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124 Table 2 Estimation results at the 10-min frequency Frictionless v=5,000 v=20,000 v=40,000 P. Giot, J. Grammig EVS portfolios δ0 3.9E−05 (3.1E−05) 0.0E+00 (3.1E−05) 0.0E+00 (3.0E−05) 1.0E−06 (3.2E−05) δ1 0.015 (0.018) 0.020 (0.018) 0.041 (0.018) 0.108 (0.018) δ2 0.011 (0.018) 0.013 (0.018) 0.020 (0.018) 0.068 (0.018) δ3 0.015 (0.018) 0.040 (0.018) 0.055 (0.018) 0.090 (0.018) ω 0.054 (0.027) 0.061 (0.032) 0.072 (0.032) 0.104 (0.038) α1 0.051 (0.015) 0.049 (0.015) 0.050 (0.014) 0.055 (0.014) β1 0.868 (0.050) 0.863 (0.055) 0.850 (0.052) 0.807 (0.057) ν 9.360 (1.437) 9.996 (1.625) 10.567 (1.784) 11.346 (1.995) Q 0.06 [1.26] 0.06 [3.35] 0.18 [17.76] 1.05 [154.91] Q 2 DCX 4.60 [23.81] 2.49 [14.32] 0.69 [24.58] 0.17 [188.48] δ0 3.0E−06 (3.7E−05) −2.0E−06 (3.6E−05) −1.0E-06 (3.6E−05) −2.0E−06 (4.0E−05) δ1 0.025 (0.018) 0.033 (0.018) 0.082 (0.018) 0.189 (0.018) δ2 0.001 (0.018) 0.004 (0.018) 0.020 (0.018) 0.069 (0.018) δ3 0.011 (0.018) 0.024 (0.018) 0.036 (0.018) 0.079 (0.018) ω 0.114 (0.036) 0.118 (0.051) 0.164 (0.049) 0.126 (0.048) α1 0.105 (0.021) 0.089 (0.023) 0.098 (0.020) 0.081 (0.019) β1 0.667 (0.079) 0.694 (0.104) 0.628 (0.089) 0.718 (0.085) ν 5.249 (0.507) 5.666 (0.577) 6.312 (0.683) 7.230 (0.861) Q 0.02 [2.96] 0.06 [5.65] 0.03 [34.57] 0.34 [258.85] Q 2 DTE 1.56 [58.49] 1.25 [42.07] 3.49 [47.48] 2.78 [228.73] δ0 7.6E-05 (4.9E−05) 2.0E-06 (4.9E−05) 2.0E-06 (4.9E−05) 2.0E-06 (5.2E−05) δ1 0.013 (0.018) 0.033 (0.018) 0.062 (0.018) 0.121 (0.018) δ2 0.030 (0.018) 0.029 (0.018) 0.034 (0.018) 0.067 (0.018) δ3 0.005 (0.018) 0.015 (0.018) 0.038 (0.018) 0.070 (0.018) ω 0.052 (0.021) 0.040 (0.021) 0.056 (0.022) 0.082 (0.027) α1 0.055 (0.013) 0.041 (0.013) 0.057 (0.014) 0.075 (0.015) β1 0.841 (0.046) 0.883 (0.045) 0.843 (0.046) 0.789 (0.051) ν 5.657 (0.578) 6.009 (0.657) 6.704 (0.784) 7.497 (0.917) Q 0.19 [4.36] 0.26 [11.94] 0.46 [32.41] 0.27 [132.27] Q 2 SAP 12.42 [82.20] 12.55 [67.59] 6.52 [74.93] 4.55 [176.03] δ0 7.3E−05 (4.7E−05) 1.0E−06 (4.7E−05) 2.0E−06 (4.8E−05) 4.0E−06 (5.1E−05) δ1 0.017 (0.018) 0.050 (0.018) 0.109 (0.018) 0.204 (0.018) δ2 −0.001 (0.018) 0.009 (0.018) 0.028 (0.018) 0.074 (0.018) δ3 −0.011 (0.018) 0.008 (0.018) 0.038 (0.018) 0.071 (0.018) ω 0.099 (0.028) 0.086 (0.029) 0.073 (0.027) 0.125 (0.047) α1 0.089 (0.016) 0.067 (0.014) 0.065 (0.014) 0.093 (0.020) β1 0.673 (0.067) 0.752 (0.063) 0.792 (0.057) 0.685 (0.089)
How large is liquidity risk in an automated auction market? 125 Table 2 (continued) Frictionless v=5,000 v=20,000 v=40,000 ν 4.123 (0.338) 4.748 (0.421) 5.486 (0.543) 6.029 (0.634) Q 0.14 [0.10] 0.15 [8.44] 0.07 [62.64] 2.72 [310.22] Q 2 3.80 [73.24] 7.10 [66.27] 6.26 [129.33] 4.66 [505.71] Section 3 provides details of the estimation procedure. The first column gives estimation results using frictionless returns. The other columns report estimation results based on actual returns. Parameter standard errors are given in parentheses. The Q-rows report the Ljung–Box Q-statistic computed on the AR residuals. For the computation of the Ljung–Box statistics the number of autocorrelations included is equal to 2, and observations of different trading days where excluded from the estimation of autocovariances. In brackets the Ljung–Box statistic of the raw return data is reported. The Q 2 rows reports the Ljung–Box statistic of the GARCH residuals. The figures in brackets are Ljung–Box statistic of squared raw returns is expected to incur a significant price impact, i.e. has to be ready to pay a considerable liquidity risk premium. The diversification effect smoothes the intraday variation, but the pronounced liquidity risk premium during the first half hours after the open cannot be diversified away. Figure 2 details this finding by displaying the intraday variation of the mean and volatility components of the Actual VaR, i.e. both components of the liquidity Fig. 1 Diurnal variation of the relative liquidity risk premium Λt for equal volume stock portfolios and individual stocks. Period 1 refers to the first half of the sample period, August, 2, 1999–September 17, 1999 when the Xetra continuous trading hours extended from 8.30 A.M. CET —5.00 P.M. CET. Period 2 refers to the second half of the sample period, September 20, 1999– October 29, 1999 when Xetra continuous trading hours extended from 9.00 A.M. CET—5.30 P.M. CET. The numbers have been multiplied by 100 to represent percentages. α=0.05
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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
Page 80 and 81: Table 5 Impulse-response functions
Page 82 and 83: 76 We also show that NYSE buyer-ini
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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
Page 129: 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
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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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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