstand how the RBA reacts to data, potentially because they are learning about this reaction function over time. This highlights the importance of clear communication.

Regarding the effects of monetary policy on the macroeconomy, the KMS high-frequency approach provides broadly similar estimates of the effects of monetary policy to earlier papers in the literature. Positive shocks to current policy (Action shocks) are associated with an appreciation of the exchange rate and an eventual rise in unemployment. These effects are similar to those found using other simpler approaches, such as using high-frequency yield changes directly, including the fact that the ‘price puzzle’ is still evident, with Action shocks tending to precede higher inflation. In addition, the macroeconomic effects of both Path and Premia shocks turn out to be imprecisely estimated. As such, the approach adds little to our understanding of the quantitative effects of monetary policy on the macroeconomy.

In the context of the broader literature, we also find evidence that the specification of the ATSM can have substantial effects on the estimated shocks. This is consistent with other papers, such as Kim and Orphanides (2012), but is important to highlight in the context of interpreting the effects of monetary policy. In particular, if we follow KMS and use statistical methods to adjust the ATSM, rather than augmenting it with surveys, the approach attributes a much larger share of shocks to changes in the path of interest rates, rather than premia. In turn, this can influence the interpretation of the effects of Path and Premia shocks.

The rest of the paper proceeds as follows. Section 2 provides an overview of the literature on monetary policy shocks. Section 3 describes the KMS approach and shows the corresponding decomposition of yield curve changes. Section 4 examines the effects of the different identified shocks over the pre-COVID-19 period. Section 5 applies the KMS approach to COVID-19-era policy, and finally Section 6 concludes.