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100 S. Frey, J. Grammig quotes on each market side are used for the construction of break even and update conditions. Table 3 reports the estimation results for a specification that does not rely on a parametric assumption on the distribution of the market order size when constructing the marginal break even and the update conditions as described in section 3.2.1. The results reported in Table 4 are obtained when using the average break even and update conditions (11) and (12) for GMM estimation, while maintaining the parametric assumption (2) about the trade size distribution. For each parametric specification, the moment condition of Eq. (6) is employed, too. The full set of eight update conditions is exploited. The estimates reported in Table 3 show that abandoning the parametric assumption concerning the market order size distribution improves the results only marginally. For four of the thirty stocks the model is not rejected at the 1% significance level. Hasbrouck’s (2004) conjecture that the distributional assumption might be responsible for the model’s empirical failure is therefore not supported. Generally, the estimates of the adverse selection components, transaction costs and drift parameters do not change dramatically compared to the baseline specification. The transaction cost estimates remain negative. Maintaining the distributional assumption, but using average break even conditions instead of marginal break even conditions, considerably improves the empirical performance. Table 4 shows that we have model non-rejection for 22 out of the 30 stocks at the 1% significance level. With a single exception the estimates of the marginal transaction cost parameter γ are positive for those stocks for which the model is not rejected at 1% significance level. The size of the implied transaction cost estimates are broadly comparable with the relative realized spreads figures reported in Table 1. For example, the estimation results imply that transaction costs account for 0.013% of the euro value of a median sized DaimlerChrsyler trade. This value is quite comparable with the average relative realized spread which amounts to 0.010% (see Table 1). Figure 1 shows graphically the improved empirical performance delivered by the revised methodology. The figure depicts means an medians of implied and observed ask side price schedules of four selected stocks. The results obtained from the baseline estimation which uses marginal moment conditions confirm the disturbing findings reported in Såndas’ (2001). The price schedules implied by the model estimates are below the observed price schedules at all relevant volumes. The economically implausible negative price discount at small volumes is caused by the negative transaction costs estimates. This suggests that the model is not only rejected on the grounds of statistical significance, but that fundamentally fails to explain the data. The model does a bad job even in describing the ‘average’ state of the order book. However, Fig. 1 shows that when working with average break even and update conditions the empirical performance of the model is considerably improved. Especially the median observed price schedules correspond closely to those implied by the model. 14 14 We have also estimated a specification that combines the nonparametric approach towards trades sizes and average moment conditions, but the results (not reported) are not improved compared to the parametric version. In this version, the model is not rejected for 16 out of 30 stocks. The following analysis therefore focuses on the parametric specification using average moment conditions.
Liquidity supply and adverse selection in a pure limit order book market 101 Fig. 1 Comparison of implied and observed price schedules (visible book). The figure depicts means and medians of implied and observed ask side price schedules of four selected stocks. In each figure the values on the horizontal axis show trade volumes (number of shares) up to the 0.9 quantile of the respective stock. The vertical axis show the per share price decrease that a sell trade of a given volume would incur if it were executed against the current book. The solid line depicts sample means and the short dashed lines sample medians computed by using all order book snapshots during the 3 month period. The bold long-dashed lines depict the mean slope implied by the estimation results reported in Table 4 (baseline model that uses marginal break even and update conditions). The dash-dot lines and the long-dashed lines are the mean and the median of the book slope as implied by the estimation results reported in Table 4 (revised specification which uses average break even and update restrictions). The stock in the left upper panel is DaimlerChrysler (DCX, from the largest trade volume quartile), the stock in the right upper panel is Bay. Hypo Vereinsbank (HVM, second volume quartile), the stock in the left lower panel is Altana (ALT, third volume quartile) and the stock in the right lower panel is Deutsche Boerse (DB1, fourth volume quartile) 4.2 Cross sectional analyses Encouraged by the improved empirical performance of the revised methodology, this section uses the estimation results reported in Table 4 to conduct a cross sectional analysis of liquidity supply and adverse selection costs in the Xetra limit order book market. To ensure comparability across stocks, we follow a suggestion by Hasbrouck (1991) and standardize the adverse selection component α by computing ¼ m ; (13) P where mis the average (non-signed), stock specific trade size expressed in number of shares. P is the sample average of the midquote of the respective stock. τ (times 100)
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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
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 74 and 75: Table 2 The base-line VEC model for
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 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 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
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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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