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188 Table 3 ML estimates of the logistic ACM-ARMA model with microstructure variables and nonsymmetric response coefficients* Par. JBX HAL Estimate Std. dev. Estimate Std. dev. μ1 −0.5443 0.2146 −0.0684 0.0079 μ2 −0.6294 0.2470 −0.0806 0.0087 (1) c1 0.8319 0.0628 1.1283 0.0277 (2) c1 −0.1738 0.0275 a11 (1) 0.1494 0.0366 0.1157 0.0148 a12 (1) 0.0561 0.0269 0.1648 0.0138 (1) a21 0.1798 0.0442 0.3795 0.0137 (1) a22 0.1075 0.0233 0.0431 0.0146 a11 (2) −0.0047 0.0153 a12 (2) −0.1053 0.0139 (2) a21 −0.2967 0.0139 (2) a22 0.0653 0.0150 gv1 (0) 0.1969 0.0316 0.1073 0.0111 gv2 (0) 0.2259 0.0312 0.1960 0.0112 (0) gt1 0.3499 0.0251 0.2599 0.0132 (0) gt2 0.3289 0.0253 0.1546 0.0133 gv1 (1) −0.0792 0.0327 −0.0761 0.0112 gv2 (1) −0.0262 0.0316 −0.0949 0.0114 (1) gt1 (1) −0.0001 0.0238 −0.0106 0.0132 g t2 0.0546 0.0234 0.0298 0.0132 Log-lik. −0.885792 −0.990106 SIC 0.897808 0.993094 Q(30) 125.6 (0.106) 169.9 (0.000) Q(50) 211.1 (0.109) 269.3 (0.001) Res. mean (−0.003, −0.001) (0.001, −0.002) Res. var. 0:898 0:035 0:035 1:092 0:886 0:044 0:044 1:141 *Dependent variable is the direction of the price changes, D i, p-values in brackets R. Liesenfeld et al. IBM transaction prices. In particular, they also find a positive impact of transaction volume and time between transactions on the activity of transaction prices. Table 4 reports the estimation results of the augmented GLARMA model for the absolute (non-zero) price changes. We find no evidence for a strong leverage effect when accounting for lagged effects. For both shares the contemporaneous effect of Di on the volatility measure S i is negative, supporting the hypothesis of a leverage effect. But this effect is completely over-compensated for in JBX and nearly compensated for in HAL by the positive effect of D i−1. This result stands in contrast to the findings by Rydberg and Shephard (2003) who find a leverage effect for transaction prices of the IBM share traded at the NYSE. Again, volume and transaction rate have a positive impact on the size of the price changes. Since the size of the price changes as well as the probability of a non-zero price change are volatility measures, our previous conclusions based upon the ACM component
Modelling financial transaction price movements: a dynamic integer count data model 189 Table 4 ML estimates of the GLARMA model with microstructure variables and leverage effect* Par. JBX HAL Estimate Std. dev. Estimate Std. dev. γ0 −0.1565 0.0439 −0.0279 0.0069 γ1 0.9321 0.0219 1.6822 0.0484 γ2 −0.6891 0.0468 δ1 0.1234 0.0183 0.1630 0.0112 δ2 −0.1445 0.0102 ζ1 0.1852 0.0282 0.1494 0.0158 ζ2 −0.1304 0.0153 ν0 −0.0491 0.0422 −0.0045 0.0016 ν1 0.0077 0.0071 0.0002 0.0003 ν2 0.0156 0.0074 0.0001 0.0003 ν3 −0.0140 0.0151 −0.0007 0.0005 ν4 −0.0095 0.0102 −0.0001 0.0003 1 2 1.2476 0.0808 1.3026 0.0349 β d0 −0.0699 0.0288 −0.0960 0.0109 β d1 0.0748 0.0390 0.0709 0.0157 βv 0 0.2227 0.0208 0.3212 0.0087 βv1 0.0561 0.0228 0.0809 0.0097 β t0 0.0427 0.0216 0.2723 0.0133 β t1 0.0214 0.0178 0.0297 0.0109 Log-lik. −0.824800 −0.811089 SIC 0.839589 0.813928 Q(30) 55.0 (0.000) 49.7 (0.000) Q(50) 76.6 (0.001) 76.2 (0.000) Res. mean 0.004 0.002 Res. var. 0.981 1.028 *Dependent variable is the absolute value of the size of a non-zero price change, S i|S i >0, p-values in brackets regarding the empirical confirmation of various implications from market microstructure theory are confirmed. 4 Diagnostics based on the predicted price change distribution So far we have analyzed the individual components of the ICH model – the ACM- ARMA part for the price direction and the GLARMA part for the price change size given the price direction – separately. However, the ICH model is a specification for the overall conditional distribution of the transaction price changes. Hence, in this section, we check to what extend the merged components of the ICH model are capable to capture the features of the observed price change distribution. In particular, based on the estimates for the model components with microstructure variables (see Tables 3 and 4), we analyze the goodness-of-fit of the ICH model.
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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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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
Page 154 and 155: Table 4 Fully specified ACI models
Page 156 and 157: Table 4 (continued) BHP NAB NCP TLS
Page 158 and 159: Table 5 ACI models without dynamics
Page 162 and 163: Table 6 ACI models without covariat
Page 166 and 167: 160 specification which includes or
Page 168 and 169: 162 5.2.5 The impact of the bid-ask
Page 170 and 171: 164 contrast to those of Pascual an
Page 173 and 174: Roman Liesenfeld . Ingmar Nolte . W
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Page 206 and 207: 200 W. B. Omrane, H. V. Oppens anal
Page 208 and 209: 202 W. B. Omrane, H. V. Oppens of s
Page 210 and 211: 204 The extrema detection method ba
Page 212 and 213: 206 price 0.8565 0.8575 0.8585 0.85
Page 214 and 215: 208 We distinguish three possible c
Page 216 and 217: 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
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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
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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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