less on the maturity.
Balduzzi, Elton and Green (2001) research on the announcement e¤ects of scheduled macroeconomic news (17 indicators) on prices, trading volumes and bid-ask spreads for US government securities with maturities between three months and 30 years. At least one of these US Treasury securities is in‡uenced by the releases of the indicators, whereas the in‡uence strongly depends on the maturity. The price adjustment occurs very quickly in the …rst minute after the release and the volatility of the prices is mainly due to the surprise component in the news release. In contrast to the bid-ask spread that achieves a normal level in 15 minutes after the announcement, the trading volume and the price volatility remain higher than normal up to 60 minutes after the news release.
Other articles related to announcement e¤ects of macroeconomic news releases and the movement of asset prices are McQueen and Roley (1993), Balduzzi, Elton and Green (1997), Li and Engle (1998), Fleming and Remolona (1999a, 1999b), Ehrmann and Fratzscher (2002), Faust et al. (2003), Goldberg and Leonard (2003), Green (2004), Christiansen and Ranaldo (2005) as well as Andersen et al. (2005).
T0 T1 0 T2 T3 (estimation window] (event window] (post-event window]
T0 T1 0 T2 T3
(estimation window] (event window] (post-event window]
Figure 3.1: Estimation window, event window and post-event window of an event study.
It is essential to adjust the released data by expectations of the market participants, because only surprises in the data release cause asset prices to move. Empirical studies of announcement e¤ects of the release of macroeconomic data quantify changes in …nancial market variables like prices, returns, volume, volatility and bid-ask spreads due to the surprises in the news releases. When quantifying the changes in returns following a news release, an abnormal return is calculated. That is the di¤erence between the actual return and a counterfactual return without the event taking place.
Early studies of announcement e¤ects deal with the analysis of stock market reactions caused by incoming news (e.g. Beaver (1968)), whereas the price of a stock is in‡uenced by macroeconomic data, balance sheets, regulatory issues and the management. In contrast to that, more recent studies consider various asset classes as dependent variables and various types of news as explanatory variables.7
The following presentation of the event study approach is based on MacKinlay (1997).
To quantify abnormal returns due to the news release at time 0, it is necessary to quantify the normal return. Hence, in the time period before the event, an estimation window (T0; T1] is speci…ed in which the normal return without the event taking place is measured. So, the estimation window yields a return which constitutes the reference value when deciding whether the return is normal or abnormal. At the end of the estimation window, the event window (T1; T2] begins during which the news release occurs and the return is a¤ected by the surprise. The abnormal return is the di¤erence between the return observed in the market in the event window and the expected return in the estimation window. After the event window, the post-event window (T2; T3] begins. A graphical representation of the di¤erent time periods used in event studies is
7Andersen et al. (2005) give an overview of the di¤erent streams in the literature on announcement e¤ects.
given in …gure 3.1.
It is very important to de…ne the time intervals as accurate as possible in order to include all relevant information of the event and to exclude all information which is not related to the event. Only then, it is warranted that the power of the test –that is the ability to detect an abnormal return if there actually is an abnormal return –is as high as possible.
3.4.1 Quantifying the Announcement E¤ect
Brown and Warner (1980, 1985) propose three useful ways to quantify the abnormal return and test for its signi…cance. The abnormal return Zit of asset i at time t is de…ned as a prediction error equal to the di¤erence between the actual ex post return observed in the market Rit and the ex ante expected return due to the assumed return generating process. In the following lines, di¤erent methods to quantify the ex ante expected return are discussed.
When using Mean Adjusted Returns, a time series perspective is taken, that is Ri is the average return during the estimation window. The abnormal return Zit is given by
Zit=Rit Ri: (3.2)
Brown and Warner conclude that this test for signi…cance of the abnormal return yields valid results in comparison to more complex methods.
When using Market Adjusted Returns, the abnormal return Zit depends on the return of the marketRmt at time t which may be represented by a leading index of this market,
Zit =Rit Rmt: (3.3)
When using an OLS Market model, the expected return of the securityRit depends on a constant i and depends linearly on the market returnRmt with loading i. The error term "it has the characteristics E("it) = 0, V ar("it) = 2"
i and Cov("it; "i;t s) = 0 for all s6= 0. Consequently, the parameters i and i can be estimated by OLS under the
assumption of a normal distribution of the asset returns:
Rit = i+ iRmt+"it; t 2(T0;T1]: (3.4)
Some empirical articles …nd that the sample should have an appropriate length for the parameters i and i to be stationary (for example Binder (1998)). Consequently, the length of the sample interval is chosen to be …ve or seven years to make i and i stationary. The OLS estimation yields an ex ante expected return of R^it,
R^it= ^i+ ^iRmt, t2(T0;T1]: (3.5)
Hence, the abnormal return Zit is represented byb"it of the market model:
Zit b"it =Rit R^it, t 2(T1;T2]: (3.6)
Under the assumption of no structural break between the estimation and event window, the OLS Market model distinguishes whether the return of a single asset Rit is due to the market return R^it or due to the event which is captured inb"it. Another advantage is that the variance of Zit is reduced by the variance of the market return (MacKinlay (1997)): the higher theR2 in the regression of the market model, the higher the variance reduction of the abnormal return.
After quantifying the ex ante expected return, the abnormal return, i.e. the market reaction due to the news release, is tested for signi…cance. The null hypothesis is H0 : Zit = 0, that is the abnormal return at time 0 is zero. Under the assumption that the abnormal return Zit is independently, identically and normally distributed, the test statistic of a two-sided t-test can be compared with the student’s t-distribution:
t statistic= Zit 0
s(Zit) t(n k); (3.7)
where s(Zit) is the sample standard deviation of Zit during the estimation window, n the number of observations andk the number of restrictions.
3.4.2 Problems of an Event Study
The event study approach is based on certain assumptions on the properties of the data which may be violated when using …nancial market data. Nevertheless, even though the number of possible undesirably characteristics of …nancial market data is large, almost all of the problems can be solved by using an appropriate statistical method. Some of the problems do not decrease the power of the test results, so they can be neglected.
If the event is not included in the event window, the power of the test in an event study is signi…cantly reduced. As macroeconomic news releases are prescheduled, all information content of the event itself as well as of the estimation window can be com-pletely used in the estimation and does not reduce the power of the test.
Another problem when applying an event study to …nancial market data may arise when a group of related securities is a¤ected by only one news release (Clustering). The overlap of the event windows of di¤erent securities leads to a correlation of the price movements between these di¤erent securities (MacKinlay (1987)). This is the case for government bonds with di¤erent maturities which are analysed in this chapter.8 The fact that interest rates are correlated when Clustering occurs has two reasons. First, the covariance between the various government bonds is di¤erent from zero because all bonds react to the release of macroeconomic news. Nevertheless, the sign and size of the reaction may di¤er. Second, the price of a security changes when the price of another security within the peer group changes.9 This chapter presents an event study of the e¤ects of the release of macroeconomic data on the term structure of interest rates. The interest rates of bonds with similar maturities are positively correlated to a high degree.
Their reactions to the release of macroeconomic news are also positively correlated. So, in the aftermath of a news release it is not possible to distinguish between the movement of the interest rate due to the news release and due to the correlation between the interest rates.
There is no consensus in the literature on the dimension of the bias when event windows are clustered. Bernard (1987) …nds that even in the case of correlated abnormal
8The correlation of interest rates of government bonds is analysed in section 2.5.
9The interpretation is similar for stocks. For example, a release of a pro…t warning by one bank a¤ects stock prices within the whole …nancial sector.
returns, the parameter estimates may be unbiased, but the variance estimate is biased.
He points out that the bias of the variance estimate is more severe when using monthly data in contrast to daily data, because the number of daily observations is much larger than the number of parameters to estimate.
Several methods have been used to overcome the estimation problems when …nancial market reactions are correlated. One solution is to constitute a portfolio of the correlated securities with overlapping event windows (MacKinlay (1997)). However, it is only possible to analyse the aggregated return of this portfolio and the aggregation yields a loss of information and a reduced power of the test. Another solution is to quantify the market reaction by the coe¢ cient of a dummy variable for the event (Binder (1998)).
In an equation system, the e¤ect of one event on a number of correlated securities is analysed, so that the sign and size of the reaction of each security can be measured. As the variance covariance matrix is explicitly estimated, the results are reliable.
Another econometric problem of event studies might be the autocorrelation of the abnormal returns due to the news release, which yields biased variance estimates and consequently wrong results of hypothesis tests. Brown and Warner (1985) construct an estimation approach that takes into account autocorrelated abnormal returns and …nd that the quality of the estimation results improves. Nevertheless, they conclude that autocorrelation plays only a minor role in event studies and can be neglected.
A higher variance during the event window might also pose a threat to the results of an empirical event study. Many return series of …nancial assets show a signi…cantly increased variance shortly before and after the event. For example, Christie (1983)10
…nds a nearly doubled variance around the event. If the variance used in the test for the abnormal return is estimated within the estimation window without an event, the resulting variance estimate is too low. As a consequence, the standard error is underestimated and the null hypothesis of no abnormal return is rejected too often.
An approach to capture the higher variance around the event for a number of se-curities that react to the same event is proposed by Boehmer, Musumeci and Poulson (1991). The time series behaviour of the return series during the estimation window is
10Brown and Warner (1985) quote Christie, A., 1983, On Information Arrival and Hypothesis Testing in Event Studies, Working Paper, University of Rochester.
left aside. The variance of the price movement on the event day is constructed by using cross sectional data for all the abnormal returns on the event day. However, Brown and Warner (1985) state that if the increase in the variance of the di¤erent securities is not the same, the abnormal returns on the event day are not identically distributed and the test statistic is not appropriately speci…ed. In addition to that, due to the cross sectional approach, the increase in the variance is only taken into account if it takes place during the announcement day.
Even though there might be various problems when applying the method of an event study to real data, the results of event studies are very robust. The reason is that either the occurring problems are negligible or the estimation technique can be adjusted for the characteristics of the data.