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164 contrast to those of Pascual and Veredas (2004). A possible explanation for these conflicting results is that Pascual and Veredas (2004) apply a discrete-time duration model which does not allow for time-varying covariates. However, particularly for the processes of the infrequent highly aggressive orders, it seems to be essential to account for changes of the order book during a spell. 16 Clear evidence for the existence of multivariate dynamic structures in the order arrival processes is found. We observe significant spill-over effects between the both sides of the market and—in a weaker form—between market trading and limit order trading. The fact that these interdependencies are primarily (significantly) positive suggests that order book dynamics are driven by general market activity which simultaneously influences all individual processes rather than by economic “crowding out” arguments which would imply negative spill-over effects. These findings support the notion that the arrival rates of aggressive orders are basically driven by two pieces of information: (1) the state of the market as revealed by the open limit order book and which directs traders’ order submission strategy, and (2) general market activity which simultaneously influences the individual arrival rates. 17 Our findings show that order book information plays the dominant role in explaining order aggressiveness. In particular, we observe that in terms of its explanatory power, a model which excludes all dynamics but includes order book covariates significantly outperforms a completely dynamic model that does not account for the state of the market. Nevertheless, the dynamic variables are absolutely necessary in order to obtain a well-specified model. These findings provide support for advocates of greater transparency in electronic trading and indicate that real benefits to traders may result from complete disclosure of the order book. Acknowledgement Special thanks are due to James McCulloch whose assistance in preparing the data has made this research project feasible. References A. D. Hall, N. Hautsch Al-Suhaibani M, Kryzanowski L (2000) An exploratory analysis of the order book, and order flow and execution on the Saudi stock market. J Bank Financ 24:1323–1357 Bauwens L, Giot P (2000) The logarithmic ACD model: an application to the Bid/Ask Quote Process of two NYSE stocks. Ann Econ Stat 60:117–149 Bauwens L, Hautsch N (2003) Dynamic Latent Factor Models for Intensity Processes, Discussion Paper 2003/103, CORE, Université Catholique de Louvain Bauwens L, Veredas D (2004) The stochastic conditional duration model: a latent factor model for the analysis of financial durations. J Econom 119:381–412 Biais B, Hillion P, Spatt C (1995) An empirical analysis of the limit order book and the order flow in the Paris bourse. J Financ 50:1655–1689 Bisière C, Kamionka T (2000) Timing of orders, orders aggressiveness and the order book at the Paris bourse. Ann Econ Stat 60:43–72 Bowsher CG (2002) Modelling Security Markets in Continuous Time: Intensity based, Multivariate Point Process Models, Discussion Paper 2002-W22, Nuffield College, Oxford 16 In our setting, an updating of the information set occurs whenever a new point of the pooled process arrives. A further extension would be to account for any changes of the order book. However this would considerably increase the computational burden in our multivariate setting. 17 This result supports the idea of Bauwens and Hautsch (2003) to model the underlying market activity in terms of a latent autoregressive component which simultaneously affects all individual intensity processes.
Order aggressiveness and order book dynamics 165 Brémaud P (1981) Point processes and queues, martingale dynamics. Springer, Berlin Heidelberg New York Cao C, Hansch O, Wang X (2003) The Informational Content of an Open Limit Order Book, Discussion paper, Pennsylvania State University Cohen KJ, Maier SF, Schwartz RA, Whitcomb DK (1981) Transaction costs, order placement strategy, and existence of the bid-ask spread. J Polit Econ 89(2):287–305 Coppejans M, Domowitz I (2002) An Empirical Analysis of Trades, Orders, and Cancellations in a Limit Order Market, Discussion paper, Duke University Dufour A, Engle RF (2000) The ACD Model: Predictability of the Time between Consecutive Trades, Discussion paper, ISMA Centre, University of Reading Engle RF, Russell JR (1998) Autoregressive conditional duration: a new model for irregularly spaced transaction data. Econometrica 66:1127–1162 Fernandes M, Grammig J (2001) A Family of Autoregressive Conditional Duration Models, Discussion Paper 2001/31, CORE, Université Catholique de Louvain Foucault T (1999) Order flow composition and trading costs in a dynamic limit order market. J Financ Mark 2:99–134 Glosten LR (1994) Is the electronic open limit order book inevitable. J Financ 49:1127–1161 Grammig J, Maurer K-O (2000) Non-monotonic hazard functions and the autoregressive conditional duration model. Econometrics J 3:16–38 Griffiths MD, Smith BF, Turnbull DAS, White RW (2000) The costs and determinants of order aggressiveness. J Financ Econ 56:65–88 Hall AD, Hautsch N (2004) A continuous-time measurement of the buy-sell pressure in a limit order book market, Discussion Paper 04-07, Institute of Economics, University of Copenhagen Handa P, Schwartz RA (1996) Limit order trading. J Financ 51:1835–1861 Handa P, Schwartz RA, Tiwari A (2003) Quote setting and price formation in an order driven market. J Financ Mark 6:461–489 Harris L (1998) Optimal dynamic order submission strategies in some stylized trading problems. Financ Mark Inst Instrum 7:1–75 Harris L, Hasbrouck J (1996) Market vs. limit orders: The SuperDOT evidence on order submission strategy. J Financ Quant Anal 31(2):213–231 Hautsch N (2004) Modelling irregularly spaced financial data—theory and practice of dynamic duration models, vol. 539 of Lecture Notes in Economics and Mathematical Systems. Springer, Berlin Heidelberg New York Hollifield B, Miller RA, Sandås P, Slive J (2002) Liquidity supply and demand in limit order markets, Discussion paper, Centre for Economic Policy Research, London Lo I, Sapp SG (2003) Order submission: the choice between limit and market orders, Discussion paper, University of Waikato, University of Western Ontario Lunde A (2000) A generalized gamma autoregressive conditional duration model, Discussion paper, Aarlborg University Parlour CA (1998) Price dynamics and limit order markets. Rev Financ Stud 11:789–816 Pascual R, Veredas D (2004) What Pieces of Limit Order Book Information are Informative? Discussion Paper 2004/33, CORE, Université Catholique de Louvain Ranaldo A (2004) Order aggressiveness in limit order book markets. J Financ Mark 7:53–74 Russell JR (1999) Econometric modeling of multivariate irregularly-spaced high-frequency data, Discussion paper, University of Chicago Seppi DJ (1997) Liquidity provision with limit orders and strategic specialist. Rev Financ Stud 1 (1):103–150 Zhang MY, Russell JR, Tsay RS (2001) A nonlinear autoregressive conditional duration model with applications to financial transaction data. J Econom 104:179–207
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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
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80 NYSE 2000 Stocks AOL America Onl
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82 A. Escribano and R. Pascual Madh
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84 1 Introduction S. Frey, J. Gramm
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86 develops the empirical methodolo
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Table 1 Sample descriptives Company
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90 comparability across stocks, we
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92 subtracting the deviations from
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94 market order distribution that c
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96 Table 2 First stage GMM results
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Table 4 First stage GMM results bas
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100 S. Frey, J. Grammig quotes on e
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102 S. Frey, J. Grammig approximate
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104 To obtain the estimates in the
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106 Hence, using the liquidity stat
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108 In the main text we discuss the
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Pierre Giot . Joachim Grammig How l
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Page 140 and 141: 134 A. D. Hall, N. Hautsch In this
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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
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Page 152 and 153: 146 Table 3 Descriptive statistics
Page 154 and 155: Table 4 Fully specified ACI models
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Page 168 and 169: 162 5.2.5 The impact of the bid-ask
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Page 206 and 207: 200 W. B. Omrane, H. V. Oppens anal
Page 208 and 209: 202 W. B. Omrane, H. V. Oppens of s
Page 210 and 211: 204 The extrema detection method ba
Page 212 and 213: 206 price 0.8565 0.8575 0.8585 0.85
Page 214 and 215: 208 We distinguish three possible c
Page 216 and 217: 210 originates. The most active tra
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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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