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130 Table 4 (continued) Frictionless v=5,000 v=20,000 v=40,000 Q Q 0.09 [1.52] 0.04 [2.05] 0.23 [3.53] 1.36 [6.93] 2 SAP 5.38 [5.42] 3.14 [2.30] 3.54 [1.66] 4.47 [1.57] δ0 2.2E−04 (1.4E−04) 4.0E−06 (1.4E−04) 4.0E−06 (1.4E−04) 5.0E−06 (1.4E−04) δ1 0.056 (0.031) 0.058 (0.031) 0.081 (0.031) 0.107 (0.031) ω 0.233 (0.133) 0.233 (0.123) 0.252 (0.116) 0.304 (0.163) α1 0.075 (0.034) 0.057 (0.028) 0.065 (0.029) 0.061 (0.031) β1 0.592 (0.200) 0.626 (0.175) 0.595 (0.161) 0.528 (0.223) ν 7.677 (1.771) 8.524 (2.157) 8.826 (2.324) 8.448 (2.134) Q Q 1.99 [3.39] 1.96 [5.32] 1.84 [10.47] 1.67 [20.75] 2 2.16 [11.23] 0.97 [8.94] 0.39 [10.17] 0.46 [15.58] See Table 2 for explanations. 1.1.2 Table 5 Decomposition of the unconditional relative liquidity risk premium at different VaR time horizons The table reports the three parts of ¼T 1P T t¼1 mb;t t ;v2 tðrmm;tÞþT 1PT t¼1 t ;v 1 tðrmb;tÞ t ;v 2 tðrmm;tÞ 1: Because of rounding errors, the sum of mean and volatility components might differ from the figures reported in Table 3. VaR horizon v=5,000 v=20,000 v=40,000 EVS portfolios 10-min 0.36+0.99−1 0.70+0.98−1 1.16+1.04−1 half-hour 0.19+0.98−1 0.38+0.96−1 0.64+0.97−1 DCX 10-min 0.29+1.01−1 0.62+1.03−1 1.08+1.13−1 half-hour 0.17+0.99−1 0.35+0.98−1 0.60+1.00−1 DTE 10-min 0.27+1.01−1 0.49+1.02−1 0.77+1.08−1 half-hour 0.12+0.98−1 0.25+0.98−1 0.40+0.99−1 SAP 10-min 0.28+1.02−1 0.47+1.03−1 0.73+1.10−1 half-hour 0.13+0.99−1 0.23+1.00−1 0.37+1.02−1 References P. Giot, J. Grammig Ahn H, Bae K, Chan K (2001) Limit orders, depth and volatility: evidence from the stock exchange of Hong Kong. J Finance 56:767–788 Andersen TG, Bollerslev T (1997) Intraday periodicity and volatility persistence in financial markets. J Empir Finance 4:115–158 Bangia A, Diebold FX, Schuermann T, Stroughair JD (1999) Modeling liquidity risk, with implications for traditional market risk measurement and management, The Wharton Financial Institutions Center WP 99-06
How large is liquidity risk in an automated auction market? 131 Bauwens L, Giot P (2001) Econometric modelling of stock market intraday activity. Kluwer Academic Publishers Beltran H, Durré A, Giot P (2005a) Volatility regimes and the provision of liquidity in order book markets, CORE Discussion Paper 2005/12 Beltran H, Giot P, Grammig J (2005b) Commonalities in the order book, CORE Discussion Paper 2005/11 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 Biais B, Hillion P, Spatt C (1999) Price discovery and learning during the preopening period in the Paris Bourse. J Polit Econ 107:1218–1248 Campbell JY, Lo AW, MacKinlay AC (1997) The econometrics of financial markets. Princeton University Press, Princeton Cao C, Hansch O, Wang X (2004) The information content of an open limit order book, Mimeo, available at http://ssrn.com/abstract=565324 Chanda A, Engle RF, Sokalska ME (2005) High frequency multiplicative component GARCH, Mimeo Chung KH, Van Ness BF, Van Ness RA (1999) Limit orders and the bid-ask spread. J Financ Econ 53:255–287 Coppejans M, Domowitz I, Madhavan A (2004) Resiliency in an automated auction, Working Paper, ITG Group Cushing D, Madhavan A (2000) Stock returns and trading at the close. J Financ Mark 3:45–67 Deutsche Börse AG (1999) Xetra Market Model Release 3 Stock Trading, Technical Report Domowitz I, Hansch O, Wang X (2005) Liquidity commonality and return comovement. Forthcoming in Journal of Financial Markets Dowd K (1998) Beyond Value-at-Risk. Wiley Engle RF (2000) The econometrics of ultra high frequency data. Econometrica 68:1–22 Engle RF, Russell J (1998) Autoregressive conditional duration; a new model for irregularly spaced transaction data. Econometrica 66:1127–1162 Foster F, Viswanathan S (1994) Strategic trading with asymmetrically informed investors and long-lived information. J Financ Quant Anal 29:499–518 Giot P (2000) Time transformations, intraday data and volatility models. J Comput Financ 4:31– 62 Giot P (2005) Market risk models for intraday data. Eur J Financ 11:309–324 Gomber P, Schweickert U, Deutsche Börse AG (2002) Der Market Impact: Liquiditätsmass im elektronischen Handel. Die Bank 7:185–189 Gouriéroux C, Le Fol G, Meyer B (1998) Analyse du carnet d'ordres. Banque et Marchés 36:5 –20 Gouriéroux C, Le Fol G, Jasiak J (1999) Intraday market activity. J Financ Mark 2:193–226 Grammig J, Schiereck D, Theissen E (2001) Knowing me, knowing you: trader anonymity and informed trading in parallel markets. J Financ Mark 4:385–412 Handa P, Schwartz RA (1996) Limit order trading. J Finance 51:1835–1861 Harris L (2002) Trading and exchanges. Oxford University Press Heidle HG, Huang RD (2002) Information-based trading in dealer and auction markets: an analysis of exchange listings. J Financ Quant Anal 37:391–424 Irvine P, Benston G, Kandel E (2000) Liquidity beyond the inside spread: measuring and using information in the limit order book, Mimeo, Goizueta Business School, Emory University, Atlanta Jorion P (2000) Value-at-Risk. McGraw-Hill Keim DB, Madhavan A (1997) Transaction costs and investment style: an interexchange analysis of institutional equity trades. J Financ Econ 46:265–292 Kyle AS (1985) Continuous auctions and insider trading. Econometrica 53:1315–1335 Madhavan A (1992) Trading mechanisms in securities markets. J Finance 47:607–642 Martens M, Chang y, Taylor SJ (2002) A comparison of seasonal adjustment methods when forecasting intraday volatility. J Financ Res 25:283–299 Pascual R, Veredas D (2004) What pieces of limit order book information are informative? An empirical analysis of a pure order-driven market, WP Stern Business School FIN-04-003 Saunders A (2000) Financial institutions management. McGraw-Hill Sofianos G, Werner IM (2000) The trades of NYSE floor brokers. J Financ Mark 3:139–176 Subramanian A, Jarrow RA (2001) The liquidity discount. Math Financ 11:447–474 Venkatamaran K (2001) Automated versus floor trading: an analysis of execution costs on the Paris and New York Stock Exchange. J Finance 56:1445–1485
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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
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Table 5 Impulse-response functions
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76 We also show that NYSE buyer-ini
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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 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 135: 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
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Page 175 and 176: Modelling financial transaction pri
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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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