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268 Acknowledgements Tay gratefully acknowledges research support from the Singapore Management University’s Wharton-SMU Research Centre. We thank participants at the 2005 North American Meeting of the Econometric Society for helpful questions and comments. All errors remain our own. Zhang Feng provided able research assistance. References A. S. Tay, C. Ting Cai Z (2002) Regression quantiles for time series. Econom Theory 18:169–192 Chen SX (2000) Probability density function estimation using gamma kernels. Ann Inst Stat Math 52:471–480 Diamond DW, Verrecchia RE (1987) Constraints on short-selling and asset price adjustment to private information. J Financ Econ 18:277–311 Dufour A, Engle RF (2000) Time and the price impact of a trade. J Finance 55(6):2467–2498 Easley D, O’Hara M (1992) Time and the process of security price adjustment. J Finance 47 (2):577–605 Easley D, Kiefer NM, O’Hara M (1997) The information content of the trading process. J Empir Finance 4:159–186 Engle RF (2000) The econometrics of ultra-high frequency data. Econometrica 68(1):1–22 Engle RF, Gonzalez-Rivera G (1991) Semiparametric ARCH models. J Bus Econ Stat 9:345–360 Engle RF, Russell JR (1998) Autoregressive conditional duration: a new model for irregularly spaced transaction data. Econometrica 66(5):1127–1162 Fan J, Gijbels I (1996) Local polynomial modelling and its application, monographs on statistics and applied probability 66, Chapman & Hall/CRC Fan J, Yao Q (2003), Nonlinear time series: nonparametric and parametric methods, Springer Gallant AR, Hsieh DA, Tauchen G (1991) On fitting a recalcitrant series: the Pound/Dollar exchange rate, 1974–83. In: Barnett WA, Powell J, Tauchen G (eds) Nonparametric and semiparametric methods in econometrics and statistics: proceedings of the fifth international symposium in economic theory and econometrics, Cambridge University Press Gallant AR, Rossi PE, Tauchen G (1992) Stock prices and volume. Rev Financ Stud 5(2):199– 242 Grammig J, Wellner M (2002) Modeling the interdependence of volatility and inter-transaction duration processes. J Econom 106:369–400 Hall P, Presnell B, Intentionally Biased Bootstrap Methods. J R Stat Soc B 61:143–158 Hall P, Wolff RCL, Yao Q (1999) Methods for estimating a conditional distribution function. J Am Stat Assoc 94(445)154–163 Jones CM, Kaul G, Lipson ML (1994) Transactions, volume, and volatility. Rev Financ Stud 7 (4):631–651 Lee CMC, Ready MJ (1991) Inferring trade direction from intraday data. J Finance 46(2)733–746 Renault E, Werker BJM (2004) Stochastic Volatility Models with Transaction Time Risk, manuscript, Universite de Montreal and Tilburg University Russell JR, Engle RF (2004) A Discrete-state continuous-time model of financial transactions prices: the ACM-ACD Model, forthcoming in J Bus Econo Stat Tay AS, Ting C, Tse Y, Warachka M (2004) Transaction-data analysis of marked durations and their implication for market microstructure, Singapore Management University SESS Working Paper Vergote O (2005) How to match trades and quotes for NYSE stocks? Center for economic studies discussion paper, K.U.Leuven, DPS 05.10 Wand MP, Jones MC (1995) Kernel smoothing, Monographs on statistics and applied probability 60, Chapman & Hall/CRC
David Veredas Macroeconomic surprises and short-term behaviour in bond futures Abstract This paper analyses the effect of macroeconomic news on the price of the ten year USATreasure bond future. We consider 15 fundamentals and we analyse the effect of their forecasting errors conditional upon their sign and the momentum of the business cycle. To obtain a smooth effect of the news arrival we estimate a Polynomial Distributed Lag model. Using 10 minutes sampled data during 9 years, we conclude that 1) releases affect the bond future for only few hours, 2) their effect depends on the sign of the forecast error, 3) their effect also depends on the business cycle and 4) the timeliness of the releases is significant. Keywords US bonds . PDL model . Business cycle . Macroeconomic announcements JEL Classification C22 . G14 1 Introduction Researchers and security price analysts are in fundamental disagreement about the causes of price movements. Both groups, economic theorists and econometrics practitioners, have produced a plethora of articles explaining the reasons for volatility, trends, clustering, long memory and other price movements. But one phenomenon in which they express common agreement is that when prices are sampled at a sufficiently high frequency (i.e. at least intradaily), short term movements can partly be attributed to news arrivals. In particular, to macroeconomic announcements. D. Veredas (*) ECARES-Université Libre de Bruxelles and CORE, 50, Av Roosevelt CP114, B1050 Brussels, Belgium E-mail: dveredas@ulb.ac.be
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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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Page 260 and 261: 254 between price changes and durat
Page 262 and 263: 256 simply aggregated. Even after a
Page 264 and 265: 258 is to make use of the fact that
Page 266 and 267: 260 together with the restrictions
Page 268 and 269: Table 3 Estimated probabilities of
Page 270 and 271: 264 A. S. Tay, C. Ting More interes
Page 272 and 273: Table 4 Estimated probabilities of
Page 276 and 277: 270 This paper explores the effect
Page 278 and 279: 272 contrast, price responses to po
Page 280 and 281: 274 Fig. 1 Continuous line is the T
Page 282 and 283: 276 the β’s can form a convex sh
Page 284 and 285: 278 fixing some intervals around th
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Page 288 and 289: 282 30 25 20 15 10 5 CC UNEMW ISM U
Page 290 and 291: Appendix Table A1 Consumer confiden
Page 292 and 293: Table A3 Non-farm payrolls • ✓
Page 294 and 295: Table A5 Weekly unemployment claims
Page 296 and 297: 290 Table A8 Retail sales • ✓ 1
Page 298 and 299: 292 Table A12 GDP, BI, TB and PI Re
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
Page 306 and 307: 300 V. Voev where �kl(t) is the (
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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