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Table 4 Fully specified ACI models BHP NAB NCP TLS WOW BHP NAB NCP TLS WOW Constants and backward recurrence parameters ω 1 −1.059*** −0.336*** −0.294*** −1.085*** −1.015*** p 1 0.844*** 0.797*** 0.820*** 0.832*** 0.737*** ω 2 −0.301*** −0.285*** −0.157 −1.207*** −0.672*** p 2 0.845*** 0.778*** 0.798*** 0.836*** 0.731*** ω 3 −0.793*** −0.782*** −0.490*** −0.722*** −0.859*** p 3 0.834*** 0.905*** 0.878*** 0.825*** 0.810*** ω 4 −0.497*** −1.348*** −1.081*** −0.420* −1.637*** p 4 0.818*** 0.862*** 0.834*** 0.700*** 0.838*** ω 5 −1.352*** −1.397*** −1.576*** −1.025*** −1.674*** p 5 0.742*** 0.749*** 0.873*** 0.653*** 0.789*** ω 6 −1.704*** −1.442*** −1.410*** −1.363*** −1.980*** p 6 0.762*** 0.741*** 0.859*** 0.671*** 0.780*** Innovation parameters α 1 1 0.115*** 0.114*** 0.076*** 0.235*** 0.176*** α 1 2 0.003 0.029** 0.018** 0.046 0.081*** α 2 1 −0.010 0.013 0.004 0.179*** 0.055*** α 2 2 0.120*** 0.115*** 0.085*** 0.244*** 0.177*** α 3 1 0.052*** 0.038*** 0.012 0.160 0.082*** α 3 2 −0.010 −0.009 0.013* −0.007 0.054** α 4 1 −0.033** −0.009 −0.007 0.076 0.059* α 4 2 0.018 0.042*** 0.003*** 0.263* 0.058* α 5 1 0.031** 0.037** 0.064*** 0.074** 0.016 α 5 2 0.046*** 0.049*** 0.022* −0.030 0.145*** α 6 1 0.044*** 0.027* 0.042*** 0.049** 0.199*** α 6 2 0.010 0.042*** 0.041*** 0.030* 0.138*** α 1 3 0.042*** −0.002 −0.015* 0.085 0.074*** α 1 4 0.028** 0.053*** 0.005 −0.079* 0.071*** α 2 3 0.026* −0.007 0.018* −0.159** 0.070*** α 2 4 0.064*** 0.048*** 0.013 0.046 0.021 α 3 3 0.097*** 0.073*** 0.028*** 0.438*** 0.122*** α 3 4 0.012 0.008 0.038*** −0.393** 0.084*** α 4 3 0.076*** −0.004 0.026*** 0.010 0.175*** α 4 4 0.116*** 0.083*** 0.033*** −0.042 0.215*** α 5 3 0.051*** 0.034** 0.022* 0.063 0.132*** α 5 4 0.031* 0.056** 0.029* 0.017 0.058 α 6 3 0.056*** 0.032** 0.008 −0.038 0.029 α 6 4 0.022 0.010 0.010 −0.014 0.201*** α 1 5 −0.008 −0.017 −0.010 −0.019 0.118*** α 1 6 0.002 −0.054** 0.012 0.127*** 0.083*** α 2 5 0.023 0.013 −0.004 0.185*** 0.133*** α 2 6 0.017 −0.024 0.012 −0.056*** 0.013 α 3 5 −0.007 0.017 −0.063*** 0.186* 0.022 α 3 6 0.000 0.014 0.024 −0.451** −0.021 α 4 5 0.058* 0.015 0.010 0.129 0.104* α 4 6 0.001 −0.002 0.012 −0.088 0.058 α 5 5 0.002 0.097*** 0.025 0.068** 0.122*** α 5 6 0.005 0.030 −0.006 0.029 −0.185*** α 6 5 0.094*** 0.002 −0.001 0.030 −0.141** α 6 6 0.067*** 0.119*** 0.017 0.019 0.068* 148 A. D. Hall, N. Hautsch
Table 4 (cont<strong>in</strong>ued) BHP NAB NCP TLS WOW BHP NAB NCP TLS WOW Persistence parameters β 11 0.980*** 0.961*** 0.993*** 0.922*** 0.994*** β 44 0.950*** 0.984*** 0.998*** −0.251*** 0.995*** β 22 0.969*** 0.968*** 0.980*** 0.820*** 0.993*** β 55 0.980*** 0.980*** 0.993*** 0.948*** 0.994*** β 33 0.955*** 0.991*** 0.995*** 0.481*** 0.994*** β 66 0.979*** 0.982*** 0.994*** −0.973*** 0.991*** Seasonality parameters 12 ν11:00 −0.673*** −0.646*** −1.216*** 8.226*** −1.355*** 34 ν11:00 0.160 −0.334 −0.831*** 3.955** −1.183*** 12 ν12:00 0.138 0.266 0.961*** −14.730*** 1.228*** 34 ν12:00 −1.159** 0.183 0.492 −7.780** 0.672 12 ν13:00 −0.981*** −1.341*** −0.937*** 2.870* −0.570* 34 ν13:00 −0.438*** −1.551*** −0.957*** 3.113*** 0.179 12 ν14:00 3.161*** 3.553*** 2.620*** 10.182*** 2.222*** 34 ν14:00 2.956 3.548*** 2.661*** 5.563** 1.526*** 12 ν15:00 −0.286 −0.521* −0.721*** −6.032** −0.034 34 ν15:00 0.025*** −1.032** −0.269 −4.674 −0.422 12 ν16:00 0.468 0.516 1.509*** 6.824** 4.069*** 34 ν16:00 −0.237 0.652 −0.986** 1.557 0.896 56 ν11:00 −0.572* −1.387*** −1.309*** −0.264 −1.894*** 56 ν14:00 3.755*** 2.814*** 2.770*** 4.863*** 1.495*** 56 ν12:00 0.266 1.555*** 1.163** 0.359 1.817*** 56 ν15:00 −1.313*** −1.044*** −0.827* −2.817*** 0.184 56 ν13:00 −1.531*** −1.605*** −1.135*** −2.265*** −0.349 56 ν16:00 1.071* 0.799* 0.273 3.798*** 0.516 Explanatory variables AD 1 −7.287*** −2.318*** −3.390*** −18.057*** −2.130*** BD 1 3.974*** 1.903*** 2.575*** 8.315*** 1.036** AD 2 4.400*** 1.340*** 2.405*** 4.035*** 0.828** BD 2 −7.144*** −1.247*** −3.381*** −16.624*** −0.586 AD 3 −7.708*** −2.072*** −3.448*** −28.527*** −3.019*** BD 3 0.148 −0.185 −0.390 4.385*** −1.137** AD 4 0.709* −0.302 0.596 2.784** −2.468*** BD 4 −8.672*** −2.400*** −4.005*** −26.667*** −2.832*** AD 5 4.822*** 6.074*** 7.804*** 2.902*** 4.521*** BD 5 1.516*** −0.048 −1.026 1.510** 0.904 AD 6 0.584 0.177 0.270 −1.085 0.937 BD 6 6.410*** 6.437*** 5.877*** 4.674*** 5.545*** AV 1 3.108*** 1.579*** 1.129 10.325*** 1.952 DAV 1 −0.649** −0.411** −0.974*** −2.109*** −1.097*** AV 2 0.477 −0.006 0.044 1.283 −4.123*** DAV 2 −0.698** −0.161 −0.899*** −1.309*** −1.558*** AV 3 8.480*** 5.378*** 8.408*** 23.421*** 3.956*** DAV 3 0.172 −0.456* −0.376 −0.662 −0.963*** AV 4 11.203*** −10.071*** −12.398*** −5.250*** −5.426* DAV 4 −0.910** −0.232 −0.868** −0.938*** 0.841* AV 5 −0.602 0.635 3.546*** 5.198*** −0.008 DAV 5 1.341*** 1.811*** 1.585*** −0.390 0.993* Order aggressiveness and order book dynamics 149
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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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Page 110 and 111: 104 To obtain the estimates in the
Page 112 and 113: 106 Hence, using the liquidity stat
Page 114 and 115: 108 In the main text we discuss the
Page 117 and 118: Pierre Giot . Joachim Grammig How l
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Page 137: How large is liquidity risk in an a
Page 140 and 141: 134 A. D. Hall, N. Hautsch In this
Page 142 and 143: 136 includes order book variables,
Page 144 and 145: 138 An important determinant of liq
Page 146 and 147: 140 The innovation term ei is compu
Page 148 and 149: Table 1 Order book characteristics
Page 150 and 151: 144 data covering the normal tradin
Page 152 and 153: 146 Table 3 Descriptive statistics
Page 156 and 157: Table 4 (continued) BHP NAB NCP TLS
Page 158 and 159: Table 5 ACI models without dynamics
Page 162 and 163: Table 6 ACI models without covariat
Page 166 and 167: 160 specification which includes or
Page 168 and 169: 162 5.2.5 The impact of the bid-ask
Page 170 and 171: 164 contrast to those of Pascual an
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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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254 between price changes and durat
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256 simply aggregated. Even after a
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258 is to make use of the fact that
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260 together with the restrictions
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Table 3 Estimated probabilities of
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264 A. S. Tay, C. Ting More interes
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Table 4 Estimated probabilities of
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268 Acknowledgements Tay gratefully
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270 This paper explores the effect
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272 contrast, price responses to po
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274 Fig. 1 Continuous line is the T
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276 the β’s can form a convex sh
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278 fixing some intervals around th
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280 . That is for a given absolute
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282 30 25 20 15 10 5 CC UNEMW ISM U
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Appendix Table A1 Consumer confiden
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Table A3 Non-farm payrolls • ✓
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Table A5 Weekly unemployment claims
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290 Table A8 Retail sales • ✓ 1
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292 Table A12 GDP, BI, TB and PI Re
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294 V. Voev with the problem of how
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296 V. Voev 2.1 A sample covariance
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298 V. Voev Using the equicorrelate
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300 V. Voev where �kl(t) is the (
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302 V. Voev where hkl,k ′ l ′ i
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304 V. Voev is 1.9%. From the daily
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306 V. Voev ACF ACF ACF 0.5 0.5 0.5
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308 V. Voev autocorrelated. Indeed,
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310 V. Voev 5 Conclusion Table 2 Ro
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312 V. Voev Engle R (1982) Autoregr
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