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82 A. Escribano and R. Pascual Madhavan A, Richardson M, Roomans M (1997) Why do security prices change? A transactionlevel analysis of NYSE stocks. Rev Financ Stud 10:1035–1064 Odders-White ER (2000) On the occurrence and consequences of inaccurate trade classification. J Financ Mark 3:259–286 O’Hara M (1995) Market microstructure theory. Blackwell, Cambridge Park JY, Phillips PCB (2000) Nonstationary binary choice. Econometrica 68:1249–1280 Pascual R, Pascual-Fuster B, Climent F (2005) Cross-listing, price discovery, and the informativeness of the trading process. J Financ Mark, forthcoming Roll R (1984) A simple implicit measure of the effective bid–ask spread in a efficient market. J Finance 39:1127–1139 Stock J, Watson M (1988) Testing for common trends. J Am Stat Assoc 83:1097–1107 Stoll HR (1989) Inferring the components of the bid–ask spread: theory and empirical tests. J Finance 19:115–134 Subrahmanyam A (1997) Multi-market trading and the informativeness of stock trades: an empirical intraday analysis. J Econ Bus 49:515–531 Tse Y (2000) Further examination of price discovery on the NYSE and regional exchanges. J Financ Res 23:331–351 White H (1994) Estimation, inference and specification analysis. Cambridge University Press, Cambridge Wooldridge JM (1994) Estimation and inference for dependent processes. In: Engle RF, McFadden DL (eds) Handbook of econometrics, vol IV. Elsevier, North Holland, Amsterdam Zellner A (1962) An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. J Am Stat Assoc 57:348–368 Zhang MY, Russell JR, Tsay RS (2005) Determinants of bid and ask quotes and implications for the cost of trading, Working Paper, University of Chicago
Stefan Frey . Joachim Grammig Liquidity supply and adverse selection in a pure limit order book market Abstract This paper analyzes adverse selection costs and liquidity supply in a pure open limit order book market. We relax assumptions of the Glosten/Såndas modeling framework regarding marginal zero profit order book equilibrium and the parametric market order size distribution. We show that using average zero profit conditions considerably increases the empirical performance while a nonparametric specification for market order size combined with marginal zero profit conditions does not. A cross sectional analysis corroborates the finding that adverse selection costs are more severe for smaller capitalized stocks. We also find additional support for one of the central hypothesis put forth by the theory of limit order book markets, which states that liquidity supply and adverse selection costs are inversely related. Furthermore, adverse selection cost estimates based on our structural model and those obtained using popular model-free methods are strongly correlated. This indicates the robustness of the theory-based approach. Keywords Limit order book market . Liquidity supply . Adverse selection JEL Classification G10 . C32 Stefan Frey is also doctoral student at the Graduiertenkolleg “Unternehmensentwicklung, Marktprozesse und Regulierung in dynamischen Entscheidungsmodellen”. Joachim Grammig is also research fellow at the Centre for Financial Research (CFR), Cologne. Earlier drafts of the paper were presented at the German Finance Association’s 2004 annual meeting, and seminars at the Universities Cologne, Konstanz, Mannheim and Tübingen, and the 2005 meeting of the Verein fuer Socialpolitik Econometrics Section as well as the 2005 MicFin Workshop in Madrid. S. Frey (*) . J. Grammig Faculty of Economics, Department of Statistics, Econometrics and Empirical Economics, University of Tübingen, Mohlstr. 36, 72074 Tübingen, Germany E-mail: stefan.frey@uni-tuebingen.de E-mail: joachim.grammig@uni-tuebingen.de
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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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Page 48 and 49: 42 K. Bien et al. deviation 0.0099,
Page 50 and 51: 44 K. Bien et al. - Set ˆzt = Âxt
Page 52 and 53: 46 K. Bien et al. % Frequency −0.
Page 54 and 55: 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
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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
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Page 78 and 79: 72 revert towards narrow levels. As
Page 80 and 81: Table 5 Impulse-response functions
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Page 86 and 87: 80 NYSE 2000 Stocks AOL America Onl
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
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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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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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