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136 includes order book variables, but excludes dynamics, outperforms a dynamic specification without covariates. This result clearly indicates that traders’ order aggressiveness and order submission strategy is affected by the state of the book. Regarding the impact of order book variables on order aggressiveness, our results broadly confirm the theoretical results on traders’ optimal order submission strategies as derived by Parlour (1998) and Foucault (1999). In particular, the impact of depth on order aggressiveness can be explained by “crowding out” effects as discussed in Parlour (1998). Moreover, our findings provide evidence for the notion that traders use the order book information to infer expected future price movements. Nevertheless, we also observe behavior that is not consistent with predictions implied by theoretical dynamic equilibrium models. For instance, we find evidence for liquidity driven order submissions after mid-quote changes in the recent past. Furthermore, no support is found for the hypothesis that the current volatility affects the mix between aggressive market and limit orders. Rather, we observe that a rise in volatility increases the overall order submission activity in the market. The remainder of the paper is organized in the following way: In Sect. 2, we discuss economic hypotheses on the basis of recent theoretical research on limit order book trading. Section 3 presents the econometric approach. In Sect. 4, we describe the data as well as descriptive statistics characterizing the limit order books of the individual stocks traded at the ASX. The empirical results are reported and discussed in Sect. 5 and Sect. 6 concludes. 2 Economic hypotheses A. D. Hall, N. Hautsch The desire for a deeper understanding of market participants’ order submission strategies in a limit order book market has inspired a wide range of theoretical and empirical research. 4 In a limit order book market investors must choose between limit orders and market orders and as a result traders face a dilemma. The advantage of a market order is that it is executed immediately. However, with a limit order, while traders have the possibility of improving their execution price, they face the risk of non-execution as well as the risk of being “picked off”. The latter arises from the possibility that, as a result of new information entering the market, a limit order can become mispriced. These economic principles form the basis of numerous theoretical approaches in this area. Parlour (1998) proposes a dynamic equilibrium model in which traders with different valuations for an asset arrive randomly in the market. The endogenous execution probability of a limit order then depends both on the state of the book and how many market orders will arrive over the remainder of the day. She shows that both the past, through the state of the book, and the future, through the expected order flow, affect the placement strategy and cause systematic patterns in transaction and order data. The major underlying idea is the mechanism of a “crowding out” of market sell (buy) orders after observing market buy (sell) orders. This is due to the effect that after a buy (sell) market order, a limit order at the ask 4 See e.g. Glosten (1994), Handa and Schwartz (1996), Harris and Hasbrouck (1996), Seppi (1997), Harris (1998), Bisière and Kamionka (2000), Griffiths et al. (2000), Lo and Sapp (2003), Cao et al. (2003), or Ranaldo (2004) among others.
Order aggressiveness and order book dynamics 137 (bid) has a higher execution probability. Since the payoff to limit orders increases with the probability of execution, a trader who wants to sell (buy) is now more likely to submit a sell (buy) limit order instead of a sell (buy) market order. Because of this crowding out of market orders on the opposite side, buy (sell) market orders are less frequent after sell (buy) market orders than after buy (sell) market orders. This trading behavior has been confirmed by the empirical work of Biais et al. (1995), Griffiths et al. (2000) and Ranaldo (2004). Parlour’s model also predicts that the probability of observing a limit buy order after the arrival of a limit buy order is smaller than the probability of observing a limit buy order after any other transaction. This is due to the fact that a lengthening of the queue at one level decreases the execution probability of further limit orders at the same level and thus makes them less attractive. Applying this theoretical underpinning, the “crowding out” argument implies testable relationships between changes in the depth of the book volume and their impact on traders’ incentive to post market orders, limit orders or cancellations. As a result, we can formulate the following hypothesis: (1)An increase of the depth on the ask (bid) side – increases the aggressiveness of market trading on the bid (ask) side, – decreases the aggressiveness of limit order trading on the ask (bid) side, – increases the probability of cancellations on the ask (bid) side. A traders’ order submission strategy does not only depend on the current state of the book but also on recent movements in the price. Positive price movements during the recent past indicate an aggressiveness in buy limit orders and buy market trading leading to a relative decline of the ask depth compared to the bid depth. Applying the crowding out concept from Parlour’s model we can formulate Hypothesis (2) as follows: (2)Past price movements are – negatively (positively) correlated with the aggressiveness of market trading on the bid (ask) side, – positively (negatively) correlated with the aggressiveness of limit order trading on the ask (bid) side, – negatively (positively) correlated with the probability of cancellations on the ask (bid) side. Foucault (1999) proposes a dynamic equilibrium model to explain traders’ choice between limit orders and market orders as a function of the asset’s volatility. In this model, investors’ valuations of shares differ and traders’ order placement strategies depend on their valuations as well as the best offers in the book. Foucault (1999) shows that the volatility of the asset is a determinant of the mix between market and limit orders. Since higher volatility increases the pick-off risk, this increases the reservation prices of limit order traders, widening spreads and increasing the cost of market trading. As a result traders’ incentive to post limit orders (market orders) increases (decreases) leading to Hypothesis (3): (3)A higher volatility decreases the aggressiveness in market order trading and increases the aggressiveness in limit order trading.
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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
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 137: How large is liquidity risk in an a
Page 140 and 141: 134 A. D. Hall, N. Hautsch In this
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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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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