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Table 3 Estimated probabilities of price change conditional on volume and duration Volume (percentile) Duration (percentile) Δp t ≤−2 −1≤Δp t ≤ 1 Δp t ≥−2 IBM GE BA MO IBM GE BA MO IBM GE BA MO (a) Seller-initiated trades 20th 20th 0.243 0.119 0.139 0.148 0.742 0.875 0.848 0.839 0.016 0.006 0.014 0.013 40th 0.298 0.142 0.203 0.184 0.677 0.850 0.775 0.799 0.025 0.008 0.022 0.017 60th 0.327 0.157 0.222 0.199 0.645 0.834 0.751 0.784 0.027 0.008 0.027 0.017 80th 0.374 0.163 0.226 0.210 0.601 0.831 0.738 0.774 0.025 0.006 0.036 0.016 40th 20th 0.246 0.125 0.141 0.158 0.742 0.869 0.846 0.832 0.012 0.006 0.013 0.010 40th 0.307 0.146 0.205 0.192 0.673 0.846 0.775 0.796 0.020 0.008 0.021 0.012 60th 0.341 0.163 0.227 0.215 0.636 0.829 0.748 0.772 0.024 0.008 0.025 0.013 80th 0.390 0.168 0.253 0.224 0.583 0.825 0.717 0.763 0.027 0.007 0.030 0.014 60th 20th 0.241 0.128 0.142 0.163 0.749 0.866 0.847 0.829 0.011 0.007 0.012 0.009 40th 0.320 0.153 0.212 0.200 0.661 0.839 0.769 0.790 0.018 0.008 0.019 0.011 60th 0.366 0.174 0.241 0.231 0.612 0.818 0.737 0.757 0.022 0.008 0.023 0.012 80th 0.411 0.181 0.269 0.245 0.564 0.812 0.704 0.746 0.026 0.007 0.027 0.010 80th 20th 0.202 0.120 0.133 0.161 0.789 0.874 0.858 0.831 0.009 0.005 0.009 0.008 40th 0.341 0.153 0.223 0.202 0.640 0.840 0.759 0.788 0.018 0.007 0.018 0.010 60th 0.397 0.202 0.267 0.264 0.582 0.791 0.712 0.723 0.021 0.006 0.021 0.013 80th 0.472 0.215 0.313 0.269 0.509 0.777 0.663 0.720 0.018 0.008 0.024 0.011 262 A. S. Tay, C. Ting
Table 3 (continued) Volume (percentile) Duration (percentile) Δpt ≤−2 −1≤Δpt ≤ 1 Δpt ≥−2 IBM GE BA MO IBM GE BA MO IBM GE BA MO (b) Buyer-initiated trades 20th 20th 0.021 0.006 0.016 0.015 0.804 0.895 0.868 0.843 0.176 0.099 0.116 0.142 40th 0.030 0.008 0.023 0.019 0.743 0.868 0.802 0.809 0.227 0.124 0.175 0.171 60th 0.033 0.007 0.022 0.015 0.717 0.854 0.799 0.834 0.250 0.140 0.179 0.151 80th 0.037 0.007 0.031 0.013 0.695 0.855 0.783 0.822 0.269 0.138 0.187 0.165 40th 20th 0.015 0.006 0.014 0.012 0.813 0.891 0.857 0.841 0.171 0.103 0.129 0.148 40th 0.026 0.008 0.021 0.015 0.729 0.861 0.786 0.806 0.245 0.131 0.193 0.179 60th 0.030 0.008 0.022 0.012 0.700 0.846 0.784 0.819 0.269 0.146 0.194 0.169 80th 0.035 0.007 0.027 0.012 0.676 0.852 0.776 0.821 0.288 0.141 0.198 0.168 60th 20th 0.013 0.006 0.011 0.010 0.816 0.886 0.890 0.837 0.171 0.108 0.100 0.154 40th 0.024 0.008 0.019 0.012 0.701 0.848 0.776 0.793 0.275 0.145 0.206 0.195 60th 0.027 0.008 0.021 0.011 0.694 0.831 0.783 0.807 0.279 0.161 0.197 0.182 80th 0.033 0.007 0.025 0.010 0.650 0.838 0.766 0.798 0.317 0.155 0.210 0.192 80th 20th 0.015 0.005 0.012 0.007 0.844 0.888 0.866 0.844 0.142 0.107 0.123 0.149 40th 0.019 0.009 0.017 0.010 0.719 0.866 0.729 0.767 0.261 0.125 0.254 0.223 60th 0.024 0.007 0.020 0.011 0.657 0.795 0.765 0.789 0.319 0.199 0.215 0.200 80th 0.030 0.008 0.021 0.007 0.602 0.804 0.745 0.743 0.368 0.188 0.234 0.250 Intraday stock prices, duration, and volume 263
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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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102 S. Frey, J. Grammig approximate
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104 To obtain the estimates in the
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106 Hence, using the liquidity stat
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108 In the main text we discuss the
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Pierre Giot . Joachim Grammig How l
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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
Page 218 and 219: 212 Table 2 Predictability of the c
Page 220 and 221: 214 6 Conclusion Using 5-min euro/d
Page 222 and 223: 216 where σk is the standard devia
Page 224 and 225: 218 If we meet a particular case su
Page 226 and 227: 220 C.5. Triple bottom (TB) TB is c
Page 228 and 229: 222 References W. B. Omrane, H. V.
Page 231 and 232: Juan M. Rodríguez-Poo · David Ver
Page 233 and 234: Semiparametric estimation for finan
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Page 260 and 261: 254 between price changes and durat
Page 262 and 263: 256 simply aggregated. Even after a
Page 264 and 265: 258 is to make use of the fact that
Page 266 and 267: 260 together with the restrictions
Page 270 and 271: 264 A. S. Tay, C. Ting More interes
Page 272 and 273: Table 4 Estimated probabilities of
Page 274 and 275: 268 Acknowledgements Tay gratefully
Page 276 and 277: 270 This paper explores the effect
Page 278 and 279: 272 contrast, price responses to po
Page 280 and 281: 274 Fig. 1 Continuous line is the T
Page 282 and 283: 276 the β’s can form a convex sh
Page 284 and 285: 278 fixing some intervals around th
Page 286 and 287: 280 . That is for a given absolute
Page 288 and 289: 282 30 25 20 15 10 5 CC UNEMW ISM U
Page 290 and 291: Appendix Table A1 Consumer confiden
Page 292 and 293: Table A3 Non-farm payrolls • ✓
Page 294 and 295: Table A5 Weekly unemployment claims
Page 296 and 297: 290 Table A8 Retail sales • ✓ 1
Page 298 and 299: 292 Table A12 GDP, BI, TB and PI Re
Page 300 and 301: 294 V. Voev with the problem of how
Page 302 and 303: 296 V. Voev 2.1 A sample covariance
Page 304 and 305: 298 V. Voev Using the equicorrelate
Page 306 and 307: 300 V. Voev where �kl(t) is the (
Page 308 and 309: 302 V. Voev where hkl,k ′ l ′ i
Page 310 and 311: 304 V. Voev is 1.9%. From the daily
Page 312 and 313: 306 V. Voev ACF ACF ACF 0.5 0.5 0.5
Page 314 and 315: 308 V. Voev autocorrelated. Indeed,
Page 316 and 317: 310 V. Voev 5 Conclusion Table 2 Ro
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312 V. Voev Engle R (1982) Autoregr
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