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Table 1 Order book characteristics BHP NAB NCP TLS WOW Mean S.D. Mean S.D. Mean S.D. Mean S.D. Mean S.D. sprd 1.075 0.311 2.175 1.516 1.277 0.599 1.006 0.079 1.485 0.922 trvol 2,746.7 7,688.5 1,174.1 2,901.3 1,113.7 4,131.5 8,877.5 39,414.2 1,646.7 4,906.6 buyvol 5,692.4 9,783.9 2,830.6 4,016.9 4,817.5 7,408.2 2,1233.8 57,399.5 3,707.5 7,184.2 sellvol 8,336.2 12,190.6 2,902.7 3,902.7 5,358.7 7,789.6 21,816.1 60,877.4 4,410.3 6,785.4 qvol 8,303.4 38,223.4 3,534.3 4,858.5 5,235.3 6,238.4 35,513.0 68,337.0 4,832.7 7,329.7 qavol 9,704.9 12,842.4 3,621.1 5,169.3 5,209.8 6,058.5 35,077.7 68,038.8 5,123.8 7,046.5 qbvol 7,212.8 49,667.3 3,453.4 4,549.0 5,264.2 6,436.2 35,927.5 68,618.7 4,585.3 7,553.3 cvol 11,172.2 81,984.5 4,308.8 6,063.3 4,533.8 5,999.2 61,007.7 97,236.4 6,021.8 9,469.3 cavol 11,595.3 17,307.7 4,348.3 6,392.8 4,315.7 5,438.5 64,477.8 100,813.0 5,793.7 8,761.2 cbvol 10,778.7 112,671.2 4,271.5 5,736.1 4,774.1 6,553.5 58,166.4 94,120.8 6,211.8 10,018.1 d_askp 5.842 86.084 6.407 24.454 7.247 52.743 3.198 11.616 4.818 16.021 d_bidp 4.662 16.628 8.543 71.853 6.131 14.328 2.377 7.798 4.104 14.611 amq 0.115 0.316 0.423 0.809 0.101 0.319 0.040 0.197 0.230 0.518 avol 1,452.4 343.7 265.1 62.1 920.6 256.4 5,409.2 1,558.8 501.8 185.0 bvol 1,376.9 665.7 222.9 78.7 720.3 216.3 6,237.1 1,647.8 344.5 230.7 adiff_1 0.897 0.671 2.018 1.926 1.271 0.939 0.577 0.268 1.376 1.323 adiff_2 1.308 1.007 2.880 2.752 1.884 1.319 0.643 0.357 2.001 1.931 142 A. D. Hall, N. Hautsch
Table 1 (continued) BHP NAB NCP TLS WOW Mean S.D. Mean S.D. Mean S.D. Mean S.D. Mean S.D. BHP NAB NCP TLS WOW Mean S.D. Mean S.D. Mean S.D. Mean S.D. Mean S.D. adiff_5 2.717 2.116 5.829 5.403 3.712 2.340 0.866 0.565 4.024 3.415 bdiff_1 0.898 0.650 1.851 1.700 1.155 0.869 0.582 0.273 1.187 1.032 bdiff_2 1.269 0.954 2.523 2.381 1.633 1.204 0.656 0.371 1.546 1.409 bdiff_5 2.488 1.948 4.806 4.639 3.077 2.078 0.937 0.612 2.781 2.610 adep_1 22.257 10.725 2.404 1.870 11.055 7.154 102.651 35.792 6.352 4.686 adep_2 34.429 21.732 3.614 3.262 15.621 12.257 196.338 77.341 9.862 8.665 adep_5 45.662 40.078 4.577 5.093 18.572 16.865 420.273 216.059 12.729 14.358 bdep_1 20.477 13.596 2.093 1.697 9.203 5.613 117.936 39.229 4.516 4.183 bdep_2 31.411 23.779 3.228 2.924 13.874 10.131 223.766 85.101 7.479 7.465 bdep_5 42.927 38.160 4.293 4.507 17.876 15.783 455.945 240.004 11.290 12.538 Means and standard deviations of various order book characteristics based on the BHP, NAB, NCP, TLS and WOW stock traded at the ASX. The samples contain all market and limit orders of the individual stocks traded at the ASX during July–August 2002, corresponding to 45 trading days. All prices are measured in cents. The order book characteristics are: Bid-ask spread (sprd), traded volume (trvol), traded buy/sell volume (buyvol, sellvol), quoted volume (qvol), quoted ask/bid volume (qavol, qbvol), cancelled volume (cvol), cancelled ask/bid volume (cavol, cbvol), difference between quoted ask price and the current best ask price (d_askp), difference between current best bid quote and quoted bid price (d_bidp), absolute mid-quote change (amq), as well as cumulated ask/bid volume (avol, bvol, in units of 1,000 shares). Furthermore, adiff_x=px,a−mq, where px,a denotes the price associated with the x%-quantile of the cumulated ask volume and mq denotes the mid-quote. Correspondingly, bdiff_x=mq-p x,b, where p x,b denotes the price associated with the x%-quantile of the cumulated bid volume. Moreover, adep_x =(x/100)·avol/(px,a−mq) and bdep_x =(x/100)·bvol/(mq−px,b), measured in units of 1,000 shares Order aggressiveness and order book dynamics 143
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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
Page 98 and 99: 92 subtracting the deviations from
Page 100 and 101: 94 market order distribution that c
Page 102 and 103: 96 Table 2 First stage GMM results
Page 104 and 105: Table 4 First stage GMM results bas
Page 106 and 107: 100 S. Frey, J. Grammig quotes on e
Page 108 and 109: 102 S. Frey, J. Grammig approximate
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 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 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
Page 173 and 174: Roman Liesenfeld . Ingmar Nolte . W
Page 175 and 176: Modelling financial transaction pri
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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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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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