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162 5.2.5 The impact of the bid-ask spread In this instance we find a clear confirmation of Hypothesis (4). Traders’ preference for aggressive market trading significantly decreases when the bid-ask spread rises. Conversely, the aggressiveness of limit order trading increases. Hence, the higher the bid-ask spread, the lower traders’ incentive to cross the market and to post a market order on the opposite side. In this case, market agents are willing to bear execution risk by posting limit orders. Interestingly, we find weak evidence for the fact that a widening of the bid-ask spread also leads to a decline of the cancellation intensity on both sides of the market. Hence, the decrease of the risk of nonexecution induced by higher spreads reduces traders’ willingness to cancel pending orders. 5.3 Summarizing the results A. D. Hall, N. Hautsch Overall, we find clear evidence that the arrival rate of aggressive market orders, limit orders, and cancellations is affected by the state of the order book and that the inclusion of order book variables significantly increases the goodness-of-fit of the model. Regarding the impact of changes in the market depth and the cumulated pending volume on order and cancellation aggressiveness, we find clear support for the “crowding-out” concept of Parlour (1998). These results are in line with previous empirical studies such as Griffiths et al. (2000), Coppejans and Domowitz (2002), Pascual and Veredas (2004) and Ranaldo (2004). In this context, we also observe relationships which support the notion that the limit order book has information value, i.e. that traders infer from the book with respect to future price movements. However, Parlour’s model has no explanatory power for the reaction of order aggressiveness after the occurrence of mid-quote movements in the recent past. In this context, liquidity effects seem to prevail. Furthermore, implications of the Foucault (1999) model regarding the impact of volatility and the size of the bid-ask spread on traders’ order aggressiveness are only partly confirmed. Whereas we find a significant crowding out of market order aggressiveness towards limit order aggressiveness after a widening of the spread (as theoretically predicted), we do not observe corresponding effects in response to changes in the volatility. These peculiarities are not suggested by existing equilibrium models. Nevertheless, our findings suggest that a separate modelling of the single processes in a multivariate setting is a valuable strategy providing a clear-cut picture of how the particular processes are individually affected by the state of the order book. Obviously, limit orders cannot necessarily be treated as less aggressive versions of market orders since they respond in a different way to certain order book variables. In this sense we confirm the results of Coppejans and Domowitz (2002). It is also demonstrated that the order book effects remain remarkably stable irrespective of whether order book dynamics are taken into account or not. While this finding illustrates the robustness of the results, it also implies that the economic relations hold conditionally on the history of the individual processes as well as unconditionally.
Order aggressiveness and order book dynamics 163 6 Conclusions We analyze the impact of order book information of traders’ order aggressiveness in the electronic trading on the Australian Stock Exchange. The novel feature of the paper is to analyze this issue using a multivariate dynamic intensity framework. Therefore, order aggressiveness in market trading, limit order trading as well as in order cancellations on both sides of the market is modelled on the basis of a sixdimensional version of the autoregressive conditional intensity (ACI) model proposed by Russell (1999). The multivariate intensity function gives the instantaneous order arrival probability per time in each instant and for each order process. Therefore, it has a natural interpretation as a (continuous-time) measure for traders’ degree of aggressiveness in the individual dimensions. In this sense, our setting merges approaches where order aggressiveness is modelled in terms of a categorized variable on the basis of the order classification scheme proposed by Biais et al. (1995) (see, for instance, Griffiths et al. 2000, or Ranaldo 2004), and, those approaches which model the intensity of aggressiveness using univariate (ACD-type) dynamic duration models (see e.g. Coppejans and Domowitz 2002, or Pascual and Veredas 2004). A novel feature of this study is to determine order aggressiveness not only based on the type of the order and the corresponding position in the book but also by the posted volume. Hence, we explicitly focus on market and limit orders with volumes which are significantly above the average. Correspondingly, we also classify cancellations by modelling only those with high orders. This strategy allows us to concentrate on the economically most relevant orders and to reduce the impact of noise. The usefulness of the individual modelling of the single order processes in a multivariate setting is confirmed by the finding that the intensities of market trading, limit order trading, and cancellations have different responses in their dependence on order book variables. This result questions the application of (too simplified) order classification schemes and supports the use of sequential classifications by distinctly distinguishing between market orders, limit orders and cancellations as implemented by Pascual and Veredas (2004). Our results show that order book information has significant explanatory power in explaining traders’ degree of aggressiveness. In particular we find that the inclusion of variables capturing the current state of the order book as well as recent changes in the book improves the model’s goodness-of-fit considerably. Analyzing the influence of fundamental market characteristics such as the depth, queued volume, the bid-ask spread, recent movements in the order flow and in the price as well as the recent price volatility during the last trading minutes, we broadly confirm economic theory. Particularly with respect to market depth, clear evidence is provided for “crowding out effects” (cf. Parlour 1998). Depth on one particular side induces a crowding out of aggressive market and limit order trading on that side towards the other side of the market. In addition to crowding out mechanisms we also find evidence for liquidity and volatility effects which are not in line with existing theoretical equilibrium models. These results indicate that traders’ order aggressiveness is not only driven by expected execution probabilities but also by price information revealed by the book as well as liquidity considerations. Our results provide clear evidence that the timing of aggressive market orders, limit orders as well as cancellations is influenced by the state of the order book which is consistent with the findings of Coppejans and Domowitz (2002), but in
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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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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
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Page 154 and 155: Table 4 Fully specified ACI models
Page 156 and 157: Table 4 (continued) BHP NAB NCP TLS
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Page 175 and 176: Modelling financial transaction pri
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Page 212 and 213: 206 price 0.8565 0.8575 0.8585 0.85
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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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