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108 In the main text we discuss the implications of the situation that the average profit equals zero. This implies that pþk X qþk Qþk þ qþk 2 ¼ 0: (23) Reordering Eq. (23) and replacing Q+k by Q+k−1+qk yields the average profit conditions Eq. (10) from which average break even and update conditions can be derived as described in the main text. References S. Frey, J. Grammig Ahn H-J, Bae K-H, Chan K (2001) Limit orders, depth, and volatility: evidence from the stock exchange of Hong Kong. J Finance 54:767–788 Andrews DWK (1999) Consistent moment selection procedures for generalized method of moments estimation. Econometrica 67:543–564 Bauwens L, Giot P (2001) Econometric modelling of stock market intraday activity (Kluwer) Beltran H, Giot P, Grammig J (2004) Commonalities in the order book. Mimeo, CORE, Universities of Louvain, Namur, and Tübingen Biais B, Hillion P, Spatt C (1995) An empirical analysis of the limit order book and the order flow in the Paris Bourse. J Finance 1655–1689 Biais B, Martimort D, Rochet J-C (2000) Competing mechanisms in a common value environment. Econometrica 68:799–837 Bloomfield R, O’Hara M, Saar G (2005) The make or take decision in an electronic market: evidence on the evolution of liquidity. J Financ Econ 75:165–199 Boehmer E (2004) Dimensions of execution quality: recent evidence for U.S. equity markets. EFA 2004 Working Paper Texas A&M University Cao C, Hansch O, Wang X (2004) The informational content of an open limit order book. EFA 2004 Working Paper, Penn State University and Southern Illinois University Cheung YC, de Jong F, Rindi B (2003) Trading European sovereign bonds: the microstructure of the mts trading platforms, Discussion paper EFA 2003 Annual Conference Paper, www.ssrn.com Coppejans M, Domowitz I, Madhavan A (2003) Dynamics of liquidity in an electronic limit order book market, Discussion paper Department of Economics, Duke University Degryse H, de Jong F, Ravenswaaij M, Wuyts G (2003) Aggressive orders and the resiliency of a limit order market, Working Paper, K.U. Leuven, Department of Economics DeJong F, Nijman T, Roëll A (1996) Price effects of trading and components of the bid-ask spread on the Paris Bourse. J Empir Finance 193–213 Dufour A, Engle RF (2000) Time and the price impact of a trade. J Finance 55(6):2467–2499 Easley D, Kiefer NM, O’Hara M, Paperman J (1996) Liquidity, information, and less-frequently traded stocks. J Finance 51:1405–1436 Fernandes M, Grammig J (2005) Non-parametric specification tests for conditional duration models. J Econom 127:35–68 Foucault T (1999) Order flow composition and trading costs in a dynamic limit order market. J Financ Mark 2:99–134 Foucault T, Kadan O, Kandel E (2003) Limit order book as a market for liquidity, Mimeo, HEC Glosten L (1994) Is the electronic limit order book inevitable? J Finance 49:1127–1161 Glosten LR, Harris LE (1988) Estimating the components of the bid-ask spread. J Financ Econ 21:123–142 Gomber P, Schweickert U, Theissen E (2004) Zooming in on liquidity, EFA 2004 Working Paper, University of Bonn Grammig J, Heinen A, Reginfo E (2004) Trading activity and liquidity supply in a pure limit order book market, CORE Discussion Paper, 58 Université Catholique de Louvain Hall A, Peixe F (2003) A consistent method for the selection of relevant instruments. Econ Rev 22:269–287 Hall A, Hautsch N, Mcculloch J (2003) Estimating the intensity of buy and sell arrivals in a limit order book market, Mimeo, University of Technology, Sidney and University of Konstanz
Liquidity supply and adverse selection in a pure limit order book market 109 Hansen LP (1982) Large sample properties of generalized method of moments estimators. Econometrica 50:1029–1054 Hasbrouck J (1991) Measuring the information content of stock trades. J Finance 46:179–207 Hasbrouck J (2004) Lecture Notes for PhD Seminar in Empirical Market Microstructure, Stern School of Business, NYU Huang R, Stoll H (1996) Dealer versus auction markets: a paired comparison of execution costs on NASDAQ and the NYSE. J Financ Econ 41:313–357 Kaniel R, Liu H (2001) So what orders do informed traders use? Discussion paper University of Texas at Austin O’Hara M (1995) Market Microstructure Theory (Blackwell, Malden, MA) Parlour C (1998) Price dynamics in limit order markets. R Financ Stud 11:789–816 Pascual R, Veredas D (2004) What pieces of limit order book information are informative? An empirical analysis of a pure order-driven market, Mimeo. Dept. of Business. Universidad de las Islas Baleares Ranaldo A (2004) Order aggressiveness in limit order book markets. J Financ Mark 7(1):53–74 Såndas P (2001) Adverse selection and competitive market making: empirical evidence from a limit order market. Rev Financ Stud 14:705–734 SEC (2001) Report on the comparison of order executions across equity market structures, Office of Economic Analysis United States Securities and Exchange Commission Working Paper Seppi D (1997) Liquidity provision with limit orders and a strategic specialist. Rev Financ Stud 10:103–150
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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
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 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 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
Page 156 and 157: Table 4 (continued) BHP NAB NCP TLS
Page 158 and 159: Table 5 ACI models without dynamics
Page 160 and 161: Table 5 (continued) BHP NAB NCP TLS
Page 162 and 163: Table 6 ACI models without covariat
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Table 6 (continued) BHP NAB NCP TLS
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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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