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Table 1 Sample descriptives Company name Ticker symbol Turnover Mkt. Cap. m Percentage of aggr. trades (%) Trades per day LO sub. LO canc. P Eff. spread TUI TUI 26,281,175 2,025 24,723 17.6 1,063 6,767 5,714 18.7 0.125 0.015 CONTINENTAL CONT 25,627,638 4,060 25,574 13.5 1,002 8,036 7,052 31.6 0.092 −0.011 MAN MAN 27,685,031 2,434 26,189 13.0 1,057 7,214 6,235 27.7 0.096 0.003 METRO MEO 38,874,669 5,018 31,480 15.7 1,235 7,975 6,702 35.0 0.089 0.000 LINDE LIN 22,378,772 3,448 24,971 15.8 896 8,342 7,454 43.6 0.080 −0.009 LUFTHANSA LHA 43,946,809 4,548 32,504 11.9 1,352 8,079 6,780 14.2 0.111 0.022 FRESENIUS FME 12,850,947 1,944 20,680 16.7 621 5,764 5,195 54.0 0.098 0.010 THYSSEN-KRUPP TKA 37,892,493 6,450 30,017 11.3 1,262 7,864 6,672 15.9 0.111 0.029 DEUTSCHE POST DPW 43,836,617 6,806 33,330 11.0 1,315 6,861 5,666 18.2 0.097 0.018 HYPO-VEREINSB. HVM 98,351,090 6,629 50,783 15.0 1,937 10,204 8,293 18.7 0.098 0.019 COMMERZBANK CBK 53,171,668 7,569 36,659 12.6 1,450 11,922 10,476 15.4 0.100 0.023 ADIDAS-SALOMON ADS 31,976,047 4,104 32,635 20.1 980 8,057 7,105 92.6 0.070 −0.002 DEUTSCHE BOERSE DB1 35,696,903 4,847 36,359 18.4 982 6,598 5,698 46.9 0.075 0.003 HENKEL HEN3 18,174,548 3,682 25,904 16.6 702 7,989 7,306 65.9 0.077 0.005 ALTANA ALT 30,985,416 3,338 28,310 18.9 1,095 7,718 6,609 48.6 0.079 0.008 SCHERING SCH 51,413,053 7,055 33,756 16.2 1,523 9,111 7,669 40.8 0.071 0.004 INFINEON IFX 146,462,315 4,790 52,331 8.6 2,799 10,320 7,744 11.6 0.104 0.040 BAYER BAY 88,776,121 15,911 36,994 12.4 2,400 15,258 12,988 23.1 0.076 0.012 RWE RWE 97,655,566 12,653 42,203 13.0 2,314 14,438 12,355 33.8 0.062 0.002 BMW BMW 87,854,358 12,211 41,639 14.4 2,110 14,736 12,764 34.7 0.060 0.003 VOLKSWAGEN VOW 104,249,843 9,688 40,963 16.0 2,545 13,474 11,273 39.2 0.056 0.004 BASF BAS 124,434,537 25,425 48,236 13.8 2,580 18,211 15,898 43.3 0.051 0.002 SAP SAP 184,628,162 27,412 65,795 21.9 2,806 19,733 17,095 131.5 0.049 0.001 E.ON EOA 160,625,983 33,753 55,950 13.6 2,871 18,899 16,468 52.5 0.048 0.003 Real. spread 88 S. Frey, J. Grammig
Table 1 (continued) Company name Ticker symbol Turnover Mkt. Cap. m Percentage of aggr. trades (%) Trades per day LO sub. LO canc. P Eff. spread MUENCH.RUECK MUV2 207,353,230 16,396 60,534 20.7 3,425 20,154 16,894 93.9 0.049 0.005 DAIMLERCHRYSLER DCX 187,737,846 30,316 56,736 14.5 3,309 18,722 15,919 36.4 0.055 0.010 DEUTSCHE TELEKOM DTE 350,627,866 34,858 78,884 5.0 4,445 14,498 11,009 15.7 0.072 0.031 DEUTSCHE BANK DBK 309,282,831 38,228 78,083 19.3 3,961 23,169 19,772 67.2 0.044 0.004 ALLIANZ ALV 289,980,556 33,805 64,114 21.4 4,523 29,791 25,882 100.1 0.049 0.010 SIEMENS SIE 321,704,299 52,893 72,831 16.7 4,418 23,659 19,920 64.0 0.041 0.006 Average 108,683,880 14,076 42,972 15.2 2,099 12,785 10,887 44.5 Mkt. cap. is the market capitalization in million euros at the end of December 2003, m is the average trade size (in euros). Percentage of Aggr. trades (%) gives the percentage of total trading volume that has not been executed at the best prices (that is, the order walked up the book). Turnover is the average trading volume in euros per trading day, trades per day is the average number of trades per day, LO sub. and LO canc., respectively, denote the average number of non-marketable limit order submissions and cancelations per day. P, eff. spread and real. spread refer to the sample averages of midquote, effective spread and realized spread, respectively. The average effective spread is computed by taking two times the absolute difference of the transaction price of a trade and the prevailing midquote and averaging over all trades of a stock. The average realized spread is computed similarly, but instead of taking the prevailing midquote, we use the midquote five minutes after the trade. To ensure comparability across stocks, we compute effective and realized spreads relative to the midquote prevailing at the time of the trade and multiply by 100 to obtain a % figure. The table is sorted in descending order by the difference of effective and realized spread. The sample ranges from January 2, 2004 to March 31, 2004 Real. spread Liquidity supply and adverse selection in a pure limit order book market 89
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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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Page 46 and 47: 40 K. Bien et al. Bivariate model s
Page 48 and 49: 42 K. Bien et al. deviation 0.0099,
Page 50 and 51: 44 K. Bien et al. - Set ˆzt = Âxt
Page 52 and 53: 46 K. Bien et al. % Frequency −0.
Page 54 and 55: 48 K. Bien et al. References Amilon
Page 56 and 57: 50 1 Introduction A. Escribano and
Page 58 and 59: 52 A. Escribano and R. Pascual Jang
Page 60 and 61: 54 A. Escribano and R. Pascual the
Page 62 and 63: 56 The generating processes of mark
Page 64 and 65: 58 with AtðLÞ ¼ 0 B @ 1 ð ÞAab
Page 66 and 67: 60 A. Escribano and R. Pascual cros
Page 68 and 69: 62 Hasbrouck (1991). The system is
Page 70 and 71: 64 unitary seller-initiated shock (
Page 72 and 73: 66 6.1 Estimation of the baseline m
Page 74 and 75: Table 2 The base-line VEC model for
Page 76 and 77: 70 Table 3 Simulation of the base-l
Page 78 and 79: 72 revert towards narrow levels. As
Page 80 and 81: Table 5 Impulse-response functions
Page 82 and 83: 76 We also show that NYSE buyer-ini
Page 84 and 85: 78 As 0 < a m < 1; L ð Þ ¼ 1 a m
Page 86 and 87: 80 NYSE 2000 Stocks AOL America Onl
Page 88 and 89: 82 A. Escribano and R. Pascual Madh
Page 90 and 91: 84 1 Introduction S. Frey, J. Gramm
Page 92 and 93: 86 develops the empirical methodolo
Page 96 and 97: 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
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Page 140 and 141: 134 A. D. Hall, N. Hautsch In this
Page 142 and 143: 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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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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