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Table A5 Weekly unemployment claims • ✓ 10 min 0.08 0.07 0.09 0.09 0.07 0.05 0.08 0.12 0.07 0.09 0.08 0.06 0.08 0.06 0.09 0.05 0.06 0.14 0.07 0.07 0.09 20 min 0.07 0.08 0.06 0.07 0.07 0.07 0.08 0.07 0.05 0.07 0.07 0.07 0.05 0.08 0.08 0.07 0.09 0.07 0.07 0.06 0.03 30 min 0.05 0.07 0.04 0.05 0.05 0.07 0.07 0.04 0.03 0.05 0.06 0.05 0.03 0.07 0.06 0.06 0.10 0.02 0.05 0.05 0.01 40 min 0.03 0.05 0.02 0.03 0.03 0.05 0.05 0.02 0.02 0.03 0.04 0.04 0.02 0.06 0.04 0.04 0.08 0.00 0.04 0.03 −0.01 50 min 0.02 0.03 0.00 0.01 0.02 0.03 0.03 0.00 0.01 0.01 0.02 0.01 0.01 0.04 0.02 0.02 0.05 −0.01 0.02 0.01 −0.01 60 min 0.00 0.01 −0.01 0.00 0.00 0.01 0.01 −0.01 −0.01 0.00 0.00 −0.01 0.00 0.02 0.00 0.00 0.02 −0.02 0.00 −0.01 −0.01 70 min −0.01 −0.01 −0.02 −0.01 −0.01 0.00 −0.01 −0.02 −0.01 −0.01 −0.01 −0.02 −0.01 0.00 −0.01 −0.01 −0.01 −0.01 −0.01 −0.03 −0.01 80 min −0.02 −0.02 −0.02 −0.02 −0.02 −0.01 −0.02 −0.02 −0.02 −0.01 −0.02 −0.03 −0.02 −0.01 −0.02 −0.02 −0.03 −0.01 −0.02 −0.04 −0.02 90 min −0.02 −0.02 −0.02 −0.02 −0.02 −0.02 −0.02 −0.02 −0.02 0.00 −0.03 −0.03 −0.04 −0.01 −0.02 −0.01 −0.04 0.00 −0.03 −0.05 −0.04 100 min −0.02 −0.01 −0.02 −0.02 −0.02 −0.01 −0.02 −0.02 −0.02 0.00 −0.03 −0.02 −0.05 −0.01 −0.02 0.00 −0.04 0.01 −0.03 −0.05 −0.06 110 min −0.01 0.00 −0.02 −0.01 −0.02 0.00 −0.01 −0.02 −0.02 0.01 −0.02 −0.01 −0.06 0.00 −0.02 0.02 −0.03 0.02 −0.02 −0.05 −0.08 120 min −0.01 0.01 −0.02 −0.01 −0.01 0.01 0.01 −0.02 −0.02 0.02 −0.01 0.00 −0.07 0.02 −0.01 0.04 −0.02 0.02 −0.02 −0.05 −0.09 See legend Table A1 288 D. Veredas
Macroeconomic surprises and short-term behaviour in bond futures 289 Table A6 Producer price index • ✓ 10 min ▪ □ ▪ ▪ □ □ ▪ ▪ ▪ ▪ 20 min ▪ ▪ ▪ ▪ ▪ □ □ □ □ 30 min ▪ □ ▪ □ □ □ 40 min □ 50 min 60 min 70 min □ □ ▪ ▪ 80 min ▪ ▪ ▪ ▪ ▪ 90 min ▪ ▪ □ ▪ 100 min ▪ □ 110 min ▪ □ 120 min □ See legend Table A1. Black (white) squares stand for significant effect at 5% (10%) Table A7 Consumer price index • ✓ 10 min ▪ ▪ ▪ ▪ ▪ □ ▪ ▪ 20 min ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ 30 min ▪ ▪ □ □ ▪ ▪ ▪ □ 40 min ▪ ▪ ▪ ▪ ▪ 50 min ▪ ▪ □ ▪ 60 min ▪ ▪ 70 min ▪ ▪ □ 80 min ▪ ▪ ▪ ▪ ▪ 90 min ▪ ▪ ▪ ▪ ▪ 100 min ▪ ▪ ▪ ▪ 110 min □ ▪ 120 min See legend in Tables A1 and A6
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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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220 C.5. Triple bottom (TB) TB is c
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222 References W. B. Omrane, H. V.
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Juan M. Rodríguez-Poo · David Ver
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Semiparametric estimation for finan
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Page 260 and 261: 254 between price changes and durat
Page 262 and 263: 256 simply aggregated. Even after a
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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 274 and 275: 268 Acknowledgements Tay gratefully
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Page 278 and 279: 272 contrast, price responses to po
Page 280 and 281: 274 Fig. 1 Continuous line is the T
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Page 284 and 285: 278 fixing some intervals around th
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Page 290 and 291: Appendix Table A1 Consumer confiden
Page 292 and 293: Table A3 Non-farm payrolls • ✓
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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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