RDP 2004-10: News and Interest Rate Expectations: A Study of Six Central Banks 3. Does News Matter?

As outlined in the previous section, in this paper we model the various influences – domestic and foreign – on interest rate expectations in six different economies. We concentrate on influences that change expectations for the future path of monetary policy: domestic macroeconomic data surprises, changes in foreign news reflected in changes in foreign interest rate futures, domestic monetary policy surprises and central bank communication. The next section summarises the data underlying our analysis, followed by a preliminary analysis. This analysis investigates the contribution of surprises in the four news categories to daily changes in interest rate futures, before a formal model of the effect of individual news events is estimated in Section 4.

3.1 Data

At the core of our empirical analysis are changes in interest rate expectations. We measure these using changes in daily implied interest rates from 90-day interest rate futures, Δf_t, at maturities from one to eight quarters, based on the last trade available for each day. Our data for individual economies start in January 1997 for Australia, Canada, the United Kingdom and the United States, and in 1999 for the euro area and New Zealand.^[4] Our panel results therefore start in 1999. The last data point included is 17 June 2004.

Domestic macroeconomic surprises, news_b,t, related to a release of data on b (for example, GDP, CPI or employment releases), are measured by taking the difference between the actual outcome of data released and the outcome expected in a survey of market economists. Consulting Bloomberg yielded a large number of surveys of expected macroeconomic news outcomes for constructing surprise variables (Table 2).

Foreign news surprises can be approximated by the contemporaneous change in the interest rate futures of equivalent maturity in an important foreign market, , and its lags. These should capture both the macroeconomic surprises for these foreign economies and monetary policy surprises. A number of studies have found that developments in US financial markets have an important effect on other economies' financial markets. We therefore include changes in US interest rate futures in the equations for all other economies, and also changes in Australian interest rate futures in the model for New Zealand.^[5]

Monetary policy surprises, ps_t, are measured by taking the change in 30-day interest rates on the day of monetary policy decisions, consistent with Campbell and Lewis (1998) and Kohn and Sack (2003). This 30-day interest rate, a market interest rate, should reflect market participants' expectation of the actual policy rate for the following month. Since central banks in our sample have regular policy meetings in a monthly or 6-weekly cycle, the expected policy rate should be very similar, if not the same, over this month. Consequently, any change of the 30-day interest rate can be attributed to a change in the (expected) policy rate which is set on the first day of the 30-day paper.

The information or news content of central bank communication cannot be collapsed into one empirical measure, making it difficult to measure the surprise element or even the direction. Therefore, we measure different types of communication, w, by the central bank through a communication dummy, com_w,t, that takes the value one if a certain communication event has happened on a day, and zero otherwise. These communication events include policy rate decisions with and without commentary, monetary policy reports, parliamentary hearings, minutes of meetings (and voting records) and speeches. The data were available on the websites of the six central banks.

A number of variables control for time-specific and other events, Other_d,t, where d denotes the different variables. These include four dummies for day-of-the-week effects, Other_1-4,t, a dummy for public holidays, Other_5,t, and a dummy for 11 September 2001, Other_6,t.^[6] We also include a measure for the days to rollover for each futures contract, Other_7,t. Every three months on a pre-set date, the 1^st futures contract is settled and the remaining futures contracts are rolled over to the next contract. Since volatility may be expected to vary as a contract approaches expiry, we include this variable to capture this effect.

3.2 A Preliminary Analysis

In Section 2 we have noted a number of theoretical reasons why macroeconomic and monetary policy news should affect interest rate expectations. However, many other factors can affect the variance of daily financial data. One simple way to assess whether different types of news affect interest rate expectations is, therefore, to ask whether interest rate futures have a higher variance on days of news releases than on other days.

Table 3 is based on the 100 largest daily changes in interest rate futures for each of the six economies in our study. For illustrative purposes, we only present the results for the 4^th futures contract in the tables, which measures expectations for one year in the future, roughly the middle of the horizon of our futures data. For each economy the first column shows the proportion of the top 100 daily changes that fall on days with foreign market movements, macroeconomic data surprises, monetary policy surprises and central bank communication. The second column shows the corresponding proportion of news days in the entire sample, which – except for the euro area and New Zealand – comprises 1947 observations. If economic announcements or monetary policy news did not affect markets, the proportion of large changes in interest rate futures occurring on news days should not be significantly different to the proportion of news days in the entire sample.

We can make two observations from these results. First, all four news categories are over-represented on the days with the largest 100 changes in interest rate futures, compared with their overall share in the sample. Second, most of the days with large changes are also days when foreign interest futures changed significantly or when domestic macroeconomic data surprises occurred. However, the methodology used in Table 3 has an obvious drawback. Different types of news can arrive on the same day, and therefore changes in interest rate expectations can be attributable to either or both. In fact, in large economies such as the United States, barely a day passes without the release of new data. To disentangle – and possibly quantify – the effect of different news, an econometric model needs to be estimated. In the remainder of this section we estimate two very simple equations with the aim of disentangling the contributions of the different news categories.

The simple model of Equation (1) explains the change in 90-day interest rate futures Δf_t with a range of factors, such as monetary policy surprises ps_t, domestic macroeconomic data surprises news_b,t, foreign data surprises Δf^OS, and different types of communication by the central bank com_w,t. As mentioned above, a number of variables, Other_d,t, control for time-specific events. We also include lags of futures rates to control for autoregressive behaviour in the futures markets.

From this model the relative contributions of the different types of news in explaining changes in interest rate expectations can be calculated based on an ANOVA analysis.^[7] Columns (1) in Table 4 show the results for each economy. An initial observation is that the unexplained residual is by far the largest component. This means that a large share of the variation in daily interest rate futures cannot be explained by simple regression on unexpected macroeconomic and monetary policy news, domestic or foreign. However, some conclusions can be drawn from the part that can be explained by the model. The pattern for Australia is illustrative for all economies: foreign market movements^[8] and domestic macroeconomic news are the largest source of variation. Their effect is prominent for interest rate futures over the entire time horizon considered (Table 4 contains only the results for the 4^th contract, but the results for all contracts are consistent with those in Section 4.2 and are available from the authors). In contrast, monetary policy surprises appear to affect interest rate expectations mainly in the very short term.

Finally, communication by the central bank explains changes in interest rate expectations only to a small degree. This might suggest that central bank communication provides some information to markets, but interest rate expectations mostly get revised after macroeconomic data surprises or unexpected monetary policy decisions. This conclusion is, however, partly complicated by our measure of communication events as a dummy. As it is difficult to quantify the information contained in central bank communication, we have identified each type of communication event only by whether or not it happens on a specific day. The estimated coefficient underlying the ANOVA analysis in Table 4, on the other hand, measures the average impact of all communication events of a specific type. If this type of communication has, on average, equally often ‘upward’ and ‘downward’ impacts, we would expect to estimate a zero impact of a communication dummy in this analysis.

An alternative is to estimate a model that uses absolute values only, such as Campbell and Lewis (1998). Taking absolute values of the impact would avoid the ‘averaging out’ of upward and downward impacts. We consequently estimated Equation (1) in absolute value form, as follows:

Columns (2) in Table 4 show the ANOVA contributions from this regression. The results confirm our earlier findings: domestic macroeconomic news and especially foreign market movements explain a much larger share of changes in interest rate futures than monetary policy surprises and central bank communication. The contribution of central bank communication remains relatively low, suggesting that the ‘averaging’ effect is not very strong. However, compared with the results for Equation (1) the contribution of foreign market movements is much lower, which may be due to the loss of information in the absolute value equation (as indicated by the lower R-squared of Equation (2)). Many foreign market movements happen on the same day as monetary policy decisions or macroeconomic news. The econometric estimation has difficulties attributing these correctly as we have given up the information on ‘direction’ of all news variables.

Taken together, these results indicate that movements in foreign markets and domestic macroeconomic data surprises affect interest rate expectations to a much larger degree than central bank communication. Of course, the latter can still affect the standard deviation of the interest rate futures on the day of the communication event. Due to the nature of the communication variables (neither direction nor strength is modelled) compared with the other ‘news variables’, a different approach is needed to assess the effect of individual types of news events on interest rate expectations. The econometric model employed in Section 4 provides such an estimation technique, modelling the mean and the standard deviation of the change in interest rate futures jointly.

Footnotes

A number of the news releases and market expectations were readily available only since 1997. Moreover, by then all inflation targeters included in the samples had put in place most elements of their current communication frameworks. The Bank of Canada changed elements of their communication strategy up until December 2000 (see, for example, Siklos 2003), but our results for Canada were qualitatively unchanged when estimated over the shorter time period starting in 2001. [4]

Ehrmann and Fratzscher (2002) find that US developments seem to be more important for euro interest rates than vice versa. They argue that one reason for this may be that US data are typically released earlier than euro area data, and thus might provide a leading indicator function. For our sample of economies, US macroeconomic data are typically released earlier than domestic data in a similar category. [5]

Day-of-the-week effects can be expected to proxy for news events that we have omitted from our study. Since releases of a specific category of news are often scheduled for the same day of the week, this can show up as additional variance on that weekday. [6]

The contributions based on an ANOVA analysis can be thought of as the differences in (unadjusted) R-squared from a regression with and without the variable (or set of variables) in question. Since this measures only the marginal contribution of this variable, the order in which the contributions are calculated can matter if the variable is correlated with the variables already contained in the model. In our model, we have included the communication variable last, thereby assuming that any change in interest rate futures that could be attributed to either communication or another news event, is attributed to the latter. While this might explain the low contribution of communication in all regressions, an ordering in which communication was included first, yielded similar results, with a contribution from communication of around 1 to 2 per cent in most cases. [7]

Foreign market movements are modelled for all economies, except for the US, as changes in US interest rate futures. For New Zealand, changes in Australian interest rate futures are also included. [8]