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52 A. Escribano and R. Pascual Jang and Venkatesh (1991) reports that, in the NYSE, one-step-ahead revisions in ask and bid quotes right after trades do not generally support the theoretical prediction of symmetry. Moreover, quote-revision patterns strongly depend on the level of the outstanding spread, with more symmetric adjustments as the spread augments. They argue the symmetry assumption may be violated in practice because certain theoretical premises are simply unrealistic. Thus, it is usually assumed that quotes can be adjusted in a continuous fashion, which is not possible because of the minimum price variations. In addition, it is usually supposed that posted quotes are always for the specialist own account. However, in most stock exchanges, as is the case of the NYSE, quotes reflect the interest of several traders that may be selectively offering one-sided liquidity (see Madhavan and Sofianos 1998; Kavajecz 1999; Chung et al. 1999). Since different agents may be subject to different trading costs, the offer and demand components of the spread may vary asymmetrically about the efficient price. Hasbrouck (1999a) points out that the usual theoretical premise of equal market making costs at the offer and demand sides of the market is reasonable if the same quote-setter is active on both sides. However, even if the specialist would take all trades, it is not clear that she would adjust quotes simultaneously and by the same amount after a trade. Thus, if the trade signals the presence of informed traders, a natural response of the specialist would be to post a wider spread. Therefore, she would update ask and bid quotes asymmetrically. Moreover, the costs of ask and bid exposure might not necessarily be balanced. Thus, a specialist offering liquidity in times of an upward price pressure would suffer from higher exposure costs on the ask side than on the bid side of the market. Biais et al. (1995) shows that asymmetries between ask and bid quotes are not an exclusivity of the NYSE. Using data on a pure order-driven market, the Paris Bourse, they show that ask and bid one-step-ahead adjustments after trades are also asymmetric. Their findings hint that the ask (bid) quote may lead the adjustment of the bid (ask) price after an order to buy (sell). These authors conclude that “there is additional information in analyzing the dynamics of ask and bid prices jointly rather than averaging them through the quote midpoint” (pg. 1679). There is also evidence suggesting that the impact of a buyer-initiated trade may not be merely the reverse of the price impact of seller-initiated trade. Empirical work on block trading (e.g., Holthausen et al. 1987; Griffiths et al. 2000; Koski and Michaely 2000) shows that orders to sell and orders to buy may have different permanent and transitory impacts on prices. Chan and Lakonishok (1993, 1995) use a broad range of trade sizes to evidence differences in the behavior of prices after institutional purchases and sales. Keim and Madhavan (1995) conclude that large buys take longer to execute than equivalent sells because traders perceive that price impacts of buys are greater than sells. Huang and Stoll (1996) report different realized spreads for buyer and seller-initiated trades. Similarly, Lakonishok and Lee (2001) observe that the information content of insider’s activities come from purchases while insider selling appears to have no predictive ability. 3 3 These asymmetries might not be exclusive of large-sized trades. Hasbrouck (1988, 1991), Barclay and Warner (1993), Kempf and Korn (1999), among others, evidence the relationship between trade size and price impact is increasing, but concave. Therefore, informed traders may concentrate their trades in medium sizes.
Asymmetries in bid and ask responses to innovations in the trading process 53 In all these papers, buyer-initiated trades are usually found to be more informative than seller-initiated trades. Price responses to buyer-initiated versus seller-initiated trades may be asymmetric for a variety of reasons. Firstly, short selling restrictions may prevent insiders from exploiting negative information (Kempf and Korn 1999). Secondly, since an investor typically does not hold the market portfolio, the choice of a particular stock to sell does not necessarily convey negative information. On the contrary, the choice of a particular stock to buy, out of the numerous possibilities on the market, is likely to convey favorable firm-specific news (Chan and Lakonishok 1993). Finally, a sell order representing a small fraction of the initiator’s known position may be considered as more liquiditymotivated than a similar buy order from an investor without current holdings in the security (Keim and Madhavan 1995). 4 Alternative empirical approaches have been proposed to jointly model the generating processes of quotes and trades. The most influential is probably due to Hasbrouck (1991). 5 He suggests the following vector autoregressive (VAR) specification, qt ¼ P 1 i¼1 ai qt 1 þ P 1 i¼0 bixt i þ v1;t xt ¼ P 1 i¼1 ci qt 1 þ P 1 i¼1 dixt i þ v2;t ; (2.1) where Δq t=(q t−q t−1) represents the revision in the quote midpoint (q t) after a trade at t and x t is a trade indicator that equals 1 for buyer-initiated trades and −1 for seller-initiated trades. The terms v 1,t and v 2,t are mutually and serially uncorrelated white noises that represent trade-unrelated and trade-related shocks respectively. This econometric approach covers the dynamics of many structural microstructure models as special cases (see Hasbrouck 1991, 1996). Hasbrouck builds on a “weak symmetry assumption:” the quote midpoint must revert to the efficient price as the end of trading approaches. Therefore, ask and bid prices may not be symmetrically posted around the efficient price. However, since the quote dynamics are averaged through the quote midpoint (qt), the VAR model is not a valid framework to accommodate and evaluate possible asymmetries in the dynamics of ask and bid prices. Similarly, the trade dynamics are averaged through 4 Other aspects of the trading process that may produce a lack of balance between the impact of buys and sells are transitory market conditions and the trade durations. Thus, a larger market pressure to sell than to buy might increase the expected impact of a seller-initiated market order (e.g., Goldstein and Kavajecz 2004) versus a similar buyer-initiated order. Easley et al. (1997) and Dufour and Engle (2000) show that trade durations (time between consecutive trades) partly explain the long-term impact of trades, even when trade size is accounted for. If a market overreacts to bad news, it might produce shorter time durations after seller-initiated trades than after buyer-initiated trades, and therefore asymmetries in the responses of quotes between buys and sells. 5 Other econometric approaches, parametric and semi-parametric respectively, to model the relationship between the trading process and the price changes are Hausman et al. (1992), that used ordered probit models, and Kempf and Korn (1999) that employed a neural networks type model.
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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,
Page 8 and 9: 2 L. Bauwens et al. component nicel
Page 10 and 11: 4 L. Bauwens et al. but provides al
Page 13 and 14: Luc Bauwens . Dagfinn Rime . Genaro
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Page 40 and 41: 34 K. Bien et al. 2.1 Copula functi
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Page 44 and 45: 38 K. Bien et al. Fig. 3 Multivaria
Page 46 and 47: 40 K. Bien et al. Bivariate model s
Page 48 and 49: 42 K. Bien et al. deviation 0.0099,
Page 50 and 51: 44 K. Bien et al. - Set ˆzt = Âxt
Page 52 and 53: 46 K. Bien et al. % Frequency −0.
Page 54 and 55: 48 K. Bien et al. References Amilon
Page 56 and 57: 50 1 Introduction A. Escribano and
Page 60 and 61: 54 A. Escribano and R. Pascual the
Page 62 and 63: 56 The generating processes of mark
Page 64 and 65: 58 with AtðLÞ ¼ 0 B @ 1 ð ÞAab
Page 66 and 67: 60 A. Escribano and R. Pascual cros
Page 68 and 69: 62 Hasbrouck (1991). The system is
Page 70 and 71: 64 unitary seller-initiated shock (
Page 72 and 73: 66 6.1 Estimation of the baseline m
Page 74 and 75: Table 2 The base-line VEC model for
Page 76 and 77: 70 Table 3 Simulation of the base-l
Page 78 and 79: 72 revert towards narrow levels. As
Page 80 and 81: Table 5 Impulse-response functions
Page 82 and 83: 76 We also show that NYSE buyer-ini
Page 84 and 85: 78 As 0 < a m < 1; L ð Þ ¼ 1 a m
Page 86 and 87: 80 NYSE 2000 Stocks AOL America Onl
Page 88 and 89: 82 A. Escribano and R. Pascual Madh
Page 90 and 91: 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
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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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How large is liquidity risk in an a
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134 A. D. Hall, N. Hautsch In this
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136 includes order book variables,
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138 An important determinant of liq
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140 The innovation term ei is compu
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Table 1 Order book characteristics
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144 data covering the normal tradin
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146 Table 3 Descriptive statistics
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Table 4 Fully specified ACI models
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Table 4 (continued) BHP NAB NCP TLS
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Table 5 ACI models without dynamics
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Table 6 ACI models without covariat
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160 specification which includes or
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162 5.2.5 The impact of the bid-ask
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164 contrast to those of Pascual an
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Roman Liesenfeld . Ingmar Nolte . W
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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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