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160 specification which includes order book information has a significantly higher explanatory power than a pure dynamic model without covariates. Nevertheless, as indicated by the BIC values in Table 4, in four of five cases the inclusion of dynamics on top of order book variables leads to a further increase of the BIC. Summarizing these findings, we conclude that the state of the order book plays a particularly important role in explaining the degree of order aggressiveness in the individual processes. Nonetheless, in addition order book dynamics have to be taken into account in order to obtain a well-specified model. 5.2 Economic results A particular important finding is that for most of our order book covariates, we find a remarkable robustness over the cross-section of stocks with no systematic differences between the individual stocks. Regarding our economic hypotheses, we can summarize the following findings. 5.2.1 The impact of market depth Our estimation results show a clear confirmation of Hypothesis (1). In fact, an increase of the depth on the ask side (AD) increases the aggressiveness in sell market order trading, decreases it in sell limit order trading and increases it in ask cancellations. The converse is true for the depth on the bid side (BD) leading to a rising intensity of aggressive buys as well as bid cancellations and a declining intensity for aggressive buy limit orders. This finding clearly confirms the crowding out concept as discussed in Parlour (1998). Furthermore, we also find a significantly negative relation between the depth on a certain side of the market and traders’ preference to post aggressive market orders on that side. Hence, trader’s preference for aggressive buys (sells) increases when the ask (bid) depth declines. This finding cannot be solely explained by a pure crowding out effect but supports the idea that traders use information from the book to infer price expectations. As discussed in Hall and Hautsch (2004), greater ask (bid) depth indicates that a relatively higher proportion of volume is to be sold (bought) at a comparably low (high) price. This induces a negative (positive) price signal which increases traders’ preference to post sell (buy) market orders. Interestingly, we do not observe a clearcut and significant impact of changes in the depth on the limit order and cancellation activity on the opposite side of the market. 5.2.2 The impact of the conditional cumulated volume A. D. Hall, N. Hautsch The variables AD and BD measure market depth by the log volume-price ratio associated with the 5% volume quantile. In addition, the cumulated (log) ask and bid volume (AV and BV) control for the amount of volume pending in the queue. Hence, the regressors associated with AV and BV reflect the impact of changes in the cumulated volume given the corresponding market depth (as measured by AD and BD). We find a positive impact of the cumulated ask (bid) volume on the intensity of aggressive sell (buy) limit orders. Since we condition on the volume-
Order aggressiveness and order book dynamics 161 price ratio, an increase of the volume must also imply a higher price impact leading to a higher dispersion of limit prices and thus an increase of the execution probability on that side of the market. Therefore, this result is consistent with the theoretical predictions of the Parlour (1998) model and Hypothesis (1). However, we also find slight evidence for a positive impact on market order aggressiveness and a negative impact on the limit order aggressiveness at the same side of the market. Hence, obviously a higher ask (bid) volume accompanied with a higher dispersion of limit prices indicates that market participants expect increasing (decreasing) prices leading to a rise (decline) in the preference for immediacy. Overall, we observe a significant reduction of the order submission and cancellation activity on the opposite side of the market. Concerning the influence of changes in the cumulated volume during the recent past we find that an increase in the cumulated volume on one market side during the past 5 min decreases the intensity of market and limit orders on both sides of the market, and simultaneously increases the cancellation intensity on the same side. Hence, after periods in which substantial (one-sided) volume has been accumulated in the queues, we observe a type of mean reversion effect causing a reduction of the overall order flow and an increase in the tendency to remove pending orders. 5.2.3 The impact of recent mid-quote movements Mid-quote movements during the past 5 min have a significant impact on traders’ preference to post aggressive market and limit orders. Interestingly, we find a clear rejection of Hypothesis (2). In particular, positive price movements decrease (increase) traders’ overall activity on the ask (sell) side. This finding is not explained by crowding out effects or the informational content of the book. A possible reason for this effect could be that, after significant price movements, traders’ order submission strategies are dominated by liquidity considerations. This might be explained by the fact that a movement of the mid-quote accompanied by the absorption of a substantial part of the pending volume on one side leads to an increase of the costs of aggressive trading on that side. Then, traders obviously become reluctant to post further aggressive orders on that market side. Moreover, it also turns out that traders significantly reduce the cancellation intensity. 5.2.4 The impact of past volatility No clear-cut confirmation of Hypothesis (3) is found. We observe an increase of the overall order submission and cancellation activity in periods of a higher mid-quote volatility. These results are highly significant and consistent over all stocks. These results contradict the implications of Foucault (1999) since the notion of a crowding out of market trading towards limit order trading is clearly rejected. Rather, we find evidence that a higher volatility is accompanied by a higher limit order book activity. A possible reason for this finding could be the well known positive relation between volatility and order volumes. Since we explicitly focus on large orders, our results could be driven by the fact that a higher volatility increases the overall arrival rate of higher order volumes.
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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
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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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 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
Page 203: Modelling financial transaction pri
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
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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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