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Table 4 Estimated probabilities of price change conditional on volume and duration and lagged information (a) Seller-initiated trades Volume (Perc.) Duration (Perc.) Δpt ≤−2 Volume Duration Δpt ≥−2 IBM GE BA MO (Perc.) (Perc.) IBM GE BA MO 20th 20th 0.070 0.026 0.074 0.096 (b) Buyer-initiated 20th 20th 0.073 0.025 0.044 0.048 40th 0.026 0.046 0.141 0.067 trades 40th 0.129 0.036 0.167 0.067 60th 0.245 0.053 0.116 0.059 60th 0.084 0.029 0.070 0.040 80th 0.156 0.107 0.111 0.033 80th 0.119 0.023 0.092 0.066 40th 20th 0.175 0.034 0.066 0.135 40th 20th 0.097 0.031 0.055 0.051 40th 0.118 0.052 0.117 0.087 40th 0.120 0.044 0.097 0.074 60th 0.151 0.118 0.113 0.098 60th 0.138 0.041 0.114 0.079 80th 0.211 0.046 0.109 0.059 80th 0.172 0.040 0.102 0.065 60th 20th 0.113 0.054 0.065 0.064 60th 20th 0.088 0.036 0.054 0.057 40th 0.202 0.067 0.136 0.095 40th 0.148 0.048 0.115 0.086 60th 0.427 0.077 0.183 0.104 60th 0.199 0.055 0.095 0.081 80th 0.324 0.087 0.189 0.082 80th 0.195 0.057 0.089 0.061 80th 20th 0.082 0.038 0.084 0.086 80th 20th 0.076 0.022 0.156 0.085 40th 0.235 0.062 0.176 0.146 40th 0.243 0.049 0.126 0.112 60th 0.254 0.362 0.192 0.108 60th 0.447 0.136 0.260 0.132 80th 0.215 0.088 0.130 0.108 80th 0.272 0.285 0.117 0.086 266 A. S. Tay, C. Ting
Intraday stock prices, duration, and volume 267 Seller-Initiated Trades Buyer-Initiated Trades a Current Duration 20th Percentile Lagged Duration 80th Percentile 0.4 0.2 0 -10 change. The distributions here are also much tighter than the corresponding distributions in Fig. 2. This suggests that our results reported earlier are more appropriate at median values of the lagged variables, or when current durations are short relative to lagged durations. These results are merely indicative; a more extensive study of the dynamic relationships in the framework used here is left for future research. 5 Concluding comments -5 0 Price Change c Current Duration 20th Percentile Lagged Duration 80th Percentile 0.4 0.2 0 -10 -5 0 Price Change 5 5 10 10 80 80 70 70 60 60 50 40 Volume (Perc.) 50 20 0 30 -10 70 80 We investigate the distribution, conditional on trading volume, duration between trades, and the sign of trades of high-frequency price changes on four stocks traded on the New York Stock Exchange. The conditional probabilities are estimated nonparametrically using local polynomial regression methods. We find substantial skewness in the distribution of price changes, with the direction of skewness dependent on trade sign. We also find that the probability of larger price changes increases with volume, but only for trades that occur with longer durations. Durations affect prices, with a stronger effect when volume is high. The evidence suggests substantial interaction effects between duration and volume with respect to their effect on prices; parametric analyses of the relationship between duration, volume, and prices should take these into account, such as in Tay et al. (2004). The high degree of skewness in the distributions indicate that it is also important to distinguish between seller- and buyer-initiated trades, and that care should be taken in interpreting results concerning the volatility of price changes. The results are consistent with theoretical research. Our findings for seller-initiated trades provide direct evidence in support of the Diamond and Verrecchia (1987) analysis, where larger probability of price falls is associated with a higher level of duration. 0.4 0.2 0 20 -10 30 40 Volume (Perc.) b Current Duration 80th Percentile Lagged Duration 20th Percentile -5 0 Price Change d Current Duration 80th Percentile Lagged Duration 20th Percentile 0.4 0.2 -5 0 Price Change Fig. 3 Estimated conditional probabilities (GE with lagged duration) 5 5 10 10 70 80 60 60 50 40 30 Volume (Perc.) 50 40 30 Volume (Perc.) 20 20
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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
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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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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 268 and 269: Table 3 Estimated probabilities of
Page 270 and 271: 264 A. S. Tay, C. Ting More interes
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
Page 318: 312 V. Voev Engle R (1982) Autoregr
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