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270 This paper explores the effect of macroeconomic announcements on the returns of bond futures. The literature in the field tracks back to Berkman (1978) and a series of articles published until mid 1980s (e.g. Grossman 1981; Roley 1983) that focus on money supply announcements. Goodhart et al. (1993) analyse the effect of the announcements of two macroeconomic numbers on the mean and variance, McQueen and Roley (1993) study the effect of eight fundamentals on the mean during eleven years, on a daily basis, of S&P500 price movements. Instead of using the news itself, they use the forecasting error, i.e. the difference between the released and the expected number. Moreover, they control for the momentum of the business cycle (hereafter we will refer indistinctively to the economic cycle as the business cycle or economic cycle). Flemming and Remolona (1999) study the effect of 25 macroeconomic numbers on the US bond market (see Table 1 of this article for a summary of the research done in macroeconomic announcements prior to 1997). They employ standard regression using five minutes returns in absolute value, the number of transactions per hour as market measures and the macroeconomic forecasting errors. DeGennaro and Shrieves (1997) look at the effect of con- Table 1 Information on macro numbers Name Acronymic Rel. hour Details D. Veredas using a GARCH-M and three months of tick-by-tick US-pound exchange rates. temporaneous and expost announcements of 27 USA and Japan fundamentals on the volatility of US-Yen exchange rates using GARCH models. Li and Engle (1998) work on the daily volatility of the 30 years note future (T-bond), via GARCH and GARCH-M models, and how it is affected by the announcements of just two fundamentals. Andersen and Bollerslev (1998) also study the effect of announcements on volatility but using a slightly different approach to Engle and Li’s. They analyse the Deutsche Mark-US exchange rate in a two-step procedure and using weighted least squares. First, they estimate a standard GARCH model and, second, they standardise the absolute residuals centered with the estimated volatility. Business inventories BI 08:30 NY %chg m/m Consumer confidence CC 10:00 NY Index SA Consumer price index CPI 08:30 NY %chg m/m SA Durable goods orders DG 08:30 NY %chg m/m SA Gross domestic product GDP 08:30 NY %chg q/q SAAR Housing starts HS 08:30 NY Millions of units SA Industrial production IP 09:15 NY %chg m/m SA Industrial Production IP 09:15 NY %chg m/m SA Institute for Supply Management ISM 10:00 NY Index Non-Farms Payrolls NFP 08:30 NY K persons chg SA Personal Income PI 08:30 NY %chg m/m SA Producer Price Index PPI 08:30 NY %chg m/m SA Retail Sales RS 08:30 NY %chg m/m SA Trade Balance TB 08:30 NY $billions Unemployment claims UNEMW 08:30 NY K persons as reported Unemployment rate UNEM 08:30 NY Rate as reported NY NY time, Rel. released, %chg percent change, SA seasonally adjusted, SAAR seasonally adjusted annual rate, m/m month to month, q/q quarter to quarter
Macroeconomic surprises and short-term behaviour in bond futures 271 They assume that this variable captures the intradaily seasonality and the news effect that standard GARCH models are not able to capture. Finally, two papers focus on the mean of the returns of the Treasury bond future and several exchange rates. Andersen et al. (2003) analyse, in a seven year period, the forecasting errors of 40 fundamentals using a very similar approach to Andersen and Bollerslev (1998) but for the mean. Hautsch and Hess (2002) use the revision unemployment re- port numbers that include important variables such as the unemployment rate and non-farms payrolls. In this paper 1) the financial asset on which we base our analysis is the US Treasury 10-year bond future, 2) we consider 15 fundamentals representing different sectors of the economy (inflation, real economy, supply and demand confidence indexes and export–import measures), 3) we are interested in the effect of news announcements on the mean rather than on volatility, 4) we consider the forecasting errors rather than the released numbers, 5) we differentiate between positive and negative forecasting errors, 6) we assume that the effect of the fundamentals on the bond market differs depending on the business cycle (top, bottom, expansion and contraction), and 7) we use an econometric model that estimates contemporaneous and ex-post effects of the macroeconomic news smoothly, that is, the parameters that measure the news effect vary smoothly through time. One of the main features of the paper is the interaction between 5 and 6. That is, we not only permit good and bad news to affect differently the bond future, but we also investigate whether these effects are robust within the economic cycle. If there are variations, we may conclude that market’s behaviour depends on the mood of the economy or how the market perceives the state of the economy. This is related to the work in behavioural economics and, in particular, behavioural finance. Since the introduction of prospect theory (Kanehman and Tversky 1979), it is well known that individuals suffer from an asymmetry between the way they make decisions involving gains (good news) and decisions involving losses (bad news). This leads to a failure of invariance, i.e. inconsistent choices (or reactions to good and bad news) when the same problem (a good or a bad news item) appears in different frames (top, bottom, expansion and contraction of the business cycle). An alternative means of explaining why the market responds differently to good and bad news in different phases of the business cycle is by means of uncertainty (Veronesi 1999): The market is uncertain about the state of the economy. For example, after a period of economic boom, good news will have no effect as traders are already aware of the strength of the economy. If, by contrast, bad news start to arrive, the market reacts because it is not no longer confident about the phase of the business cycle. This introduces more uncertainty and causes an asymmetry in the response to good and bad news. On this basis, Conrad et al. (2002) test this asymmetric reaction to the news sign and the state of the stock market (the equivalent to the business cycle in our paper). In a firm-specific study, and using earnings announcements, they conclude that price responses to negative earnings surprises increase as the difference between the market value at the moment of the announcement and the average market value during the last 12 months, increases. In other words, the effect of a negative earnings surprise is higher if the market feels itself to be in expansion with respect to the previous 12 months, than in contraction. Or, put differently, markets respond more strongly to bad news in good times. By
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High Frequency Financial Econometri
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Prof. Luc Bauwens CORE Voie du Roma
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vi Contents Intraday stock prices,
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2 L. Bauwens et al. component nicel
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4 L. Bauwens et al. but provides al
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Luc Bauwens . Dagfinn Rime . Genaro
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Exchange rate volatility and the mi
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32 K. Bien et al. Although economet
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34 K. Bien et al. 2.1 Copula functi
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36 K. Bien et al. and x k t ≡ (xk
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38 K. Bien et al. Fig. 3 Multivaria
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40 K. Bien et al. Bivariate model s
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42 K. Bien et al. deviation 0.0099,
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44 K. Bien et al. - Set ˆzt = Âxt
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46 K. Bien et al. % Frequency −0.
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48 K. Bien et al. References Amilon
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50 1 Introduction A. Escribano and
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52 A. Escribano and R. Pascual Jang
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54 A. Escribano and R. Pascual the
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56 The generating processes of mark
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58 with AtðLÞ ¼ 0 B @ 1 ð ÞAab
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60 A. Escribano and R. Pascual cros
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62 Hasbrouck (1991). The system is
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64 unitary seller-initiated shock (
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66 6.1 Estimation of the baseline m
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Table 2 The base-line VEC model for
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70 Table 3 Simulation of the base-l
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72 revert towards narrow levels. As
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Table 5 Impulse-response functions
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76 We also show that NYSE buyer-ini
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78 As 0 < a m < 1; L ð Þ ¼ 1 a m
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80 NYSE 2000 Stocks AOL America Onl
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82 A. Escribano and R. Pascual Madh
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84 1 Introduction S. Frey, J. Gramm
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86 develops the empirical methodolo
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Table 1 Sample descriptives Company
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90 comparability across stocks, we
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92 subtracting the deviations from
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94 market order distribution that c
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96 Table 2 First stage GMM results
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Table 4 First stage GMM results bas
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100 S. Frey, J. Grammig quotes on e
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102 S. Frey, J. Grammig approximate
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104 To obtain the estimates in the
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106 Hence, using the liquidity stat
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108 In the main text we discuss the
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Pierre Giot . Joachim Grammig How l
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How large is liquidity risk in an a
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134 A. D. Hall, N. Hautsch In this
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136 includes order book variables,
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138 An important determinant of liq
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140 The innovation term ei is compu
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Table 1 Order book characteristics
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144 data covering the normal tradin
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146 Table 3 Descriptive statistics
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Table 4 Fully specified ACI models
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Table 4 (continued) BHP NAB NCP TLS
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Table 5 ACI models without dynamics
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Table 6 ACI models without covariat
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160 specification which includes or
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162 5.2.5 The impact of the bid-ask
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164 contrast to those of Pascual an
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Roman Liesenfeld . Ingmar Nolte . W
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Modelling financial transaction pri
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200 W. B. Omrane, H. V. Oppens anal
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202 W. B. Omrane, H. V. Oppens of s
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204 The extrema detection method ba
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206 price 0.8565 0.8575 0.8585 0.85
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208 We distinguish three possible c
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210 originates. The most active tra
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212 Table 2 Predictability of the c
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214 6 Conclusion Using 5-min euro/d
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216 where σk is the standard devia
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218 If we meet a particular case su
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Page 274 and 275: 268 Acknowledgements Tay gratefully
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Page 280 and 281: 274 Fig. 1 Continuous line is the T
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Page 300 and 301: 294 V. Voev with the problem of how
Page 302 and 303: 296 V. Voev 2.1 A sample covariance
Page 304 and 305: 298 V. Voev Using the equicorrelate
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Page 308 and 309: 302 V. Voev where hkl,k ′ l ′ i
Page 310 and 311: 304 V. Voev is 1.9%. From the daily
Page 312 and 313: 306 V. Voev ACF ACF ACF 0.5 0.5 0.5
Page 314 and 315: 308 V. Voev autocorrelated. Indeed,
Page 316 and 317: 310 V. Voev 5 Conclusion Table 2 Ro
Page 318: 312 V. Voev Engle R (1982) Autoregr
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