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Table 4 First stage GMM results based on average profit conditions instead of marginal profit conditions Ticker α γ λ ξ μ J(8) p-value τ (%) IFX 0.0004 (151.6) 0.0042 (24.3) 4.5335 (170.6) −0.0051 (14.9) 0.0000 (0.2) 0.1 1.000 0.0157 DBK 0.0057 (102.7) 0.0137 (20.4) 1.1517 (256.1) −0.0031 (19.4) 0.0000 (0.7) 0.3 1.000 0.0099 SAP 0.0286 (32.8) 0.0532 (9.7) 0.5030 (237.4) −0.0058 (9.6) 0.0004 (4.1) 0.7 1.000 0.0109 DCX 0.0029 (145.8) 0.0059 (17.8) 1.5638 (254.9) −0.0023 (17.9) 0.0002 (6.1) 0.8 0.999 0.0124 DB1 0.0152 (56.2) 0.0049 (2.0) 0.7739 (114.1) −0.0016 (4.5) 0.0001 (1.1) 0.8 0.999 0.0251 SIE 0.0045 (117.0) 0.0133 (27.8) 1.1442 (297.3) −0.0030 (23.7) 0.0001 (2.6) 0.8 0.999 0.0080 TUI 0.0053 (103.0) 0.0021 (2.8) 1.3215 (127.0) −0.0016 (7.0) −0.0002 (2.6) 0.9 0.999 0.0374 MUV2 0.0171 (89.0) 0.0234 (15.4) 0.6476 (246.9) −0.0036 (15.7) 0.0001 (0.8) 1.1 0.998 0.0117 FME 0.0428 (24.1) 0.0101 (1.0) 0.3839 (96.4) −0.0016 (1.7) 0.0000 (0.0) 1.5 0.992 0.0303 HVM 0.0015 (83.7) 0.0055 (11.7) 2.8391 (130.9) −0.0032 (12.2) 0.0000 (0.9) 1.8 0.986 0.0218 ALT 0.0216 (83.1) 0.0055 (3.2) 0.5785 (142.4) −0.0015 (6.2) −0.0002 (2.1) 1.9 0.985 0.0259 BAS 0.0053 (161.4) 0.0037 (11.4) 1.1206 (244.1) −0.0015 (16.6) 0.0000 (1.0) 3.1 0.930 0.0136 DTE 0.0002 (232.1) 0.0046 (49.9) 5.0499 (232.7) −0.0061 (18.8) 0.0000 (0.0) 3.8 0.877 0.0064 ALV 0.0155 (80.3) 0.0280 (19.8) 0.6453 (294.4) −0.0039 (18.9) −0.0002 (3.8) 3.8 0.872 0.0099 ADS 0.0503 (31.8) 0.0252 (3.0) 0.3528 (141.0) −0.0028 (3.4) −0.0002 (1.4) 5.1 0.750 0.0191 VOW 0.0062 (21.8) 0.0020 (1.4) 1.0472 (195.8) −0.0011 (3.7) 0.0001 (0.2) 6.5 0.595 0.0166 EOA 0.0053 (79.1) 0.0099 (12.0) 1.0663 (252.7) −0.0027 (12.9) 0.0000 (0.8) 7.5 0.488 0.0107 CBK 0.0016 (139.6) 0.0020 (10.5) 2.4055 (152.0) −0.0018 (13.5) −0.0001 (1.8) 9.7 0.283 0.0248 BMW 0.0051 (143.8) 0.0018 (5.2) 1.2029 (203.0) −0.0013 (13.4) −0.0001 (2.8) 9.7 0.284 0.0176 BAY 0.0024 (168.5) 0.0037 (17.4) 1.6352 (225.8) −0.0017 (17.6) 0.0000 (1.2) 12.9 0.117 0.0167 LIN 0.0232 (93.8) −0.0107 (9.8) 0.5728 (134.6) 0.0004 (3.1) 0.0004 (3.1) 15.0 0.059 0.0305 DPW 0.0025 (131.8) 0.0012 (4.0) 1.8362 (150.0) −0.0015 (8.7) −0.0001 (3.2) 16.0 0.042 0.0252 HEN3 0.0405 (27.5) −0.0004 (0.1) 0.3937 (114.0) −0.0005 (0.6) −0.0003 (1.9) 26.9 0.001 0.0241 SCH 0.0099 (92.5) 0.0052 (6.1) 0.8250 (168.8) −0.0017 (9.4) 0.0000 (0.4) 35.1 0.000 0.0201 CONT 0.0132 (108.1) −0.0105 (12.7) 0.8166 (139.6) 0.0009 (5.0) 0.0002 (2.2) 51.0 0.000 0.0338 MAN 0.0105 (105.0) −0.0086 (10.2) 0.9445 (134.5) 0.0006 (3.4) 0.0004 (5.2) 53.0 0.000 0.0359 98 S. Frey, J. Grammig
Table 4 (continued) Ticker α γ λ ξ μ J(8) p-value τ (%) LHA 0.0019 (137.1) −0.0006 (2.0) 2.3210 (150.8) −0.0008 (6.4) 0.0001 (4.3) 63.0 0.000 0.0307 MEO 0.0134 (115.8) −0.0071 (9.3) 0.9066 (166.7) 0.0000 (0.1) 0.0000 (0.4) 99.1 0.000 0.0345 RWE 0.0051 (178.5) 0.0000 (0.1) 1.2460 (210.3) −0.0009 (11.5) 0.0001 (3.3) 112.6 0.000 0.0188 TKA 0.0024 (139.4) −0.0012 (5.0) 1.9075 (158.9) −0.0002 (2.2) 0.0000 (1.3) 362.6 0.000 0.0285 2×4 quotes from the bid and ask side of the visible book are used to construct average update and average break even conditions. The exponential assumption on the distribution of the trade size is maintained. ¼ m, where m and P denote stock specific sample averages of the non-signed trade sizes (number of shares) and the P midquotes, respectively. The stocks are sorted by ascending order of the J-statistic Liquidity supply and adverse selection in a pure limit order book market 99
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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.
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 94 and 95: Table 1 Sample descriptives Company
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 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 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
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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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