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Table 5 ACI models without dynamics BHP NAB NCP TLS WOW BHP NAB NCP TLS WOW Constants and backward recurrence parameters ω 1 −0.970*** −0.304*** −0.256** −0.578* −1.849*** p 1 ω 2 −0.250* −0.243** −0.088 −0.814** −0.569*** p 2 ω 3 −0.671*** −0.845*** −0.517*** −0.611** −0.639*** p 3 ω 4 −0.450*** −1.463*** −1.131*** −0.440* −1.827*** p 4 ω 5 −1.343*** −1.427*** −1.583*** −0.718** −0.987*** p 5 ω 6 −1.771*** −1.552*** −1.393*** −1.030*** −3.317*** p 6 Seasonality parameters 12 ν11:00 −0.779*** −0.817*** −1.134*** 5.225*** −1.216*** 34 ν11:00 12 ν12:00 0.247 0.424 0.861*** −9.370*** 0.998*** 34 ν12:00 12 ν13:00 −0.867*** −1.119*** −0.951*** 0.947 −0.257 34 ν13:00 12 ν14:00 2.917*** 3.231*** 2.764*** 8.350*** 1.551*** 34 ν14:00 12 ν15:00 −0.474** −0.909*** −1.008*** −4.896*** −1.108** 34 ν15:00 12 ν16:00 −0.072 0.584* 1.315*** 4.353** 1.640*** 34 ν16:00 56 ν11:00 −0.414 −1.361*** −1.125*** −0.220 −1.224*** 56 ν14:00 56 ν12:00 0.042 1.531*** 1.018*** 0.352 0.920 56 ν15:00 56 ν13:00 −1.498*** −1.592*** −1.353** −2.334*** 0.047 56 ν16:00 Explanatory variables AD 1 −5.852*** −2.001*** −2.542*** −18.482*** −1.714*** BD 1 AD 2 4.520*** 1.436*** 2.372*** 3.827*** 1.153*** BD 2 AD 3 −6.738*** −1.523*** −3.776*** −27.456*** −1.939*** BD 3 AD 4 0.578 0.008 −0.225 3.221** −0.477 BD 4 AD 5 5.127*** 6.341*** 7.808*** 2.694*** 5.165*** BD 5 1.036** 0.333 0.570 −1.004 0.942 BD 6 AD 6 0.828*** 0.772*** 0.779*** 0.806*** 0.557*** 0.830*** 0.759*** 0.777*** 0.827*** 0.575*** 0.837*** 0.865*** 0.849*** 0.795*** 0.701*** 0.804*** 0.837*** 0.811*** 0.700*** 0.614*** 0.713*** 0.706*** 0.823*** 0.649*** 0.604*** 0.727*** 0.714*** 0.791*** 0.668*** 0.627*** −0.403* −0.403 −0.812*** 4.292** −0.571* −0.360 0.245 0.594* −8.839** −0.240 −0.415 −1.408*** −1.149*** 4.130** 0.862* 2.459*** 3.449*** 2.791*** 5.828*** 1.174* −0.129 −1.453*** −0.210 −5.695* −1.713** −0.147 0.741 −1.208** 2.312 2.539*** 3.742*** 2.795*** 3.108*** 4.911*** 1.569** −1.188*** −1.263*** −0.898 −2.894*** −1.288 0.233 0.685* −0.396 3.788*** 0.932 3.915*** 1.971*** 2.843*** 7.732*** 0.465 −6.109*** −1.208*** −3.027*** −17.238*** −0.745* 0.113*** 0.024 −0.621* 3.950*** −1.365*** −7.338*** −2.499*** −4.289*** −26.201*** −3.493*** 1.746*** −0.108 −0.974 0.881 −0.589 6.696*** 6.540*** 6.174*** 4.977*** 4.212*** 152 A. D. Hall, N. Hautsch
Table 5 (cont<strong>in</strong>ued) AV 1 AV 2 AV 3 AV 4 AV 5 AV 6 BV 1 BV 2 BV 3 BV 4 BV 5 BV 6 MQ 1 MQ 2 MQ 3 MQ 4 MQ 5 MQ 6 SP 1 SP 2 SP 3 BHP NAB NCP TLS WOW BHP NAB NCP TLS WOW 1.717*** 0.639 −2.788*** 12.936*** 0.091 DAV 1 −0.415 −0.261 −0.648*** −1.970*** 0.587* 1.510*** 0.455 0.802* 0.909 1.200* DAV 2 −0.615** −0.210 −0.796*** −0.990*** −0.437* 7.577*** 4.381*** 8.662*** 21.358*** 3.234*** DAV 3 0.367 −0.566** −0.306 −0.499 0.164 −10.311*** −10.219*** −9.006*** −5.775*** −15.237*** DAV 4 −0.837** −0.302 −0.660* −1.029*** 1.491*** −0.774 0.157 1.334 3.986*** 3.446*** DAV 5 1.587*** 1.868*** 1.630*** −0.346 1.719*** −3.138*** −6.224*** −5.912*** −4.956*** −12.886*** DAV 6 0.442 −1.120*** −0.642 0.598** 0.314 0.787* −0.651* 3.028*** −3.281* 2.461*** DBV 1 −0.234 −0.514*** −0.301* −0.932*** −0.374* 0.084 −0.759** −0.244 10.528*** −1.222* DBV 2 −0.143 −0.284*** −0.284 −1.270*** 0.310* −4.146*** −4.487*** −6.735*** −5.331*** −1.634* DBV 3 −0.227 −0.218 −0.140 −1.055** −0.410** 13.824*** 11.335*** 11.352*** 21.499*** 18.504*** DBV 4 0.440 0.082 0.734* −0.125 0.635** −4.307*** −4.653*** −6.657*** −6.256*** −7.446*** DBV 5 −1.502*** −0.129 −0.784** 0.181 0.329 −2.250*** 1.421* 0.830 2.351** 10.951*** DBV 6 0.551* 1.270*** 1.271*** −0.937*** 1.600*** −1.642*** −0.310*** −0.678*** −0.856*** −0.183** VL 1 3.989*** 0.261*** 2.613*** 0.068 0.279* 1.493*** 0.177*** 0.380*** 1.547*** 0.289*** VL 2 4.621*** 0.256*** 2.442*** 0.001 0.265* −0.260* −0.026 −0.253*** −0.782 −0.035 VL 3 3.772*** 0.029 1.405*** 0.039 −0.021 0.482*** −0.049 −0.109 1.034** 0.029 VL 4 3.892*** 0.114*** 0.975*** −0.073 −0.342 −1.321*** −0.171** −0.856*** 0.411 −0.399** VL 5 5.922*** 0.271*** 2.889*** 0.026 0.483* 1.870*** −0.080 0.433*** 0.205 0.586*** VL 6 5.942*** 0.273*** 1.547*** 0.083 0.755*** −1.951*** −0.389*** −1.148*** −3.472 −0.665*** SP 4 0.535*** 0.188*** 0.493*** 1.745*** 0.193*** −1.740*** −0.299*** −0.917*** −2.502 −0.377*** SP 5 −0.898*** 0.051* −0.311*** −1.514** −0.019 0.639*** 0.221*** 0.416*** 1.787*** 0.229*** SP 6 −1.010*** 0.129*** −0.157** −1.332* −0.196** Diagnostics Obs 9,316 10,463 9,142 3,102 3,438 LL −20,426 −24,192 −20,210 −6,422 −7,322 BIC −20,809 −24,581 −20,593 −6,759 −7,664 Order aggressiveness and order book dynamics 153
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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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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
Page 119 and 120: How large is liquidity risk in an a
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 154 and 155: Table 4 Fully specified ACI models
Page 156 and 157: Table 4 (continued) BHP NAB NCP TLS
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
Page 173 and 174: Roman Liesenfeld . Ingmar Nolte . W
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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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