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146 Table 3 Descriptive statistics of trade and limit order arrival processes at the ASX A. D. Hall, N. Hautsch BHP NAB NCP TLS WOW Num. Prop. Num. Prop. Num. Prop. Num. Prop. Num. Prop. Total number 147,552 107,595 252,009 97,804 59,519 Aggr. Buys, v ≥ 75% 1,946 0.013 2,657 0.025 2,385 0.009 522 0.005 838 0.014 Aggr. buys 5,998 0.041 8,057 0.075 7,700 0.031 1,761 0.018 3,359 0.056 Normal buys 28,349 0.192 14,303 0.133 21,402 0.085 19,142 0.196 10,125 0.170 Most aggr. asks, v ≥ 75% 1,511 0.010 1,763 0.016 2,058 0.008 241 0.002 795 0.013 Most aggr. asks 4,092 0.028 4,695 0.044 6,879 0.027 909 0.009 2,460 0.041 Aggr. asks 15,065 0.102 11,632 0.108 14,544 0.058 12,595 0.129 6,787 0.114 Normal asks 10,306 0.070 7,963 0.074 50,769 0.201 9,565 0.098 4,106 0.069 Canc. asks, v ≥ 75% 1,029 0.007 842 0.008 528 0.002 716 0.007 273 0.005 Canc. asks 6,689 0.045 5,912 0.055 30,390 0.121 4,572 0.047 2,763 0.046 Aggr. sells, v ≥ 75% 2,338 0.016 2,867 0.027 2,372 0.009 551 0.006 892 0.015 Aggr. sells 6,133 0.042 8,016 0.075 7,711 0.031 1,747 0.018 3,559 0.060 Normal sells 15,455 0.105 13,100 0.122 17,249 0.068 17,707 0.181 7,329 0.123 Most aggr. bids, v ≥ 75% 1,611 0.011 1,455 0.014 1,174 0.005 353 0.004 348 0.006 Most aggr. bids 5,772 0.039 4,626 0.043 5,903 0.023 1,249 0.013 2,109 0.035 Aggr. bids 17,441 0.118 12,967 0.121 15,333 0.061 13,848 0.142 8,010 0.135 Normal bids 14,900 0.101 8,504 0.079 42,375 0.168 9,125 0.093 5,594 0.094 Canc. bids, v ≥ 75% 881 0.006 879 0.008 625 0.002 719 0.007 292 0.005 Canc. bids 7,329 0.050 6,265 0.058 27,624 0.110 5,584 0.057 3,318 0.056 All aggr. Orders, v ≥ 75% 9,316 10,463 9,142 3,102 3,438 Descriptive statistics of order arrival processes of the BHP, NAB, NCP, TLS and WOW stock. The order categories are defined in Table 2. “v ≥ 75%” means that the quoted volume is equal or higher than the 75%-quantile of all order volumes. The table shows the number of orders in the individual categories as well as their corresponding percentage with respect to the complete sample of the individual stock. The sample contains all market and limit orders of the individual stocks traded on the ASX during July and August 2002, corresponding to 45 trading days retaining sufficient observations in the individual dimensions, we use the 75%quantile as a natural compromise for the choice of the selection threshold. Hence, we select those aggressive orders whose posted volume is equal or higher than the 75%-quantile of all order volumes. Correspondingly, aggressive cancellations are cancellations of pending limit orders with a volume equal or higher than the 75%quantile of all order volumes. This selection rule leads to a significant reduction of the sample size resulting in 9,316, 10,463, 9,142, 3,102, and 3,438 observations for BHP, NAB, NCP, TLS, and WOW respectively (see Table 3).
Order aggressiveness and order book dynamics 147 5 Empirical results As the estimation of six-dimensional ACI processes is a challenging task requiring the estimation of a large number of parameters, we estimate restricted ACI specifications. In order to reduce the number of parameters, we specify the baseline intensity functions in terms of a Weibull parameterization, where we do not allow for interdependencies between the individual functions, i.e. pr k =1∀k ≠ r. This restriction is motivated by the fact that the consideration of interdependencies would require estimating 30 additional parameters without significantly improving the model’s goodness-of-fit in terms of the BIC. Similar arguments hold for the specification of spill-over effects in the persistence terms, where we restrict the matrix B to be specified as a diagonal matrix. In order to account for deterministic intra-day seasonality patterns, we specify three linear spline functions for the processes of aggressive market orders, limit orders, and cancellations based on 1 h nodes. 11 To ease the numerical optimization of the log-likelihood function, we standardize the time scale by the average duration of the pooled process. To test the economic hypotheses formulated in Sect. 2, we define several explanatory variables to capture the state of the market. The market depth on the ask side is measured by the (log) ratio between the current 5% ask volume quantile and the corresponding price impact, formally given by AD=ln[0.05·avol/ (p0.05,a−mq)], where avol denotes the aggregated volume pending on the ask queue, p 0.05,a is the limit price associated with the 5% ask volume quantile and mq denotes the mid-quote. Correspondingly, the bid depth is given by BD=ln[0.05·bvol/(mq− p 0.05,b)]. The choice of the 5% quantile is driven by the trade-off between a parsimonious specification 12 and an appropriate measurement of market depth. However, recent studies (see e.g. Pascual and Veredas 2004 or Hall and Hautsch 2004) show that traders’ order submission is dictated by the depth in the lower sections of the book. Therefore, we presume that the impact of market depth is well approximated by the volume–price relation over the 5% volume quantile. In order to account not only for the volume–price ratio solely, but also for the volume level itself, we include AV=ln(avol) and BV=ln(bvol ) as separate regressors. Furthermore, we capture the (signed) cumulative changes in the logarithmic aggregated ask volume (DAV), the logarithmic aggregated bid volume (DBV) as well as in the mid-quote (MQ) process during the past 5 min. Finally, we include the current volatility (VL), measured by the average squared mid-quote changes during the past 5 min as well as the current bid-ask spread (SP). In order to analyze the importance of order book dynamics and the information provided by the open limit order book for the goodness-of-fit and the explanatory power of the model, we estimate three different specifications. Table 4 reports the estimation results based on an ACI model including both dynamic variables as well as order book variables. Table 5 is based on a specification which includes order book information, but does not account for any dynamics in the multivariate process. Hence, in this specification, e i is set to zero. Finally, Table 6 gives the results of a specification which accounts for dynamic structures but excludes any order book covariates. 11 However, motivated by the results by Hall and Hautsch (2004), we assume identical seasonality patterns on the ask and bid side. 12 Note that for each regressor six parameters have to be estimated.
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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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Page 106 and 107: 100 S. Frey, J. Grammig quotes on e
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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
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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 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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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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