Intraday jumps and US macroeconomic news announcements

Journal of Banking & Finance 35 (2011) 2511–2527

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Intraday jumps and US macroeconomic news announcements Kevin P. Evans ⇑ Cardiff Business School, Cardiff University, Colum Drive, Cardiff CF10 3EU, United Kingdom

a r t i c l e

i n f o

Article history: Received 30 September 2008 Accepted 16 February 2011 Available online 23 February 2011 JEL classification: C14 E44 G14 Keywords: Jumps Realised volatility Bipower variation Macro news announcements

a b s t r a c t This paper applies recent non-parametric intraday jump detection procedures to investigate the presence and importance of intraday jumps in US futures markets. More importantly, the paper investigates the extent to which statistically significant intraday jumps are associated with US macroeconomic news announcements. Jumps are prevalent, large and contribute heavily to total daily price variation. Approximately one third of jumps correspond to US macroeconomic news announcements, with pure announcement effects causing large increases in the absolute sizes of jumps and the informational surprise of the announcement explaining large proportions of the jumps. The statistical and economic significance of news-related jumps is confirmed by results that show higher volatility persistence, predictability of lower frequency returns, larger effects on microstructure variables, jump clustering and co-jumps from these jumps versus non-news-related jumps, although there are some interesting variations across asset classes. Ó 2011 Elsevier B.V. All rights reserved.

1. Introduction Continuous-time diffusion processes have formed the basis for theoretical asset and derivative pricing models for many years. However, the development of empirical procedures for their estimation and inference has been hindered due to their incompatibility with the discrete nature of asset price data. More recently, research activity has made significant progress in this context using both parametric and non-parametric techniques. The recent empirical evidence using parametric techniques suggests that multifactor models provide major improvements over more traditional one-factor models, although they are unable to account for the fattailed property of returns. This has prompted the inclusion of a jump component as an essential factor in continuous-time asset pricing models. The improvement in the empirical performance of continuous-time parametric models, along with the detection of jumps and the evaluation of their economic importance and contribution, has spurred a burst of recent research activity and a number of recent studies have demonstrated the need to allow for jumps in prices and volatility in addition to a time-varying diffusive volatility component in order to represent the observed price process satisfactorily. Furthermore, Das (2002) and Johannes (2004) reconcile jump times and sizes with news arrivals in interest rate markets providing some initial evidence that information

⇑ Tel.: +44 29 2087 4558; fax: +44 29 2087 4419. E-mail address: [email protected] 0378-4266/$ - see front matter Ó 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jbankfin.2011.02.018

about macroeconomic performance enters the term structure through these surprise jumps. Alongside these advances, powerful non-parametric techniques have been developed, which benefit from the rich information contained in high frequency data. Crucial to this framework is the decomposition of the instantaneous return to any arbitrage-free logarithmic price process as an expected return component and a martingale innovation. The quadratic variation of the local martingale may be further decomposed into a continuous local martingale component and a compensated jump martingale, if the possibility of jumps is assumed to exist. The advances in this literature are the measurement of realised variation, which measures quadratic variation, and realised bipower variation (BarndorffNielsen and Shephard, 2004), which measures integrated variance, the continuous component. The difference between realised variation and realised bipower variation therefore isolates the jump component. The econometrics of testing for jumps, including asymptotic distribution theory to identify statistically significant jumps has enjoyed a burst of recent analysis. The pioneering theoretical framework is provided by Barndorff-Nielsen and Shephard (2006) who document strong empirical evidence for the presence of jumps.1 Huang and Tauchen (2005) present extensive simulation evidence in support of the finite sample properties of these 1 See also Aït-Sahalia and Jacod (2009), Fan and Wang (2007), Jiang and Oomen (2008) and Lee and Mykland (2008) for alternative approaches to detecting jumps, and Bollerslev et al. (2008) and Jacod and Todorov (2009) for jump tests in a multivariate setting.

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K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527

non-parametric jump tests and confirm strong evidence for the existence of jumps and their contribution to stock price variation. Tauchen and Zhou (2011) confirm these findings and further suggest that jump intensity and jump size distribution are time varying. Placing greater emphasis on the economic importance and relevance of the continuous and jump components of total return variation, Andersen et al. (2007a) confirm the contribution of jumps and make important gains in the forecasting accuracy of realised volatility by separating jump variation from the continuous sample path variability. More recently, Wright and Zhou (2009) and Tauchen and Zhou (2011) introduce rolling measures of jump risks and show their importance for predicting future, lower frequency returns. Whilst showing evidence for the presence of jumps, these studies preclude the measurement of multiple jumps on particular days and prevent the exact intraday timing of jumps. These issues are addressed in Andersen et al. (2007c, 2008) who present alternative methods for identifying intraday jumps, thereby providing superior information on jumps. Related high frequency studies often reveal occasional violent price movements that coincide with the release of surprising macroeconomic news. The relationships between macroeconomic fundamentals and financial markets have been analysed for many years, especially with the recent availability of high frequency data (see Andersen and Bollerslev, 1998; Andersen et al., 2003, 2007b). Interesting recent investigations have focussed and the effect of news announcements on price discovery in the foreign exchange market (Chen and Gau, 2010) and the high frequency effects of monetary policy announcements (Chuliá et al., 2010; Rosa, 2011; Hussain, 2011) Given the strong evidence for the presence of jumps in financial markets and the extreme reactions in financial markets to macroeconomic news announcements, this paper merges these literatures by investigating the extent to which macroeconomic announcement surprises coincide with statistically significant intraday jumps. The existing evidence detects multiple intraday jumps, but investigates stock markets that are closed at the times of scheduled macroeconomic data announcements. This paper makes an important contribution to this literature by applying jump detection tests to markets that are active at the time of news events. A more innovative contribution is the investigation of the extent of linkages between statistically defined, significant jumps (rather than returns) and macroeconomic news, thereby examining possible economic explanations for jumps. Further contributions then confirm the economic importance of news-related jumps in terms of their size and effects on return predictability, volatility persistence, microstructure variables, jump clustering and co-jumps. The results show that jumps are prevalent, large and contribute heavily to total price variation, confirming previous findings. Approximately one third of intraday jumps are found to coincide with macroeconomic data releases with the jumps statistically and economically significantly larger when announcements occur. Furthermore, jumps are closely related to the information surprise delivered, showing dramatic reactions of financial markets to economic fundamentals in the very short term. These findings have obvious implications for short term trading strategies, portfolio allocation decisions and risk management procedures, reinforce the requirement to account for jumps in asset pricing and risk management models, offer a simple way to predict the occurrence and magnitude of some intraday jumps, and may also be important for policy makers concerned with the effects that macroeconomic announcements have on financial markets. Furthermore, news-related jumps are found to be of greater economic significance than non-news-related jumps since they generate larger jumps in absolute terms, which give rise to stronger volatility persistence, return predict-

ability at lower frequency, clustering and co-jumps. There is clear evidence that news-related jumps are more important for bond and foreign exchange futures, although there are some interesting variations across asset classes. The remainder of the paper is organised as follows. Section 2 describes the theoretical framework, including daily and intraday jump detection and adjustments for market microstructure noise effects. Section 3 explains the data; Section 4 discusses sample statistics of daily and intraday jumps, whilst the main findings relating intraday jumps to macroeconomic news announcements are described in Section 5. Section 6 then investigates the relative economic importance of news versus non-news-related jumps before Section 7 concludes.

2. Theoretical framework The logarithmic price process is expressed as a continuous-time jump-diffusion process, as used in a number of recent studies:

dpðtÞ ¼ lðtÞdt þ rðtÞdWðtÞ þ jðtÞdqðtÞ;

ð1Þ

where l(t) is a continuous and locally bounded variation process, r(t) is a strictly positive stochastic volatility process with a sample path that is right continuous and has well defined left limits, which allows for occasional jumps in volatility, W(t) is a standard Brownian motion, and q(t) is a counting process with possible time-varying intensity k(t). This implies that P[dq(t) = 1] = k(t)dt, and j(t) measures the size of the corresponding discrete jumps in the logarithmic price process. The quadratic variation of the cumulative return process is then defined as

½r; rt ¼

Z

t

r2 ðsÞds þ

0

X

j2 ðsÞ;

ð2Þ

0
representing the continuous sample path and jump components of total return variation. A continuous sample path for asset prices cannot be observed in practise, which confines empiricists to the use of discretely sampled prices and D-period high frequency returns. However, the daily realised variation, defined as the summation of the corresponding 1/D high frequency intraday squared returns,

RV tþ1 ðDÞ 

1=D X

r2tþjD;D ;

ð3Þ

j¼1

converges uniformly in probability to quadratic variation, thereby providing a consistent non-parametric measure of total return variation. In developing the theory of quadratic variation, BarndorffNielsen and Shephard (2004) introduce realised bipower variation, defined as the scaled summation of the product of adjacent absolute high frequency returns,

BV tþ1 ðDÞ  l2 1

1=D X

jr tþjD;D jjrtþðj1ÞD;D j;

ð4Þ

j¼2

pffiffiffiffiffiffiffiffiffi where l1  2=p, and show that it converges in the limit (as D ? 0) to integrated volatility. Combining these results then allows the separation of the continuous and discontinuous components of quadratic variation, isolating the contribution of jumps,

RV tþ1 ðDÞ  BV tþ1 ðDÞ !

X

j2 ðsÞ:

ð5Þ

t
The first challenge to the empirical application of Eq. (5) is the potential identification of very small values for jump variation that do not represent genuine jumps. The asymptotic

K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527

distribution theory developed in Barndorff-Nielsen and Shephard (2004, 2006) alleviates this problem and, consistent with recent literature, statistically significant jumps are identified according to

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dure is conducted using staggered measures of realised bipower variation.4,5 3. Data

1

Z tþ1 ðDÞ  D1=2

½RV tþ1 ðDÞ  BV tþ1 ðDÞRV tþ1 ðDÞ

2 1=2 2 ½ðl4 1 þ 2l1  5Þ maxf1; TQ tþ1 ðDÞBV tþ1 ðDÞ g

 Nð0; 1Þ;

ð6Þ

with tripower quarticity defined as

TQ tþ1 ðDÞ  D1 l3 4=3

1=D X

jr tþjD;D j4=3 jrtþðj1ÞD;D j4=3 jr tþðj2ÞD;D j4=3 ;

ð7Þ

j¼3

Using l4/3  22/3  C(7/6)  C(1/2)1. Significant jumps are identified by the realisations of Zt+1(D) in excess of the 99.9% critical value U1a:2

J tþ1;a ðDÞðZÞ  I½Z tþ1 ðDÞ > U1a   ½RV tþ1 ðDÞ  BV tþ1 ðDÞ:

ð8Þ

The second problem of this procedure is the presence of market microstructure frictions. This causes the measurement of RVt+1(D) and BVt+1(D) to be biased, which is controlled for in empirical applications by the choice of the sampling frequency and recent studies report that 5 min is appropriate.3 However, the presence of i.i.d noise also generates spurious first order serial correlation between adjacent returns, which presents an additional source of bias for realised bipower variation and tripower quarticity. This biases the jump test statistic against finding jumps. The spurious serial correlation in the observed returns is annihilated by using staggered returns in measuring realised bipower variation and tripower quarticity. The staggered measures are consistent even in the absence of the market microstructure noise contamination and the associated jump test statistics are asymptotically standard normally distributed. Huang and Tauchen (2005) demonstrate more accurate finite sample approximations when using staggered measures, and so they are adopted here. The jump identification technique described above is only able to isolate the trading days that contain at least one jump. Given that these techniques employ high frequency asset returns, which react very quickly and sometimes dramatically to macroeconomic news announcements, it is interesting and useful to identify multiple intraday jumps along with their precise timing. The intraday jump detection procedure of Andersen et al. (2007c) defines a randomly selected intraday return as P D r tþnD;D ¼ 1= j¼1 r tþjD;D Iðn ¼ jÞ, where n is an independently drawn index (uniformly distributed) from the set {1,2, . . . , 1/D}. This return is identified as a jump by comparing its absolute value to an appropriately scaled realisation of bipower variation. Andersen et al. (2007c) assume that intraday scaled returns are distributed as D1/2rt+nD,D  N(0, IVt+1) and form its empirical counterpart by using realised bipower variation such that randomly drawn intraday diffusive returns are distributed approximately as N(0, D  BVt+1(D)). Multiple intraday jumps are then detected by

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

1 D ð9Þ

jj ðDÞ ¼ rtþjD;D  I½jrtþjD;D j > U1b=2  D  BV tþ1 ðDÞ; j ¼ 1; 2; . . . ;

where U1b/2 represents the corresponding appropriate critical value from the standard normal distribution. Market microstructure frictions remain an issue in this test and the jump detection proce2 This stringent significance level allows comparability with other recent studies and helps to ensure the identification of genuine jumps, but the choice of significance level remains an empirical matter. 3 See, for example, Andersen et al. (2007a,c), Huang and Tauchen (2005) and the references therein.

This paper applies the daily and intraday jump detection procedures detailed above to S&P 500 E-Mini, 10-Year US Treasury Bond and Euro-Dollar (EUR-USD) foreign exchange futures markets over an 8-year period. Futures market data are readily available and report transaction prices, which are more informative than indicative quotes, and cover instruments with lower transactions costs than their corresponding cash markets, ensuring actively traded contracts. There is also evidence that futures markets generally lead the cash market in terms of price discovery. More importantly, these futures markets are open and active at the time of macroeconomic news announcements in order to capture this potential source jumps. The sample includes tick data from July 1998 to June 2006 using the contract closest to expiration and switching to the nextmaturity contract automatically when the latter becomes more actively traded. Guided by patterns of trading volume, this study applies trading hours of 8:20–16:15 EST to the S&P 500 E-Mini contract, even though the contract is traded electronically between 16:30 and 16:15 EST. Pit trading times of 8.20–15.00 EST are maintained for the US 10-Year T-Bond and EUR-USD contracts with data from July 2003 supplemented by electronic trading. The empirical work is based on local currency continuously compounded 5-min returns, which are calculated as log(pj/pj1)  100, where pj denotes the price of the last trade in the jth interval. Any interval that contains no trades is assigned the price from the previous interval. Days where data are missing, usually occurring when exchanges close early due to public holidays, or very occasionally due to missing data, are removed from the sample. The sample therefore includes 1993, 1905 and 1819 days for the E-Mini, T-Bond and EUR-USD contracts comprising 189,335, 152,400 and 145,520 five-minute returns respectively.6 4. Daily jump variation and intraday jumps Fig. 1 shows the sample measures of realised volatility and statistically significant daily jump volatility at the 0.1% level. Realised volatility is far higher for the E-Mini contract than the other two. In all three markets, volatility was particularly high during the second half of 1998, 2000, 2001 and late 2002. In contrast to the T-Bond and currency futures, there was no elevated volatility in 2004 for the E-Mini contract and volatility has been remarkably stable in this contract since mid-2003. The largest spikes in realised 4 Andersen et al. (2007c) assume for tractability that intraday volatility is constant throughout the trading day and provide simulation evidence to show that their procedure is robust to the inclusion of an inherent volatility pattern that is well documented in high frequency studies. Empirical analysis of this issue, which is not documented here, reveals that annihilating the intraday pattern finds far fewer intraday jumps than raw returns, but also fails to identify genuine, jumps following macroeconomic data releases. A robust analysis of intraday jump tests in this context is left to future work. 5 An alternative sequential intraday jump detection approach introduced by Andersen et al. (2008) has also been applied. The tests report quantitatively and qualitatively similar results, however only the results for the intraday measure of Eq. (9) are reported, with any important discrepancies footnoted. 6 The EUR-USD cash market is known to be more liquid than the corresponding futures contract. A brief comparison of intraday jumps detected in a shorter sample of spot EUR-USD data from indicative quotes with the identical trading days and times of the futures contract revealed many more jumps in the spot market. However, encouragingly for this work, the jumps detected common to both markets were extremely similar in size and the vast majority of these additional spot market jumps were not related to US macroeconomic news announcements. This inspires confidence in the futures data used in this study, but also raises interesting econometric and microstructure questions that are left for future work.

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K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527

S&P 500 E-Mini, RV t1 2 7 6 5 4 3 2 1 0 1999

2000

2001

2002

2003

2004

2005

2006

2004

2005

2006

2004

2005

2006

2004

2005

2006

S&P 500 E-Mini, J t1 2 7 6 5 4 3 2 1 0 1999

2000

2001

2002

2003

US 10-Yr T-Bond, RVt1 2

2 1.5 1 0.5 0 1999

2000

2001

2002

2003

US 10-Yr T-Bond, J t1 2

2 1.5 1 0.5 0 1999

2000

2001

2002

2003

EUR-USD, RVt1 2

2 1.5 1 0.5 0 1999

2000

2001

2002

2003

2004

2005

2006

2004

2005

2006

EUR-USD, J t1 2 2 1.5 1 0.5 0 1999

2000

2001

2002

2003

Fig. 1. Realised volatility and daily jump variation. Notes: This figure shows plots of daily realised variation and daily jump variation as defined by Eq. (8) for statistically significant jumps for each market.

volatility occurred in late 1998 for the E-Mini, and the first half of 2004 for the other two contracts. Other days showing huge volatility are found in 2000, 2001 and 2002, which are consistent for all

three contracts and may imply co-movements or spillovers across markets. Applying a stringent significance level to the jump test emphasises the frequency, magnitude and contribution of the

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K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527 Table 1 Summary statistics for daily and intraday jumps.

No. days No. jump days

S&P 500 E-Mini 1993 120 Mean

(a) Daily jumps Jt Jþ t 1=2

Jt

0.052 0.856 0.040

US 10-Year T-Bond 1905 354 SD 0.833 3.305 0.223

EUR-USD 1819 222

LB(10)

Mean

SD

LB(10)

Mean

SD

0.15 0.01 2.55

0.013 0.073 0.042

0.063 0.130 0.108

7.86 0.88 18.22

0.014 0.113 0.037

0.068 0.164 0.111

LB(10) 2.62 0.31 3.05

ðJ t Þþ Jt/RVt (Jt/RVt)+ JD

0.669

0.642

0.25

0.227

0.146

3.98

0.301

0.149

1.10

0.020 0.327 15.941

0.082 0.101 18.767

9.47 0.10 12.50

0.072 0.390 5.391

0.159 0.110 6.519

84.66 1.03 45.74

0.044 0.358 8.213

0.121 0.087 8.856

11.15 1.39 10.02

No. intraday jumps No. jump days

365 330

Mean

Max

Min

1=2

Mean

596 513 Max

Mean

Max

0.000 0.144 0.020

0.001 0.225 0.071

1.457 1.457 2.102

0.000 0.055 0.003

0.001 0.272 0.087

1.379 1.379 1.894

0.000 0.099 0.009

0.862

0.115

0.307

0.884

0.053

0.276

0.843

0.091

0.925

0.130

0.356

0.892

0.107

0.314

0.882

0.159

(b) Intraday jumps |jj| |jj|+ JV þ t;j

0.001 0.548 0.486

5.584 5.584 31.105

JV þ t;j =RV t

0.237

JVDþ t =RV t

0.262

Min

571 501 Min

Notes: The table shows summary statistics for various series relating to daily and intraday jumps. Panel a summarises daily jumps detected according to Eqs. (5), (6), and (8) and Panel b describes intraday jumps defined according to Eq. (9). In Panel a Jt denotes the jump variation series, Jt/RVt measures the contribution of jump variation to daily realised variation, jump duration is denoted as JD and measures the number of days between jumps. + Indicates jumps significant at the 0.1% level. In Panel b, JV þ t;j measures the jump variation due to individual intraday jumps and JVDþ t represents daily jump variation, which includes multiple intraday jumps.

jumps. Fewer jump days are identified for the E-Mini contract compared to the US 10-Year T-Bond and EUR-USD contracts and the plots also reveal interesting jump clustering and time variation of jump sizes along the sample period. A more robust treatment of the statistically significant jumps is provided in Panel a of Table 1, which shows descriptive statistics for a range of series relating to daily jump variation. The results show 120, 354 and 222 days containing jumps for the S&P 500 EMini, US 10-Year T-Bond and EUR-USD contracts, far more than would be expected from purely diffusive processes, comparable to previous findings, confirming that jumps are an integral feature of the price process. The mean jump variation, absolute actual jump and relative jump contribution on significant jump days for the E-Mini are 0.856%, 0.669% and 32.7% respectively, which compare to corresponding figures of 0.073%, 0.227% and 39% for the US 10-Year T-Bond and 0.113%, 0.301% and 35.8% for EUR-USD. In addition to confirming previous findings, these results emphasise the magnitude of jumps and their contribution to total price volatility. There are fewer jump days for the E-Mini contract, but average jump variation and absolute actual jumps are substantially higher than for the other two contracts. The average duration between jump days is implies 13, 44 and 30 jumps per year for the E-Mini, T-Bond and EUR-USD markets respectively, which are far more than the recent parametric studies allow. The dynamic properties of the jump duration series show a high degree of serial correlation, indicating that the timing of jumps may be predictable even if their magnitudes are less dependent over time. Panel b of Table 1 shows summary statistics for intraday jumps identified using the approach of Andersen et al. (2007c). Panel b shows many more significant jump days than Panel a across all three markets and also detects many days containing multiple jumps. The average absolute intraday jumps of 0.548, 0.225 and 0.272 are huge, but these averages mask the enormous range of jump sizes (2.884 to 5.584 for the E-Mini, 1.457 to 0.954 for the T-Bond and 1.379 to 0.948 for EUR-USD), which show the extremity of some intraday jumps. On average, the intraday jumps contribute 26.2%, 35.6% and 31.4% of realised variation, but again these averages hide the small contributions of some jumps

(10.7%) and the dramatic contributions of others (92.5%). Consistent with Panel a, fewer jumps are detected for the E-Mini contract, but they tend to be larger in magnitude and can cause extremely large proportions of realised variation. 5. Intraday jumps and news announcement effects The more innovative contribution of this paper investigates the impact of macroeconomic news announcements on intraday jumps. Preliminary inspection reveals that the largest intraday jumps coincide precisely with the release of US macroeconomic data. Indeed, of the fifteen largest intraday jumps in the sample, eleven coincide with macroeconomic news announcements for the E-Mini, all 15 coincide with news for the T-Bond and eleven for EUR-USD futures, showing that large jumps are associated with news announcements. Furthermore, separating intraday jumps into two sub-samples of news-related jumps and non-news-related jumps reveals initial differences between the two types of jumps. First, tests of the equality of means show that news-related jumps are not significantly different from non-news-related jumps since positive and negative jumps likely offset each other. However, for absolute values of jumps, news-related jumps are significantly larger on average than non-news-related jumps. Separating positive and negative jumps also reveals important differences between the two groups with average positive (negative) newsrelated jumps significantly larger (smaller) than average positive (negative) non-news-related jumps. These preliminary findings are consistent across all three markets and indicate that there are distributional differences between news-related jumps and non-news-related jumps. Fig. 2 plots the news related and non-news related intraday jumps for each market.7 Large intraday jumps are observed in the early part of the sample for the E-Mini, related to the crisis period 7 Intraday jumps are plotted against the day on which they occur rather than the precise intraday interval and the largest intraday jump for the E-Mini of 5.584% is not shown fully, both for display purposes. Multiple intraday jumps are cumulated to give a daily measure.

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K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527

resulting in the collapse of LTCM, the bursting of the technology bubble and the ensuing recession. There are more non-news-related jumps than news-related jumps, particularly later in the sample. News-related jumps tend to be larger than non-news jumps. For the T-Bond, it is clear to see a higher proportion of news-related jumps that are larger than non-news-related jumps. The largest jumps occur in 2003, which correspond to a vigorous economic expansion and subsequent slowdown. Similar to the E-Mini, there is a lower proportion of news-related jumps for the EUR-USD contract than the T-Bond. Also, the plot does not show such clear evidence of discrepancies in sizes of the two types of jumps. The empirical analysis in this paper investigates more formally the relationship between macroeconomic news announcements and intraday jumps and investigates the relative economic importance of news-related jumps versus non-news-related jumps.

5.1. Intraday jumps and instantaneous macroeconomic news announcements To assess more formally the impact of macro news announcements on intraday jumps, the following regression is estimated

jjj j ¼ xk þ bk Dk;j þ ek;j ;

ð10Þ

where Dk,j, is a dummy variable that takes the value one if the 5min interval j immediately follows an announcement of data relating to macroeconomic indicator k. The coefficient bk measures the contribution of the announcement of news to the average absolute value of intraday jumps. Table 2 reports the total number of intraday jumps and the number of coincidences of intraday jumps and news announcements (N(Dk,j, jj)) along with coefficient estimates and inference for bk. The table also shows the results of a single regression combining all news into a single variable. number of findings emerge. First, the number of jumps in each market is less than the total number of news announcements meaning that many news announcements do not cause jumps. This is entirely expected, since only surprises relative to expectations should move prices significantly.8 Second, the number of occurrences of jumps which coincide with all news announcements is less than the total number of jumps, indicating that not all jumps can be explained by the arrival of US macroeconomic information. Third, the proportions of jumps corresponding to news, and hence the impact of news arrival varies across markets. Finally, for all news combined, bk is statistically significantly positive at the 1% level for all markets. This shows that the arrival of public information, in the form of US macroeconomic news, increases the absolute size of intraday jumps significantly. The extent of this effect varies across markets, with T-Bond futures showing particular sensitivity. Considering the results for the S&P 500 E-Mini futures, coincidences of intraday jumps and news, as a proportion of the total number of jumps, are large at 30%. The corresponding estimates of bk are statistically significantly positive, showing large increases in absolute jump size in response to macroeconomic information releases. Not only are the magnitude and statistical significance

8 There is a considerable literature investigating when and how macroeconomic announcements affect asset markets. Commonly, returns are affected by surprises relative to consensus expectations, and Balduzzi et al. (2001) show evidence that these expectations are unbiased, whilst volatility sometimes increases even without a surprise. Other contributing factors are the timing of the announcement in the economic calendar, a reversal of cumulative news flow and good and bad news in different phases of the business cycle. A more thorough analysis of these effects in the context of jumps is left for future work since it requires a much longer sample.

of these coefficients remarkable, but their scale relative to average absolute non-announcement jumps is also noteworthy (45.1% larger than xk) and demonstrates the economic significance of pure announcement effects. The important individual announcements, identified by number of coincidences of jumps and news and statistical significance of reactions, are GDP Advance, the Employment Report and FOMC monetary policy decisions. Whilst the coefficients are large and statistically significant, they also represent economically significant reactions relative to average absolute intraday jumps. The role of macroeconomic news is much more pronounced in the US 10-Year T-Bond market with far more occurrences and proportions of jumps corresponding to news announcements than the E-Mini and EUR-USD futures. These large proportions and statistically and economically significant coefficient values for all news confirm the importance of macroeconomic news announcements in this market, even though bk estimates are lower than for the E-Mini. Compared to the E-Mini and EURUSD, many individual indicators provide large values of N(Dk,j, jj) but low statistical significance levels for estimates of bk in Table 2, including Business Inventories, Chicago PMI, Construction Spending, Consumer Confidence, CPI, Housing Starts, Initial Claims, ISM Index, New Home Sales, New York Empire State Index, PPI and Retail Sales. Consistent with the findings for E-Mini futures, the dominant announcements in terms of numbers of coincidences of jumps and news and statistical significance are GDP Advance, the Employment Report and FOMC decisions. The Employment Cost Index also deserves mention for this market given its statistically significant coefficient estimate, but the reaction is dwarfed by the tremendous influence of the Employment Report on T-Bond futures. Finally, EUR-USD futures show more occurrences of jumps coinciding with news than the E-Mini, but less than the T-Bond with the corresponding reaction also tending to lie in between that measured for the other two markets. The Employment Report, FOMC and GDP Advance remain the most critical and influential, but the US Trade Balance is also prominent, as expected according to exchange rate determination models and prior empirical findings. 5.2. Intraday jumps and instantaneous macroeconomic news surprises The following equation considers the extent to which the information surprise of announcements impacts intraday jumps

jj ¼ -k þ ck Sk;j þ ek;j ;

ð11Þ

where standardised news Sk is measured as (Ak  Ek)/rk, the deviation of the actual announcement, Ak, from its expected value, Ek, standardised by the sample standard deviation of this deviation, rk. Regressions use only those intraday jumps that correspond to the relevant news announcements, provided there are at least five such coincidences, and the results are displayed in Table 3. Surprisingly, the results show that very few of the traditional economic indicators show a significant coefficient for E-Mini futures. FOMC policy decisions are significant at the 1% level, with higher (lower) than expected interest rates causing sharp negative (positive) jumps in the E-Mini. The only other indicators showing significant relationships are Construction Spending and Consumer Confidence where higher than expected figures is associated with positive jumps in the equity market. The T-Bond futures market shows many more instances of jumps caused by news and many more macroeconomic indicators showing significant coefficients on standardised news than the E-Mini. The signs of coefficients confirm that positive news surprises about US economic performance generate significant

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K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527

S&P 500 E-Mini: News related jumps 3 2 1 0 -1 -2 -3 1999

2000

2001

2002

2003

2004

2005

2006

2005

2006

S&P 500 E-Mini: Non-news related jumps 3 2 1 0 -1 -2 -3 1999

2000

2001

2002

2003

2004

US 10-Yr T-Bond: News related jumps 2 1 0 -1 -2 1999

2000

2001

2002

2003

2004

2005

2006

2005

2006

US 10-Yr T-Bond: Non-news related jumps 2 1 0 -1 -2 1999

2000

2001

2002

2003

2004

EUR-USD: News related jumps 2 1 0 -1 -2 1999

2000

2001

2002

2003

2004

2005

2006

2005

2006

EUR-USD: Non-news related jumps 2 1 0 -1 -2 1999

2000

2001

2002

2003

2004

Fig. 2. Daily news related and non-news-related jumps. Notes: This figure shows plots of news related and non-news related intraday jumps against the day on which they occur. Multiple intraday jumps on occasional days are cumulated to give a daily measure. The largest jump for the E-Mini of 5.584% on 15/10/98 is not shown fully for display purposes.

negative jumps in T-Bond futures, indicating that growth and associated inflationary prospects drive bond yields higher and

bond prices lower. Capacity Utilisation, Chicago PMI, Consumer Confidence, GDP Advance, Industrial Production, ISM

2518

K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527

Table 2 Intraday jumps and news announcements. S&P 500 E-Mini No. jumps: 365

All News Business Inventories Capacity Utilisation Chicago PMI Construction Spending Consumer Confidence CPI Current Account Employment Cost Index Existing Home Sales Factory Orders GDP Advance GDP Final GDP Prel Housing Starts Industrial Production Initial Claims ISM Manufacturing Leading Indicators Mich Sentiment Prel Mich Sentiment Rev New Home Sales NY Empire State Index Non-Farm Payrolls Personal Income Personal Spending PPI Productivity Prel Productivity Rev Retail Sales Trade Balance Unemployment Rate FOMC

US 10-Year T-Bond No. jumps: 596

EUR-USD No. jumps: 571

N(D, j)

bk

N(D, j)

bk

N(D, j)

112 8

0.217⁄⁄ 0.008 0.063 0.003 0.066 0.085 0.147⁄⁄ 0.230+ 0.122 0.071 0.394⁄

1 3

0.075⁄⁄ 0.151

11 10 4 2 3 2 2 27

0.080 0.040 0.038 0.133 0.040 0.040 0.251 0.237⁄⁄

7 5 1 10

0.006 0.105 0.099⁄⁄ 0.012

27 10

0.237⁄⁄ 1.100⁄⁄

0.085⁄⁄ 0.045 0.060 0.003 0.026+ 0.016 0.036+ 0.018 0.167⁄⁄ 0.057 0.069 0.086⁄⁄ 0.053 0.036 0.025 0.060 0.005 0.036+ 0.016 0.036 0.039⁄ 0.000 0.005 0.262⁄⁄ 0.003 0.003 0.018 0.010 0.091 0.010 0.024 0.262⁄⁄ 0.087⁄⁄

166 6

8 9 11 12 1 4 1 5 5

271 15 7 14 26 18 28 2 8 5 9 14 1 2 11 7 44 28 4 6 3 10 10 43 5 5 17 4 2 34 4 43 24

bk 0.069⁄⁄ 0.049⁄

6 9 16 8 2 4 5 3 9

0.003 0.012 0.010 0.033 0.006 0.046 0.049 0.019 0.068

2 3

0.027+ 0.039

26 11 1 2 1 7 6 38 2 2 15 4 1 16 18 38 12

0.001 0.010 0.076 0.039 0.139 0.009 0.014 0.199⁄⁄ 0.017⁄⁄ 0.017⁄⁄ 0.009 0.028 0.038 0.011 0.034 0.199⁄⁄ 0.055+

Notes: The table shows the impact of US macroeconomic announcements on significant intraday jumps detected according to Eq. (9). N(D, j) measures the number of intraday jumps coinciding with macroeconomic announcements, but each regression uses all significant intraday jumps. Only those announcements with at least one coincidence of news and jumps are displayed and All News refers to the aggregation of all separate announcements into one dummy variable. bk reports the estimated coefficient from Eq. (10) with ⁄⁄, ⁄ and + denoting statistical significance at the 1%, 5% and 10% levels respectively for a one-tailed test that bk = 0.

Manufacturing index, New Home Sales and Non-Farm Payrolls all show significant relationships at the 1% level, whilst coefficients for Employment Cost Index, Factory Orders, Initial Claims, PPI and Retail Sales are significant at the 5% level. For these indicators, R2 measures are staggering, mostly above 0.5, showing the very strong link between the information content of these news announcements and the size of the intraday jumps that they cause. Importantly, these reactions and R2 values for intraday jumps are substantially larger than previous findings for returns, further emphasising the role of macroeconomic news in short term price discovery. Although there are many important announcements identified for the T-Bond futures, Non-Farm Payrolls stands out as particularly dominant in terms of the number of jumps it causes, the size and significance of the coefficient and size of the R2. Significant relationships between jumps and other news surprises include Chicago PMI, Construction Spending, Consumer Confidence, Employment Cost Index, Factory Orders, ISM and New Home Sales announcements and Retail Sales data shows a particularly strong reaction. In support of these findings, EUR-USD futures also show important contributions to the timing and magnitude of jumps coming from macroeconomic news releases. Positive news of the US economy strengthens the dollar against the euro and significant relationships and high R2 are found for Consumer Confidence, ISM, New Home Sales, Non-Farm Payrolls, Trade Balance and the Unemployment Rate. Initial Claims and Retail Sales show large coincidences of jumps and news, but insignificant coefficients. It is also

noteworthy that almost half of Non-Farm Payrolls announcements coincide with jumps.9

6. The relative economic importance of macroeconomic newsrelated jumps 6.1. High frequency and daily return predictability and volatility persistence The effects of macroeconomic news announcements on asset returns are known to be short lived, with dramatic effects occurring within 5 min. Asset return volatility, however, tends to remain elevated for much longer, sometimes hours after the announcement. This section aims to provide further evidence on these claims by investigating the return predictability and volatility persistence of jumps. By separating jumps into those coinciding with macroeconomic news announcements and those not related to news, the analysis also attempts to understand the relative importance of news-related jumps to asset return dynamics. 9 The lack of observations in the estimations reported in Table 3 may present a concern. Repeating the regressions using all intraday jumps delivered more convincing results. Specifically, news response coefficients were slightly larger in absolute terms in many cases, the level of statistical significance of coefficients was higher owing to more reliable t-statistics, but, entirely as expected when including intraday jumps not directly relevant to the individual news announcement in question, R2 values were lower.

2519

K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527 Table 3 Intraday jumps and standardised news. S&P 500 E-Mini N(S, j) Business Inventories Capacity Utilisation Chicago PMI Construction Spending Consumer Confidence CPI Employment Cost Index Existing Home Sales Factory Orders GDP Advance Housing Starts Industrial Production Initial Claims ISM Manufacturing Mich Sentiment Prel New Home Sales NY Empire State Index Non-Farm Payrolls Personal Income Personal Spending PPI Productivity Prel Retail Sales Trade Balance Unemployment Rate FOMC

US 10-Year T-Bond

ck

8

R

0.010

8 9 11 12

2

0.00

0.257 0.304⁄ 0.256⁄⁄ 0.013

0.11 0.50 0.51 0.00

5 5

0.082 0.129

0.03 0.01

11 10

0.111 0.137

0.05 0.07

27

0.202

0.08

7 5 10

0.446 0.025 0.760

0.27 0.01 0.02

27 10

N(S, j)

ck

R

15 7 14 26 18 28 8 5 9 14 11 7 44 28 6 10 10 43 5 5 17

0.065 0.126⁄⁄ 0.157⁄⁄ 0.019 0.130⁄⁄ 0.049 0.288⁄ 0.141 0.060⁄ 0.226⁄⁄ 0.017 0.114⁄⁄ 0.070⁄ 0.155⁄⁄ 0.118 0.119⁄⁄ 0.076 0.361⁄⁄ 0.163 0.282+ 0.086⁄

0.06 0.84 0.40 0.01 0.68 0.05 0.39 0.38 0.55 0.45 0.01 0.79 0.14 0.50 0.44 0.46 0.14 0.44 0.17 0.69 0.18

34

0.04 0.30

0.158 0.741⁄⁄

EUR-USD 2

43 24

0.606

⁄

0.21

⁄

0.09 0.05

0.151 0.083

N(S, j) 6

R2

ck 0.223 +

0.19

6 9 16 8

0.193 0.496 0.188⁄⁄ 0.063

0.52 0.23 0.76 0.04

5

0.025

0.02

9

⁄

0.291

0.68

26 11

0.030 0.172⁄⁄

0.01 0.50

7 6 38

0.112⁄⁄ 0.127 0.349⁄⁄

0.32 0.17 0.49

15

0.038

0.02

16 18 38 12

+

0.663 0.209⁄⁄ 0.161⁄ 0.174

0.35 0.72 0.11 0.05

Notes: The table shows the impact of US macroeconomic news on intraday jumps, estimated according to Eq. (11). N(S, j) represents the number of intraday jumps coinciding with macroeconomic news announcements, reflecting the number of observations used in each regression. Only those indicators with at least five coincidences of news and jumps are displayed. ck reports the estimated coefficient from Eq. (20) with ⁄⁄, ⁄ and + denoting statistical significance at the 1%, 5% and 10% levels.

The method adopted follows the simple technique of Ederington and Lee (1993). Using only a sub-sample of the full data set that includes only intraday jumps, returns and volatility in subsequent intraday intervals are analysed as follows:

1 ; D

ð12Þ

1 ; D

ð13Þ

NNJ r j ¼ bNJ HF;D NJDj þ bHF;D NNJDj þ eHF;D;j ;

j ¼ 1; 2; . . . ;

NNJ r 2j ¼ bNJ HF;D NJDj þ bHF;D NNJDj þ eHF;D;j ;

j ¼ 1; 2; . . . ;

where rj represents the 5-min return in intraday interval j, which is equivalent to the intraday jump if a jump was observed at interval j, and NJDj and NNJDj are dummy variables equal to one if the intraday jump is news related and non-news related, respectively. The subscript referencing the day has been omitted to maintain simplicity in the notation. Eqs. (12) and (13) are estimated first using only the intraday jumps, and following the procedure of Ederington and Lee (1993), are then estimated for returns and squared returns for each of 24 intraday intervals (2 h) following the jumps. In order to test the dynamics of returns and volatility following jumps at the daily level, a similar procedure is followed where daily returns and realised variation are regressed on news jump dummies and nonnews jumps dummies as follows: NNJ r t ¼ bNJ DLY;D NJDt þ bDLY;D NNJDt þ eDLY;D;t ;

RV t ¼

bNJ DLY;D NJDt

þ

bNNJ DLY;D NNJDt NJ

þ eDLY;D;t : NNJ

persistence, is observed for news-related jump dummies compared to non-news-related jump dummies, confirming their relative importance for asset return dynamics. This persistence is apparent at the daily level as well as the intraday frequency, which further emphasises the effect that the occurrence of jumps, and news-related jumps in particular, have on return volatility. The only exception to this pattern is at the daily level for EUR-USD, where coefficients tend to be larger following non-news-related jump dummies, showing that the occurrence of news-related jumps has important short-term impacts on volatility, whereas nonnews-related jump dummies affect lower frequency dynamics in the foreign exchange market. Table 4 documents the important influence of jumps on volatility, however, the lack of evidence of return predictability may well be explained by the offsetting effects of positive and negative jumps.10 6.2. Rolling measures of jump risks This section extends the analysis of return predictability to incorporate the more recent, sophisticated and relevant techniques of Wright and Zhou (2009) and Tauchen and Zhou (2011). Given the relatively infrequent occurrence of jumps in asset prices, the innovative contribution of their work defines more appropriate

ð14Þ ð15Þ

Estimated values of b and b are reported in Table 4 for squared returns and realised variation. The results for returns show no evidence of predictability so are not displayed. Table 4, however, shows more interesting and important results, with significant volatility persistence at all lags, in all markets, at both frequencies and following both types of jump dummies. More importantly, larger coefficients, and therefore stronger volatility

10 A number of further investigations reveal insignificant results. First, there is no return predictability, neither in response to the actual jump (as opposed to jump dummies) nor following individual macroeconomic news announcements (tested using announcement dummies, intraday jumps and standardised news measures). Second, large reactions occur at the time of announcements that are related to the news surprise, but there is little predictability or persistence beyond the initial intraday jumps or day of the announcement. Third, an analysis of asymmetry confirms the importance of news-related jumps, but shows very little evidence of asymmetry between positive and negative jumps or between positive and negative news surprises in predictability or persistence. Only the E-Mini shows tentative evidence of stronger volatility persistence following negative jumps than positive ones.

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K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527

Table 4 Volatility persistence using intraday jump dummies. S&P 500 E-Mini

JV ht

US 10-Year T-Bond

EUR-USD

Panel a: High frequency squared returns LAG

bNJ HF;D

bNNJ HF;D

bNJ HF;D

bNNJ HF;D

bNJ HF;D

bNNJ HF;D

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0.8597⁄⁄ 0.1001 0.0212⁄⁄ 0.0296⁄ 0.0141⁄⁄ 0.0209⁄⁄ 0.0125⁄⁄ 0.0167⁄⁄ 0.0167⁄⁄ 0.0106⁄⁄ 0.0090⁄⁄ 0.0146⁄⁄ 0.0238⁄⁄ 0.0260⁄⁄ 0.0217⁄⁄ 0.0177⁄⁄ 0.0192⁄⁄ 0.0217⁄⁄ 0.0289⁄⁄ 0.0202⁄⁄ 0.0190⁄⁄ 0.0166⁄⁄ 0.0125⁄⁄ 0.0134⁄⁄ 0.0165⁄⁄

0.3367⁄⁄ 0.0495⁄⁄ 0.0158⁄⁄ 0.0136⁄⁄ 0.0208⁄⁄ 0.0165⁄⁄ 0.0154⁄⁄ 0.0169⁄⁄ 0.0109⁄⁄ 0.0114⁄⁄ 0.0109⁄⁄ 0.0115⁄⁄ 0.0148⁄⁄ 0.0084⁄⁄ 0.0103⁄⁄ 0.0074⁄⁄ 0.0075⁄⁄ 0.0106⁄⁄ 0.0127⁄⁄ 0.0084⁄⁄ 0.0080⁄⁄ 0.0079⁄⁄ 0.0097⁄⁄ 0.0065⁄⁄ 0.0082⁄⁄

0.1044⁄⁄ 0.0092⁄⁄ 0.0064⁄⁄ 0.0040⁄⁄ 0.0035⁄⁄ 0.0028⁄⁄ 0.0024⁄⁄ 0.0024⁄⁄ 0.0024⁄⁄ 0.0025⁄⁄ 0.0017⁄⁄ 0.0027⁄⁄ 0.0021⁄⁄ 0.0021⁄⁄ 0.0015⁄⁄ 0.0014⁄⁄ 0.0017⁄⁄ 0.0018⁄⁄ 0.0019⁄⁄ 0.0017⁄⁄ 0.0014⁄⁄ 0.0017⁄⁄ 0.0011⁄⁄ 0.0011⁄⁄ 0.0014⁄⁄

0.0459⁄⁄ 0.0111⁄⁄ 0.0030⁄⁄ 0.0026⁄⁄ 0.0037⁄⁄ 0.0024⁄⁄ 0.0020⁄⁄ 0.0015⁄⁄ 0.0018⁄⁄ 0.0022⁄⁄ 0.0019⁄⁄ 0.0017⁄⁄ 0.0013⁄⁄ 0.0014⁄⁄ 0.0015⁄⁄ 0.0012⁄⁄ 0.0014⁄⁄ 0.0011⁄⁄ 0.0013⁄⁄ 0.0012⁄⁄ 0.0014⁄⁄ 0.0013⁄⁄ 0.0011⁄⁄ 0.0010⁄⁄ 0.0013⁄⁄

0.1304⁄⁄ 0.0131⁄⁄ 0.0074⁄⁄ 0.0087⁄⁄ 0.0068⁄⁄ 0.0056⁄⁄ 0.0065⁄⁄ 0.0034⁄⁄ 0.0028⁄⁄ 0.0029⁄⁄ 0.0034⁄⁄ 0.0051⁄⁄ 0.0051⁄⁄ 0.0042⁄⁄ 0.0032⁄⁄ 0.0037⁄⁄ 0.0036⁄⁄ 0.0040⁄⁄ 0.0056⁄⁄ 0.0035⁄⁄ 0.0036⁄⁄ 0.0032⁄⁄ 0.0020⁄⁄ 0.0027⁄⁄ 0.0026⁄⁄

0.0734⁄⁄ 0.0152⁄⁄ 0.0052⁄⁄ 0.0044⁄⁄ 0.0036⁄⁄ 0.0050⁄⁄ 0.0039⁄⁄ 0.0030⁄⁄ 0.0031⁄⁄ 0.0031⁄⁄ 0.0029⁄⁄ 0.0024⁄⁄ 0.0027⁄⁄ 0.0028⁄⁄ 0.0031⁄⁄ 0.0023⁄⁄ 0.0029⁄⁄ 0.0031⁄⁄ 0.0030⁄⁄ 0.0018⁄⁄ 0.0030⁄⁄ 0.0023⁄⁄ 0.0017⁄⁄ 0.0021⁄⁄ 0.0021⁄⁄

bNJ DLY;D

bNNJ DLY;D

bNJ DLY;D

bNNJ DLY;D

Panel b: Daily realised variation LAG

bNJ DLY;D

bNNJ DLY;D ⁄⁄

0 1 2 3 4 5 6 7 8 9 10

⁄⁄

1.9997 0.9557⁄⁄ 1.1443⁄⁄ 0.9983⁄⁄ 1.0764⁄⁄ 1.0457⁄⁄ 1.1099⁄⁄ 1.2231⁄⁄ 1.0885⁄⁄ 1.2463⁄⁄ 1.0607⁄⁄

1.2830 1.0704⁄⁄ 0.9889⁄⁄ 0.8724⁄⁄ 0.9714⁄⁄ 0.9469⁄⁄ 0.9279⁄⁄ 0.9776⁄⁄ 0.9680⁄⁄ 0.8927⁄⁄ 0.9045⁄⁄

⁄⁄

0.2136 0.1242⁄⁄ 0.1186⁄⁄ 0.0967⁄⁄ 0.1031⁄⁄ 0.1078⁄⁄ 0.1011⁄⁄ 0.0921⁄⁄ 0.1035⁄⁄ 0.0963⁄⁄ 0.1100⁄⁄

⁄⁄

0.1234 0.0857⁄⁄ 0.0844⁄⁄ 0.0833⁄⁄ 0.0961⁄⁄ 0.0858⁄⁄ 0.0986⁄⁄ 0.1120⁄⁄ 0.0928⁄⁄ 0.0847⁄⁄ 0.0966⁄⁄

⁄⁄

0.2883 0.1517⁄⁄ 0.1505⁄⁄ 0.1737⁄⁄ 0.1869⁄⁄ 0.1701⁄⁄ 0.1664⁄⁄ 0.1538⁄⁄ 0.1655⁄⁄ 0.1615⁄⁄ 0.1629⁄⁄

0.2417⁄⁄ 0.1895⁄⁄ 0.1982⁄⁄ 0.1866⁄⁄ 0.1993⁄⁄ 0.1961⁄⁄ 0.1844⁄⁄ 0.1907⁄⁄ 0.1925⁄⁄ 0.1881⁄⁄ 0.1922⁄⁄

Notes: This table reports the coefficient estimates of Eqs. (13) and (15) investigating the high frequency and daily volatility persistence following news related and nonnews related intraday jumps. Panel a shows the results for high frequency persistence in the 2 h following jumps, whilst Panel b shows the results for persistence up to 2 weeks after jumps. ⁄⁄, ⁄ and + indicate significant coefficients at the 1%, 5% and 10% levels respectively.

rolling measures of jump risk and then investigates whether these smoothed measures are associated with risk premia. The rolling measure of realised variation is defined as the average daily measure over a 22 day month:

1 RV ht ¼ h  22

h221 X

RV tl ;

ð16Þ

l¼0

where h measures the length of the rolling window. Following Wright and Zhou (2009), the window size (h) remains as one month throughout, but the analysis here experiments with daily and monthly measures by rolling the window along the sample by 1 day or 1 month respectively. The jump risk measures of jump intensity (JI), jump mean (JM) and jump volatility (JV) are defined respectively as

JIht ¼

X 1 h221 JDDLY tl ; h  22 l¼0

JMht ¼

DLY J DLY tl  JDtl Ph221 DLY ; JDtl l¼0

ð19Þ

where h measures the rolling window length (h = 12 and 24 months), Jt represents a daily measure of the jump size and JDt is a dummy variable if a jump occurs on a particular day. To incorporate the superior information gained from the intraday jump detection method of Andersen et al. (2007c), this study uses the intraday jumps detected by Eq. (9) as the variable Jt.11 The above risk measures are calculated both daily and monthly by rolling the window forward by 1 day or 1 month respectively. In order to assess the relative importance of news-related jumps versus non-news-related jumps, the jump risk measures are re-calculated to provide separate risk measures according to the jump type. Fig. 3 plots the monthly measures calculated using a 24-month window, which reveal interesting time variation in some measures. The intensity of jumps is rising steadily for most markets, both jump types and for most of the sample demonstrating that jumps are becoming more frequent. Non-news-related jumps in the T-Bond drops to mid-2003 then rises again to mid-2006 showing the only variation to this pattern and confirming the clustering of jumps in 1999 and from 2004 in Fig. 2. News-related jump intensity in the E-Mini falls slightly over the sample, suggesting that macroeconomic news has become less important for this market over the sample as economic conditions stabilised after the bursting of the technology bubble. Non-news jump intensity is higher than news-related jump intensity for the E-Mini and EUR-USD, but is lower for the T-Bond in much of the sample, confirming the importance of macroeconomic news announcements for this asset class. The jump mean is stable for the T-Bond and EUR-USD markets, though, interestingly, news and non-news-related jump means tend to have opposite signs. The news-related jump mean for the E-Mini shows far more variation, dropping substantially up to the start of 2003 as the result of the recession in 2001. It then increases during the remainder of the sample coinciding with the vigorous economic expansion of 2003. The pattern for the nonnews-related jump mean is less dramatic, but is the inverse of that for news-related jumps. Although Section 5 does not find strong evidence of news effects from individual announcements in the E-Mini, the rolling jump means demonstrate that equity market jumps are related to macroeconomic conditions. Finally, jump volatility is constant across the sample for the T-Bond and EUR-USD, but decreases for the E-Mini. Jump volatility in equities tends to be highest during recessions and lower during expansions. The identification of risk premia for future returns can then be investigated by estimating various regressions which are nested in the following expression:

rtþ1 ¼ b0 þ b1 RV 1t þ b2 JIht þ b3 JMht þ b4 JV ht þ etþ1 ;

ð20Þ

and the effects of news-related jumps and non-news-related jumps are separated in a similar expression:

rtþ1 ¼ b0 þ b1 RV 1t þ b5 NJIht þ b6 NJMht þ b7 NJV ht þ b8 NNJIht þ b9 NNJMht þ b10 NNJV ht þ etþ1 :

ð21Þ

Tables 5–7 report the estimation results for the E-Mini, T-Bond and EUR-USD futures markets respectively with various panels in each table investigating the effects of using 12-month and 24month rolling measures on future daily and monthly returns.

ð17Þ

Ph221 l¼0

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uPh221 DLY u ðJ tl  JMht Þ2  JDDLY tl ; ¼ t l¼0 Ph221 JDDLY tl l¼0

ð18Þ

11 This contrasts with the method of Wright and Zhou (2009) and Tauchen and Zhou (2011) who rely on the assumption that a jump dominates the daily return. Whilst this is both a realistic and an accurate assumption, this study aims to contribute to the literature by implementing the superior information held in intraday jumps. Multiple jumps on some days are cumulated to provide the daily measure.

2521

K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527

-0.1

-0.2

-0.2 Jul-00 0.2

0

0 Jul-06

0.2

Jul-05

0.4

Jul-04

0.4

Jul-03

0.6

Jul-02

0.6

Jul-06

0.8

Jul-05

0.8

Jul-04

1

Jul-03

1

Jul-02

1.2

Jul-01

1.2

Jul-01

NNJV

Jul-00

NJV

Jul-04

-0.1

Jul-03

0

Jul-02

0

Jul-06

0.1

Jul-05

0.1

Jul-04

0.2

Jul-03

0.2

Jul-02

0.3

Jul-01

0.3

Jul-00

0.4

Jul-01

NNJM

0.4

Jul-00

Jul-06

Jul-00

NJM

Jul-06

0 Jul-05

0

Jul-05

0.05

Jul-04

0.05

Jul-03

0.1

Jul-06

0.1

Jul-05

0.15

Jul-04

0.15

Jul-03

0.2

Jul-02

0.2

Jul-01

0.25

Jul-00

0.25

Jul-02

NNJI

Jul-01

NJI

Fig. 3. Twenty-four-month rolling measures of jumps risks. Notes: The figures show monthly rolling measures of jump intensity (JI), jump mean (JM) and jump variation (JV), as defined by Eqs. (17)–(19), calculated using 24-month windows, but separately for news and non-news-related jumps. The dashed, solid and dotted lines represent the EMini, T-Bond and EUR-USD respectively.

2522

K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527

significant and negative in Panels b and d where h = 24, showing that there are clear risk premia associated with jump risks. Consistent with Table 5, more interesting results are revealed when separating news related and non-news-related jump risks. In support of the findings of Wright and Zhou (2009), the rolling measure of jump mean is significant and negative, but the innovative results of this study show that this is only the case for news-related jumps, a result which is strongest in Panel d, arguably the most important in terms of the experimental design. News-related jump intensity also has a significant and negative effect when the measures use the longer window of h = 24, which again, is most apparent for monthly data in Panel d. News-related jump variation does not play an important role in predicting future returns, although there is minor evidence that it has a significant and positive effect in Panel b that uses daily data and h = 24. In contrast to Table 5, there is one non-news-related jump risk variable that is important to TBond returns. Non-news-related jump variation shows a positive and significant effect on returns when h = 12 suggesting that in shorter windows, in which most jump activity is likely to be non-news related, non-news jump variation is important. Finally, Table 7 reports the results for the EUR-USD futures contract. Despite the evidence of Section 5 that some intraday jumps are strongly linked to macroeconomic news announcements, the evidence of Table 7 is less convincing of the importance of newsrelated jumps for predicting longer horizon returns. Indeed, realised variation, jump risk measures and news-related jump risk

For the E-Mini futures in Table 5, realised variation is significant and positive in all four panels showing the conventional positive relation between risk and return. Also, throughout the entirety of Table 5 both jump risk measures and non-news-related jump risk measures are insignificant, displaying no influence on future returns. In contrast, news-related jump risk measures are significant, illustrating the importance of news-related jumps for long horizon returns and the importance of identifying separately those jumps that relate to macroeconomic news announcements. Specifically, when considered individually, the coefficients on news-related jump intensity and jump mean are significant and negative when using 24-month measures, whilst that on news-related jump volatility is significant and positive for the monthly measures and for both 12-month and 24-month window versions. High newsrelated jump intensity and larger positive news-related jumps are associated with lower future returns, whereas high news-related jump variation is followed by higher returns. When combining news-related and non-news-related jump risk measures, there are clear differences between the sizes of estimated coefficients, but only the news-related jump mean measure retains significant coefficients (albeit at the 10% level in Panel d). Very similar findings emphasising the importance of the two types of jumps emerge in Table 6 for the T-Bond futures. Realised variation has a significant and positive effect for monthly returns, however, in contrast to the E-Mini, jump intensity and jump mean measures that include both news and non-news-related jumps are

Table 5 Rolling jump risk measures for S&P 500 E-Mini returns. b0

RV 1t

JIht

JM ht

JV ht

Panel a: Daily returns and rolling measures, h = 12 months 0.10⁄⁄ 0.06⁄ 0.08 0.24 0.03 0.20 0.09 0.40

Panel b: Daily returns and rolling measures, h = 24 months 0.10⁄⁄ 0.06⁄ 0.09 0.31 0.03 1.27 0.14 0.89

Panel c: Monthly returns and rolling measures, h = 12 months 2.20⁄⁄ 1.21⁄⁄ 0.80 0.86 0.60 5.93 1.60 5.80

Panel d: Monthly returns and rolling measures, h = 24 months 2.20⁄⁄ 1.21⁄⁄ 1.47 3.32 0.24 19.88 2.44 13.81

b0

NJIht

0.04 0.03 0.03 0.10 0.03 0.01

0.03

0.03 0.02 0.03 0.03 0.03 0.03

1.40⁄

0.99+ 0.71 0.67+ 1.07 0.66 1.06

2.18

0.66 0.56 0.55 0.63 0.64 0.76

NJM ht

NJV ht

0.19 0.36+

b0

NNJIht

0.00 0.01 0.00

0.06

0.07

NNJM ht

NNJV ht

0.04 0.06 0.45 0.26

0.10

0.00

0.04

0.37⁄⁄ +

0.54

0.04 0.05 0.03

0.11 0.12 0.11 0.40

0.89 ⁄

0.60

0.54 0.56

0.45

3.01 ⁄⁄

8.45

0.33 0.56 0.75

0.97 0.53 0.34 3.21

4.07

8.23

0.19

2.11

1.51

26.92⁄⁄ 7.57⁄⁄ 12.47⁄

0.50 1.08 0.49

2.11 2.63 2.33 5.30

14.98 11.16+

8.25 9.64

11.89

Notes: This table shows the results of various regressions of future S&P 500 E-Mini returns on rolling jump risk measures all of which are nested in Eqs. (20) and (21). Jump risk measures are defined by Eqs. (16)–(19) and their news and non-news related versions. Panels a and b report the results for daily returns and daily rolling measures, whilst Panels c and d show the results for monthly data. Panels a and c use 12-month rolling windows and Panels b and d use 24-month windows. Each panel reports the results of 13 regressions, the first four rows represent univariate regressions on each jump risk measure, whilst the regressions in the bottom three rows include news related and nonnews-related jump risk measures. ⁄⁄, ⁄ and + denote statistical significance at the 1%, 5% and 10% levels respectively.

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K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527 Table 6 Rolling jump risk measures for US 10-Year T-Bond returns. b0

RV 1t

JIht

JM ht

JV ht

Panel a: Daily returns and rolling measures, h = 12 months 0.00 0.13 0.03 0.05 0.01 0.14 0.03 0.14

Panel b: Daily returns and rolling measures, h = 24 months 0.00 0.13 0.15⁄ 0.49⁄ 0.15⁄ 0.89⁄ 0.08+ 0.44+

Panel c: Monthly returns and rolling measures, h = 12 months 0.63+ 7.01⁄⁄ 0.89 1.86 0.09 3.00 0.84 3.35

Panel d: Monthly returns and rolling measures, h = 24 months 0.63+ 7.01⁄⁄ 3.08⁄ 9.67⁄ 2.79⁄ 16.55⁄ 1.53+ 8.56

b0

NJIht

NJM ht

0.01 0.01 0.01 0.01 0.01 0.08⁄

0.10

0.01 0.03⁄ 0.03⁄ 0.18⁄⁄ 0.03⁄ 0.12

1.15

0.34+ 0.52⁄⁄ 0.44⁄ 0.31 0.53⁄ 1.90+

5.92

0.33 0.66⁄⁄ 0.54⁄ 3.62⁄⁄ 0.62⁄ 0.80

45.26+

NJV ht

b0

0.10

0.04 0.02 0.08⁄

0.03 0.23

NNJIht

NNJMht

0.20 0.11 0.38⁄ 0.19

0.07

0.14 0.37⁄

0.01

1.13⁄ 0.91⁄

0.08 0.07 0.08

0.80⁄

0.20 0.15 0.40 0.38 0.11

1.06

0.96

0.31

11.23⁄ 7.02

0.10 0.18 1.66

3.93

1.52 0.54 8.36+ 3.61

10.25+

1.59 14.16⁄

3.61

27.99⁄⁄ 15.45 15.18

NNJV ht

2.08 1.83 0.01

⁄⁄

5.61 4.32 1.59 7.68

35.95

⁄

11.96 6.58

13.80

Notes: This table shows the results of various regressions of future US 10-Year T-Bond returns on rolling jump risk measures all of which are nested in Eqs. (20) and (21). Jump risk measures are defined by Eqs. (16)–(19) and their news and non-news related versions. Panels a and b report the results for daily returns and daily rolling measures, whilst Panels c and d show the results for monthly data. Panels a and c use 12-month rolling windows and Panels b and d use 24-month windows. Each panel reports the results of 13 regressions, the first four rows represent univariate regressions on each jump risk measure, whilst the regressions in the bottom three rows include news related and nonnews-related jump risk measures. ⁄⁄, ⁄ and + denote statistical significance at the 1%, 5% and 10% levels respectively.

measures show little effect on returns. However, consistent with Table 6 for the T-Bond futures market, non-news-related jump variation generates a significant relationship for shorter windows, but this is unexpectedly negative. Non-news-related jump intensity shows a significant negative coefficient in Panels a and c, whilst non-news-related jump mean exerts a significant negative relationship in Panel b. Panel d, arguably the most relevant, shows no significant risk premia associated with jump risks. In brief summary, separating news-related jumps from non-news-related jumps reveals that there is clear evidence supporting the importance of the influence of news-related jump risk measures for predicting future returns in the E-Mini and T-Bond futures market. News-related jump risks are associated with significant risk premia. Non-news-related jumps play only a very minor role for the T-Bond and EUR-USD futures, which is apparent only when using short windows to measure jump risks.

that may be important for EUR-USD. The investigation of each of these sources of news, although of interest in their own right, seems daunting considering the scale of information collection required. Rather, this study uses 5-min data on trade volume, number of ticks and average size of trade to serve as proxies for the arrival of information to the market that may cause jumps. It is important to recognise, however, that these microstructure variables may also represent the trading behaviour of market participants based on their private information. This section aims to investigate whether such variables can detect important discrepancies in market conditions between jumps related to US macroeconomic news and jumps that may be caused by other public or private information.12 In order to avoid any problems associated with intraday patterns, each variable is standardised by dividing by its intraday average value. The following equations are then estimated to determine the impact of jumps on trading volume.

6.3. Intraday jumps and microstructure variables

V j ¼ aV þ bV JDj þ eV;j ;

ð22Þ

Whilst the evidence presented so far suggests that news-related jumps are more important than non-news-related jumps in statistical and economic terms, an important issue that remains to be addressed is the possible explanation for the occurrence of nonnews-related jumps, which constitute two thirds of all jumps. Of course, there are many public information announcements not captured here that could help explain other jumps such as analyst reports and corporate earnings for the E-Mini, Treasury bond auctions for the T-Bond and macroeconomic news from the Eurozone

V j ¼ aV þ bNJ;V NJDj þ bNNJ;V NNJDj þ eV;j ;

ð23Þ

V j ¼ bNJ;V NJDj þ bNNJ;V NNJDj þ eV;j ;

ð24Þ

12 See Chan and Fong (2006) and Giot et al. (2010) for other investigations of the relationships between microstructure variables and daily realised variation and its continuously varying and discontinuous jump components.

2524

K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527

Table 7 Rolling jump risk measures for EUR-USD returns. b0

RV 1t

JIht

JMht

JV ht

Panel a: Daily returns and rolling measures, h = 12 months 0.09⁄⁄ 0.23 0.04 0.02 0.04 0.07 0.05 0.00

Panel b: Daily returns and rolling measures, h = 24 months 0.09⁄⁄ 0.23 0.09 0.12 0.08⁄ 0.31 0.08 0.13

Panel c: Monthly returns and rolling measures, h = 12 months 3.40 1.67⁄ 1.02 0.16 0.91 0.73 1.22 1.22

Panel d: Monthly returns and rolling measures, h = 24 months 1.67⁄ 3.40 2.16 3.38 1.89⁄ 7.66 2.09 4.60

b0

NJIht

0.05⁄⁄ 0.04⁄⁄ 0.05⁄⁄ 0.06 0.05⁄⁄ 0.20⁄⁄

0.44

0.06⁄⁄ 0.05⁄⁄ 0.06⁄⁄ 0.01 0.06⁄⁄ 0.38⁄

1.13+

0.98⁄⁄ 0.99⁄⁄ 1.00⁄⁄ 1.46 1.00⁄⁄ 4.11⁄⁄

3.49

1.33⁄⁄ 1.14⁄⁄ 1.13⁄⁄ 0.75 1.18⁄⁄ 6.04

18.08+

NJM ht

NJV ht

0.03 0.22

b0

0.17⁄⁄ 0.10+ 0.19⁄⁄

0.17

NNJIht

NNJM ht

NNJV ht

0.39⁄ 0.14 0.50⁄ 0.13

0.07

0.28 0.47+

0.05

0.24 0.04

0.35⁄ 0.28⁄⁄ 0.09

0.89+ 0.62⁄ 0.12 0.62

0.81 +

0.31

0.57 0.66⁄

0.30 2.81

0.27

3.30⁄⁄ 1.75 4.03⁄⁄

4.24

7.27⁄ 2.21 10.69⁄ 4.38

0.04

2.84 10.54+

0.36

4.95 5.77

5.64 4.81⁄ 1.39

13.57 9.79 0.68 9.26

14.91 5.29

3.39 10.40

3.51

Notes: This table shows the results of various regressions of future EUR-USD returns on rolling jump risk measures all of which are nested in Eqs. (20) and (21). Jump risk measures are defined by Eqs. (16)–(19) and their news and non-news related versions. Panels a and b report the results for daily returns and daily rolling measures, whilst Panels c and d show the results for monthly data. Panels a and c use 12-month rolling windows and Panels b and d use 24-month windows. Each panel reports the results of 13 regressions, the first four rows represent univariate regressions on each jump risk measure, whilst the regressions in the bottom three rows include news related and nonnews-related jump risk measures. ⁄⁄, ⁄ and + denote statistical significance at the 1%, 5% and 10% levels respectively.

where Vj measures excess volume in interval j relative to the sample intraday average volume for interval j. JD, NJD and NNJD represent jump, news-related jump and non-news-related jump dummy variables respectively. Eqs. (22) and (23) use the full sample data, whilst Eq. (24) uses a sub-sample of intervals that contain intraday jumps only. Following the approach of Ederington and Lee (1993), Eq. (24) is then re-estimated for each of the intraday periods in the 2 h after intraday jumps to assess the persistence of volume following jumps. The analysis is then repeated replacing volume for the number of ticks and average size of trades. Tables 8 and 9 report the coefficient estimates for volume and number of ticks.13 Volume, as shown in Panels a and b of Table 8 is significantly higher when jumps occur, and significantly higher than usual for both types of jumps. For the T-Bond and EUR-USD markets, volume is significantly higher at the time of news-related jumps compared to non-news-related jumps, again confirming the importance of these jumps for financial markets, whereas for the E-Mini, volume is much higher at the time of non-news-related jumps. The significant coefficients for non-news-related jumps for all three markets, and the E-Mini especially, suggests that these types of jumps may well be the result of other public news announcements or private information shocks. Panel c shows that volume drops substantially in the intervals following intraday jumps, but the significant coefficients at numerous lags shows persistent high volume following T-Bond and EUR-USD news-related

13 Information for the average trade size regressions is not shown explicitly since it duplicates the findings for volume and number of ticks.

jumps and E-Mini non-news-related jumps. There is clearly higher volume for news-related jumps at all lags in the T-Bond and EURUSD market, whereas, interestingly, volume is dramatically higher following non-news-related jumps for the E-Mini, possibly suggesting the arrival of other public or private information. Table 9 for the number of ticks confirms these results. The number of ticks is substantially higher following news-related jumps for the T-Bond and EUR-USD futures, whilst the number of ticks is vastly higher for non-news-related jumps in the E-Mini. The evidence from volume and number of ticks is unequivocal in highlighting discrepancies between news and non-news-related jumps. It also illustrates important discrepancies between the asset classes under consideration. These microstructure variables are indeed influenced by the presence of jumps with news-related jumps exerting significant surges in the bond and foreign exchange futures and non-news jumps generating vast increases in stock market futures. This raises the interesting an important challenge of exploring the nature and causes of the microstructure effects that underlie these non-news-related jumps. 6.4. Clustering of intraday jumps and co-jumps This final section again attempts to uncover differing features of news and non-news-related jumps. Figs. 1 and 2 show plots of daily jump variation and intraday jumps respectively and indicate some clustering of jumps. At the intraday level, a preliminary examination of jump clustering can simply identify the number of occasions when intraday jumps follow each other quite closely.

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K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527 Table 8 Excess volume and intraday jumps. S&P 500 E-Mini

US 10-Yr T-Bond

EUR-USD

Panel (a) All data

aV 19.34 Panel (b) All data NJDj

JDj 10,032.04⁄⁄

aV

JDj 48.13

NNJDj

NJDj

12,905.85⁄⁄

15,653.74⁄⁄

Panel (c) Jumps only LAG NJDj

NNJDj

NJDj

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

12,886.51⁄⁄ 8,140.19⁄⁄ 4,226.79⁄⁄ 3,557.17⁄⁄ 3,250.55⁄⁄ 2,771.64⁄⁄ 2,818.35⁄⁄ 2,191.86⁄⁄ 1,887.80⁄⁄ 1,994.69⁄⁄ 2,113.22⁄⁄ 2,176.61⁄⁄ 1,241.41⁄⁄ 1,355.30⁄⁄ 1,600.63⁄⁄ 976.63⁄⁄ 1,133.46⁄⁄ 1,434.05⁄⁄ 1,234.13⁄⁄ 936.25⁄ 1,290.84⁄⁄ 925.99⁄⁄ 1,015.80⁄⁄ 840.59⁄ 778.50⁄

15,605.61⁄⁄ 9,784.83⁄⁄ 7,362.64⁄⁄ 5,990.66⁄⁄ 5,083.63⁄⁄ 4,656.54⁄⁄ 3,958.63⁄⁄ 3,790.54⁄⁄ 3,538.07⁄⁄ 3,506.40⁄⁄ 2,424.48⁄⁄ 3,551.65⁄⁄ 3,186.71⁄⁄ 3,020.77⁄⁄ 2,397.14⁄⁄ 2,501.01⁄⁄ 2,177.82⁄⁄ 1,807.48⁄⁄ 1,286.88+ 1,292.16⁄ 1,171.22⁄ 1,655.30⁄⁄ 1,408.64⁄⁄ 1,446.25⁄⁄ 990.37⁄

3,540.31⁄⁄

3,520.97⁄⁄ 1,706.45 438.99 257.79 247.31 28.43 300.39 345.61 335.67 595.63+ 263.81 156.69 66.44 500.28 661.89 790.20 511.51 653.17 818.80 525.87 552.32 368.89 4.76 115.54 437.30

12,306.63⁄⁄ NNJDj 9,515.66⁄⁄ NNJDj 9,467.53⁄⁄ 9,545.35⁄⁄ 4,986.24⁄⁄ 3,987.92⁄⁄ 3,444.24⁄⁄ 3,175.59⁄⁄ 2,145.73⁄⁄ 1,799.70⁄⁄ 1,018.07⁄ 882.43⁄ 1,621.98⁄ 878.92⁄ 972.01+ 630.29 741.92 601.76 282.42 164.93 274.06 442.91 293.93 422.88 511.88 19.48 237.82

aV

JDj 6.54

1,667.72⁄⁄

NJDj

NNJDj

2,572.11⁄⁄

1,297.03⁄⁄

NJDj

NNJDj

2,565.56⁄⁄ 1,699.54⁄⁄ 1,218.54⁄⁄ 1,065.25⁄⁄ 929.15⁄⁄ 763.38⁄⁄ 742.08⁄⁄ 601.69⁄⁄ 534.42⁄⁄ 568.87⁄⁄ 377.94⁄⁄ 454.57⁄⁄ 425.52⁄⁄ 395.67⁄⁄ 382.65⁄⁄ 311.82⁄⁄ 305.49⁄⁄ 313.09⁄⁄ 315.38⁄⁄ 347.36⁄⁄ 361.72⁄⁄ 284.37⁄⁄ 255.60⁄⁄ 191.48⁄⁄ 228.51⁄⁄

1,290.49⁄⁄ 941.46⁄⁄ 586.07⁄⁄ 397.89⁄⁄ 272.09⁄⁄ 259.03⁄⁄ 245.26⁄⁄ 176.40⁄⁄ 117.75⁄⁄ 102.71⁄ 98.69⁄ 86.89⁄ 80.14⁄ 96.40⁄ 128.23⁄ 119.37⁄⁄ 98.91⁄ 104.50⁄ 81.40⁄ 57.31+ 81.99⁄ 73.70⁄ 53.07 58.89+ 38.39

Notes: This table reports the coefficient estimates of Eqs. (22)–(24) that investigate the effect of intraday jumps on trading volume in excess of the sample intraday average volume. JD, NJD and NNJD refer to jump, news-related jump and non-news-related jump dummy variables respectively. Panel a shows the estimates from Eq. (22) that uses all data in the sample; Panel b reports the estimates of Eq. (23) again using the full sample and Panel c displays the results from estimations of Eq. (24) that investigates instantaneous and persistent effect of jumps on volume and uses only the sub-sample of intraday jumps and the 24 intervals that follow them. ⁄⁄, ⁄ and + denote statistical significance at the 1%, 5% and 10% levels respectively.

Although not reported in full to conserve space, these occurrences of clustering vary across the three markets considered. For the EMini, there are very few instances of intraday jumps occurring in quick succession, which confirms the previous results that jumps are rare events in the equity market. For the T-Bond and EURUSD, there are more occurrences of intraday jumps and more occasions when jumps cluster. In the vast majority of these situations, the clustering involves news-related jumps. This suggests the presence of intraday jumps in the intervals immediately preceding and following news-related jumps, which are classed as non-news-related jumps in this study. A less rigid separation of news and non-news-related jumps may then define many more intraday jumps as news related and may prove a fruitful examination of the price discovery process following macroeconomic news announcements. Fig. 3 plots the time variation of rolling measure of jump intensity. The finding that non-news-related jumps congregate around news-related jumps may require a more sophisticated measure of jump intensity to allow interaction between the two types of jumps. Another interesting question to address is whether jumps occur simultaneously across markets, or in other words, whether markets co-jump. This is analysed here by first considering the markets in pairs and then combining all three markets together. There are 87 intraday co-jumps between the E-Mini and T-Bond futures markets, of which 67 coincide precisely with US macroeconomic announcements. The figures for the E-Mini and EUR-USD are 54 co-jumps, with 39 of these news related, and for the T-Bond and

EUR-USD, 104 co-jumps are related to news in 82 cases. When considering all three markets, there are 36 co-jumps with 29 of these in response to US macroeconomic news announcements. There is clear and interesting evidence, therefore, that intraday jumps sometimes cluster around the timing of news announcements as part of the price discovery process, and that macroeconomic news surprises generate the overwhelming majority of co-jumps across asset classes. Both empirical facts serve to emphasise the importance of the arrival of public information in the form of macroeconomic news announcements in the context of price jumps.

7. Conclusions This paper investigates whether statistically significant jumps are related to macroeconomic news announcements. The findings show that jumps are prevalent in S&P 500 E-Mini, US 10-Year TBond and EUR-USD futures markets, occurring far more frequently than parametric models currently allow, are large and contribute substantial proportions of daily price variation. One of the paper’s contributions investigates the links between jumps and news announcements at the intraday level, representing an innovative application of a recently proposed intraday jump detection method. The results show that, even when classifying news-related jumps very strictly as occurring in the 5 min following announcements, approximately one third of jumps are related to macroeconomic news. News announcements are found to cause statistically

2526

K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527

Table 9 Excess number of ticks and intraday jumps. S&P 500 E-Mini

US 10-Yr T-Bond

EUR-USD

Panel (a) All data

aV 2.51

JDj 1,304.24⁄⁄

aV 1.50

JDj 382.58⁄⁄

aV 2.22

JDj 566.95⁄⁄

Panel (b) All data NJDj

NNJDj

NJDj

NNJDj

NJDj

NNJDj

818.59⁄⁄

1,519.23⁄⁄

500.51⁄⁄

284.25⁄⁄

856.45⁄⁄

448.29⁄⁄

Panel (c) Jumps only LAG NJDj

NNJDj

NJDj

NNJDj

NJDj

NNJDj

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1,516.72⁄⁄ 936.86⁄⁄ 443.76⁄⁄ 356.08⁄⁄ 335.38⁄⁄ 289.72⁄⁄ 247.34⁄⁄ 230.45⁄⁄ 180.23⁄⁄ 177.90⁄⁄ 172.09⁄⁄ 202.31⁄⁄ 126.77⁄⁄ 116.77⁄⁄ 142.98⁄⁄ 82.06⁄⁄ 81.55⁄⁄ 100.57⁄⁄ 96.78⁄⁄ 64.12⁄ 92.08⁄⁄ 62.20⁄ 61.05+ 47.26 42.73

499.01⁄⁄ 261.49⁄⁄ 197.22⁄⁄ 148.25⁄⁄ 121.99⁄⁄ 102.45⁄⁄ 95.34⁄⁄ 87.42⁄⁄ 74.90⁄⁄ 73.77⁄⁄ 51.89⁄⁄ 66.61⁄⁄ 57.93⁄⁄ 61.59⁄⁄ 50.43⁄⁄ 51.24⁄⁄ 42.46⁄⁄ 41.27⁄⁄ 24.78+ 28.76⁄⁄ 22.62⁄⁄ 30.00⁄⁄ 26.09⁄⁄ 29.89⁄⁄ 17.87⁄

282.76⁄⁄ 297.47⁄⁄ 155.63⁄⁄ 117.70⁄⁄ 102.62⁄⁄ 88.06⁄⁄ 66.78⁄⁄ 55.53⁄⁄ 40.53⁄⁄ 37.80⁄⁄ 49.46⁄⁄ 28.64⁄⁄ 24.59⁄⁄ 23.43⁄⁄ 25.03⁄⁄ 15.86⁄ 13.51⁄ 12.82 9.86 18.81+ 15.96+ 12.70+ 16.12+ 4.52 13.76+

854.23⁄⁄ 585.98⁄⁄ 412.24⁄⁄ 372.91⁄⁄ 327.69⁄⁄ 271.46⁄⁄ 271.15⁄⁄ 213.83⁄⁄ 198.63⁄⁄ 202.07⁄⁄ 137.74⁄⁄ 162.51⁄⁄ 147.48⁄⁄ 128.89⁄⁄ 125.36⁄⁄ 109.78⁄⁄ 114.84⁄⁄ 114.47⁄⁄ 116.19⁄⁄ 126.54⁄⁄ 134.36⁄⁄ 101.79⁄⁄ 91.67⁄⁄ 71.44⁄⁄ 88.50⁄⁄

446.06⁄⁄ 327.88⁄⁄ 213.85⁄⁄ 150.44⁄⁄ 109.97⁄⁄ 103.46⁄⁄ 88.92⁄⁄ 76.98⁄⁄ 53.04⁄⁄ 45.21⁄⁄ 43.93⁄⁄ 35.75⁄⁄ 35.23⁄⁄ 46.09⁄⁄ 43.76⁄⁄ 39.65⁄⁄ 38.07⁄⁄ 40.03⁄⁄ 32.83⁄ 26.20⁄ 31.75⁄ 33.22⁄⁄ 21.54+ 22.65⁄ 15.95

816.07⁄⁄ 320.50⁄⁄ 115.43⁄ 101.10⁄ 67.14 66.36 12.98 9.58 1.85 41.93 34.75 21.43 25.23 33.28 20.63 57.34 25.86 19.93 96.55 57.77 57.24 74.80 1.26 3.27 16.56

Notes: This table reports the coefficient estimates of Eqs. (22)–(24) (using number of ticks rather than volume) that investigate the effect of intraday jumps on number of ticks in excess of the sample intraday average number of ticks. JD, NJD and NNJD refer to jump, news-related jump and non-news-related jump dummy variables respectively. Panel a shows the estimates from Eq. (22) that uses all data in the sample; Panel b reports the estimates of Eq. (23) again using the full sample and Panel c displays the results from estimations of Eq. (24) that investigates instantaneous and persistent effect of jumps on number of ticks and uses only the sub-sample of intraday jumps and the 24 intervals that follow them. ⁄⁄, ⁄ and + denote statistical significance at the 1%, 5% and 10% levels respectively.

and economically significant increases in average absolute jump size, and these reactions are more dramatic for E-Mini futures and occur in response to a wider set of indicators for the T-Bond contracts. The information surprises delivered by the announcements generate much larger coefficients than previous studies of high frequency news announcement effects and explain staggering proportions of the intraday jumps for the T-Bond and EUR-USD futures markets. The results are not so explicit for the E-mini, suggesting the need to examine more complex price discovery mechanisms in equity markets. In light of the important instantaneous effects of macroeconomic news in causing jumps, the paper’s other contributions are to investigate the relative economic importance of these news-related jumps versus their non-news related counterparts. This is investigated by separating jumps into news related and non-news related sub-samples and examining their distributional differences and their influence on return predictability and volatility persistence at the high frequency and daily levels. The findings show that news-related jumps are larger, on average, in absolute terms than non-news-related jumps. Indeed, the largest intraday jumps in the sample are invariably associated with macroeconomic news announcements. There is little evidence that intraday jumps help to predict returns, but substantive evidence that they precede volatility persistence and that the presence of news-related jumps generates stronger volatility than non-news-related jumps. A more sophisticated and robust method of assessing the relative impor-

tance of the two types of jumps applies the rolling measures of jump risks designed by Wright and Zhou (2009) and Tauchen and Zhou (2011), but adapted to separate jumps risks emanating from news related and non-news-related jumps, and tests whether these jump risk measures hold risk premia in future returns. For the E-Mini and T-Bond markets, there is clear evidence that news-related jump risks are more important than non-news jump risk in predicting future returns. News-related jump intensity and jump mean are significant predictors for both markets, and newsrelated jump volatility is also important for the E-Mini. Another contribution of the paper investigates the effect of jumps on microstructure variables as proxies for the arrival of other public information or private information shocks in an attempt to suggest possible other explanations for the presence of non-news-related jumps. There is very clear evidence that both trade volume and the number of ticks are significantly higher than normal following intraday jumps indicating that non-news-related jumps may be associated with other public news announcements or the arrival of private information. For the T-Bond and EURUSD markets, both variables are higher following news-related jumps, whereas they are higher following non-news-related jumps for the E-Mini, an interesting result for equities that could prompt further study, possibly incorporating order flow and the methods of Evans and Lyons (2008), Love and Payne (2008) and King et al. (2010). Finally, the paper’s last contribution addresses briefly the additional features of jump clustering and co-jumps. Intraday

K.P. Evans / Journal of Banking & Finance 35 (2011) 2511–2527

jumps tend to cluster around news-related jumps in the T-Bond and EUR-USD markets, suggesting that there are likely more interesting dynamics surrounding the price discovery process than are captured in this paper. There is also strong evidence that newsrelated jumps tend to be co-jumps across asset classes, a result that further emphasises the importance of US macroeconomic announcements and the jumps that they cause for financial markets and generates interesting avenues for further research.

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