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Table 2 The base-line VEC model for the entire sample Equation Variable NYSE Sample 1996 NYSE Sample 2000 SSE Sample 2000 Coefficients average Std. (+) (−) Coefficients average Std. (+) (−) Coefficients average Std. (+) (−) at s −0.0627 0.0234 0 11 −0.0401 0.0201 0 11 −0.0795 0.0298 0 11 Δa −0.0639 0.0568 2 9 0.0298 0.0475 9 2 −0.1935 0.1255 0 10 x B 0.0038 0.0015 11 0 0.0034 0.0022 11 0 0.0011 0.0010 11 0 x S −0.0004 0.0006 3 7 −0.0013 0.0012 0 11 −0.0004 0.0005 1 10 bt s 0.0541 0.0226 11 0 0.0438 0.0187 11 0 0.1016 0.0140 11 0 Δb −0.0558 0.0629 2 9 0.0183 0.0412 8 3 −0.1834 0.0669 0 10 x B 0.0002 0.0005 6 4 0.0006 0.0006 10 0 0.0004 0.0004 11 0 x S −0.0040 0.0019 0 11 −0.0040 0.0027 0 11 −0.0012 0.0012 0 11 x B t s 0.3611 1.2205 8 2 2.0670 1.1449 11 0 −2.6797 9.3107 4 7 Δa −34.9566 15.6377 0 11 −18.4364 13.8204 0 11 −79.9208 63.2079 0 11 x B 0.7402 0.0457 11 0 0.6917 0.0625 11 0 0.7300 0.0796 11 0 x S 0.1702 0.0297 11 0 0.2086 0.0410 11 0 0.2509 0.0574 11 0 x S t s 1.2202 0.8637 10 1 1.8800 1.2852 11 0 15.5386 17.0832 10 1 Δb 34.6731 16.7873 11 0 16.9249 14.1931 11 0 87.8538 70.3804 11 0 x B 0.1351 0.0429 11 0 0.1671 0.0360 11 0 0.2124 0.0511 11 0 x S A. Escribano and R. Pascual 0.6935 0.0653 11 0 0.6325 0.0454 11 0 0.7058 0.0575 11 0 68
Table 2 (continued) Equation Variable NYSE Sample 1996 NYSE Sample 2000 SSE Sample 2000 R 2 Coefficients average Std. (+) (−) Coefficients average Std. (+) (−) Coefficients average Std. (+) (−) at 0.0826 0.0294 0.0537 0.0260 0.1444 0.0489 bt 0.0802 0.0299 0.0555 0.0263 0.1478 0.0493 x B t 0.5157 0.0658 0.4860 0.0547 0.4845 0.1212 x S t 0.4621 0.0798 0 1 B This table summarizes the estimation of the VEC model, B 0 @ 0 0 0.3977 0 AaB 1 AbB 0 1 0 0 AaB AbS 0 1 0.0298 1 at C bt C A ex 0.5241 0.1128 B t ex S 0 1 B C B C @ A t ¼ EC a L ð Þ EC b L 0 B @ 1 ð Þ C BðLÞ A SðLÞ st at 1 bt 1 1 þ AL ð Þ ex B t 1 ex S 0 1 B C B C @ A t 1 þ ua t ub t uB t uS 0 1 AaaðLÞ 0 AaBðLÞ AaSðLÞ B 0 AbbðLÞ AbBðLÞ AbSðLÞ C restrictions, AL ð Þ ¼ B C @ ABaðLÞ 0 ABBðLÞ ABSðLÞA 0 1 B C B C @ Awith the t 0 ASbðLÞ ASBðLÞ ASSðLÞ The model is defined in trade time and truncated at 5 lags. We use data on 11 NYSE-listed stocks from January to March 1996, 11 NYSE-listed stocks from January to March 2000, and 11 SSE stocks from July to September 2000. The model is estimated by SURE. We report, for each sample and variable, the cross-sectional average of the sum of all lags whenever the coefficients are statistically significant at the 1% level. We also provide the cross-sectional average R 2 for each equation in the system. Finally, we include the number of stocks for which the coefficient of the corresponding variable is statistically positive/negative at the 1% level. The error-correction term is the bid-ask spread; Δat (Δbt) is the change in the ask (bid) quote between two consecutive trades. The trade-size indicators are ex B t ¼ xBt log ðVtÞand ex S t ¼ xS t log ðVtÞ, where Vt is the trade size in number of shares, and xt B (xt S ) equals 1 for buys (sells) and zero otherwise. For midpoint trades both variables equal zero Asymmetries in bid and ask responses to innovations in the trading process 69
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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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Page 38 and 39: 32 K. Bien et al. Although economet
Page 40 and 41: 34 K. Bien et al. 2.1 Copula functi
Page 42 and 43: 36 K. Bien et al. and x k t ≡ (xk
Page 44 and 45: 38 K. Bien et al. Fig. 3 Multivaria
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 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 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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How large is liquidity risk in an a
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134 A. D. Hall, N. Hautsch In this
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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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