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80 NYSE 2000 Stocks AOL America Online Inc 626,768 C Citigroup Inc 260,149 EMC EMC Corporation 282,196 GE General Electric Co 350,795 IBM Int Business Machines Corp 234,766 LU Lucent Technologies Inc 705,948 MOT Motorola Inc 195,067 NOK Nokia Corp 221,005 NT Nortel Networks Corp 239,094 PFE Pfizer Inc 233,658 T ATT Corp 236,662 SSE 2000 Stocks AMS Amadeus Global Travel Distribution 49,824 BBVA Banco Bilbao-Vizcaya Argentaria 123,687 ELE Endesa 80,844 IBE Iberdrola 35,811 REP Repsol YPF 90,213 SCH Banco Santander Central Hispano 196,880 TEF Telefónica 424,327 TPI Telefónica Publicidad e Información 52,253 TPZ Telepizza 36,880 TRR Terra Networks 146,285 ZEL Zeltia 88,339 References A. Escribano and R. Pascual Arranz MA, Escribano A (2000) Cointegration testing under permanent breaks: a robust extended error correction model. Oxf Bull Econ Stat 62:23–52 Barclay MJ, Warner JB (1993) Stealth trading and volatility, which trades move prices? J Financ Econ 34:281–305 Bessembinder H, Kaufman HM (1997) A comparison of trade execution costs for NYSE and Nasdaq-listed stocks. J Financ Econ 46:293–319 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 50:1655–1689 Blume ME, Goldstein MA (1997) Quotes, order flow, and price discovery. J Finance 52:221–244 Bollerslev T, Melvin M (1994) Bid–ask spreads and volatility in the foreign exchange market. An empirical analysis. J Int Econ 36:355–372 Chan LKC, Lakonishok J (1993) Institutional trades and intraday stock price behavior. J Financ Econ 33:173–199 Chan LKC, Lakonishok J (1995) The behavior of stock prices around institutional trades. J Finance 50(4):1147–1174 Chung KH, Van Ness BF, Van Ness RA (1999) Limit orders and the bid–ask spread. J Financ Econ 53:255–287 Davidson R, MacKinnon JG (1993) Estimation and Inference in Econometrics. Oxford University Press Dufour A, Engle RF (2000) Time and the price impact of a trade. J Finance 55(6):2467–2498 Easley D, O’Hara M (1987) Price, trade size, and information in securities markets. J Financ Econ 19:69–90 Easley D, O’Hara M (1992) Time and the process of security price adjustment. J Finance 47 (2):577–605 Easley D, Kiefer NM, O’Hara M (1997) One day in the life of a very common stock. Rev Financ Stud 10:805–835 Ellis K, Michaely R, O’Hara M (2000) The accuracy of trade classification rules: evidence from Nasdaq. J Financ Quant Anal 35:529–551 Engle R, Granger C (1987) Co-integration and error correction: representation, estimation and testing. Econometrica 35:251–276
Asymmetries in bid and ask responses to innovations in the trading process 81 Engle RF, Patton AJ (2004) Impacts of trades in a error-correction model of quote prices. J Financ Mark 7:1–25 Engle RF, Yoo BS (1991) Cointegrated economic time series: a survey with new results. In: Granger CWJ, Engle RF (eds) Long-run economic relations. Readings in cointegration, Oxford University Press, pp 237–266 Escribano A, Granger CWJ (1998) Investigating the relationships between gold and silver prices. J Forecast (17):81–107 Escribano A, Peña D (1994) Cointegration and common factors. J Time Ser Anal 15:577–586 Glosten LR (1987) Components of the bid–ask spread and the statistical properties of transaction prices. J Finance 42:1293–1307 Glosten LR, Harris LE (1988) Estimating the components of the bid/ask spread. J Financ Econ 21:123–142 Glosten LR, Milgrom PR (1985) Bid, ask and transaction prices in specialist market with heterogeneously informed traders. J Financ Econ 14:71–100 Goldstein MA, Kavajecz KA (2004) Trading strategies during circuit breakers and extreme market movements. J Financ Mark 7:301–333 Green WH (1997) Econometric analysis. Prentice-Hall, Upper Saddle River, NJ Griffiths MD, Smith BF, Alasdair D, Turnbull S, White RW (2000) The costs and determinants of order aggressiveness. J Financ Econ 56:65–88 Harris M, Raviv A (1993) Differences of opinion make a horse race. Rev Financ Stud 6:473–506 Harris FH, McInish TH, Shoesmith GL, Wood RA (1995) Co-integration, error correction, and price discovery on internationally linked security markets. J Financ Quant Anal 30:563–579 Hasbrouck J (1988) Trades, quotes and information. J Financ Econ 22:229–252 Hasbrouck J (1991) Measuring the information content of stock trades. J Finance 46:179–207 Hasbrouck J (1995) One security, many markets: determining the contributions to price discovery. J Finance 50(4):1175–1199 Hasbrouck J (1996) Modeling market microstructure time series. In: Maddala GS, Rao CR (eds) Handbook of statistics, vol 14. Statistical methods in finance. Elsevier, North-Holland, Amsterdam Hasbrouck J (1999a) Security bid/ask dynamics with discreteness and clustering: simple strategies for modeling and estimation. J Financ Mark 2:1–28 Hasbrouck J (1999b) The dynamics of discrete bid and ask quotes. J Finance 54(6):2109–2142 Hasbrouck J, Sofianos G, Sosebee D (1993) New York Stock Exchange systems and trading procedures, NYSE Working Paper #93–01 Hausman JA, Lo AW, MacKinlay AC (1992) An ordered probit analysis of transaction costs prices. J Financ Econ 31:319–379 Holthausen RW, Leftwich RW, Mayers D (1987) The effect of large block transactions on security prices. J Financ Econ 19:237–267 Huang RD, Stoll HR (1994) Market microstructure and stock return predictions. Rev Financ Stud 7(1):179–213 Huang RD, Stoll HR (1996) Dealer versus auction markets: a paired comparison of execution costs on NASDAQ and the NYSE. J Financ Econ 41:313–357 Huang RD, Stoll HR (1997) The components of the bid–ask spread: a general approach. Rev Financ Stud 10:995–1034 Jang H, Venkatesh PC (1991) Consistency between predicted and actual bid–ask quote-revisions. J Finance 46:433–446 Johansen S (1991) Estimation and hypothesis testing of co-integration vectors in Gaussian vector autoregressive models. Econometrica 59:1551–1580 Kavajecz KA (1999) A specialist’s quoted depth and the limit order book. J Finance 54:747–771 Keim DB, Madhavan A (1995) Anatomy of the trading process: empirical evidence on the behavior of institutional traders. J Financ Econ 37:371–398 Kempf A, Korn O (1999) Market depth and order size. J Financ Mark 2:29–48 Koski JL, Michaely R (2000) Prices, liquidity, and the information content of trades. Rev Financ Stud 13:659–696 Lakonishok J, Lee I (2001) Are insider trades informative? Rev Financ Stud 14(1):79–111 Lee CM, Ready MJ (1991) Inferring trade direction from intraday data. J Finance 46:733–746 Lin JC, Sanger GC, Booth GG (1995) Trade size and components of the bid–ask spread. Rev Financ Stud 8:1153–1183 Madhavan A, Sofianos G (1998) An empirical analysis of NYSE specialist trading. J Financ Econ 48:189–210
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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 38 and 39: 32 K. Bien et al. Although economet
Page 40 and 41: 34 K. Bien et al. 2.1 Copula functi
Page 42 and 43: 36 K. Bien et al. and x k t ≡ (xk
Page 44 and 45: 38 K. Bien et al. Fig. 3 Multivaria
Page 46 and 47: 40 K. Bien et al. Bivariate model s
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
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 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
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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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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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