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60 A. Escribano and R. Pascual crostructure of the NYSE changed from 1996 to 2000. Particularly interesting for the purposes of this paper is the progressive decrease of the minimum price variation or tick, from US$1/8 in 1996 to 1 cent in 2000. Jang and Venkatesh (1991) remarks that symmetric responses of ask and bid quotes are impaired by the discreteness of quote changes. Therefore, a small tick should decrease the probability of observing asymmetric adjustments of ask versus bid quotes after traderelated shocks. Trading activity in the NYSE has sharply increased since 1996. For example, from January to March 1996 IBM transacted 130,620 times; during the same interval in 2000 the number of trades was 234,766. For GE, the number of trades increased from 106,347 in 1996 to 350,795 in 2000. By considering these two NYSE subsamples, we have the opportunity to check whether microstructure and trading activity changes have influenced the dynamical relationship between trades and quotes. The NYSE sample includes the 11 most frequently traded stocks in 1996 and 2000, respectively, excluding stocks that experienced splits. The complete set of stocks is listed in Appendix II. We consider trades from both the primary market (NYSE) and regional markets. However, we only keep NYSE quotes because the evidence suggests that regional quotes only follow with some delay those of the primary market (Blume and Goldstein 1997). 12 Trades not codified as “regular trades”, such as trades out of sequence or reported with error, have been discarded. Trades from the same market, with the same price, and with the same time stamp are treated as just one trade. All quote and trade registers prior to the opening and after the close are dropped. The overnight changes in quotes are treated as missing values. Quotes with bid–ask spreads lower than or equal to zero or quoted depth equal to zero have also been eliminated. After these adjustments, around 3% of all trades have been eliminated. Finally, we follow Blume and Goldstein (1997) in deleting quoted spreads that exceed 20% of the quote midpoint, and quote updates that exceed 50% of the prior quote. Prices and quotes are coupled using the “fivesecond rule” (Lee and Ready 1991). This rule assigns to each trade the first quote stamped at least fives before the trade itself. A trade is classified as buyer (seller) initiated when the transaction price is closer to the ask (bid) price than to the bid (ask) price. Trades with price equal to the quote midpoint are not classified. The trade indictor xt B (x t S ) equals one for buys (sells) and zero otherwise; for a midpoint trade, both indicators equal zero. A change in quotes, either Δa t or Δb t, is the difference between the quote prevailing right before the t-th trade takes place and the quote prevailing right before the next trade in time. The SSE database contains the 11 most frequently traded stocks in 2000, listed in Appendix II. Spanish data is supplied by the SSE Interconnection System (SIBE). We retrieve trades and quotes from July to September 2000 because data from January to March is not available. We apply the same filters as for the US 12 Hasbrouck (1995) concludes that the contribution of the regional markets to the price discovery process of NYSE-listed stocks is negligible. Harris et al. (1995), however, observe that both the NYSE and the regional markets error correct to deviations from each other, therefore suggesting that the regional quotes do are informative. Tse (2000) compares the methodologies used in these papers. He concludes that the discrepant findings are only due to the choice of quotes (Hasbrouck) versus trade prices (Harris et al.). Tse suggests that trades in the regional markets could contribute to price discovery even when quotes were non-informative. This conclusion fundaments our choice of discarding regional quotes while keeping regional trades.
Asymmetries in bid and ask responses to innovations in the trading process 61 data. Quote changes and trade indicators are computed analogously to the NYSE case. Since price improvements are impossible in the SSE, there are no transaction prices inside the bid–ask spread. Therefore, we do not require trade-direction algorithms to classify the trades as either buys or sells. We rely on the theoretical and empirical research in market microstructure to determine the exogenous variables to be included in the vector MCt. Easley and O’Hara (1987) formally show that large-sized trades are more informative. Empirically, Hasbrouck (1991) and Barclay and Warner (1993), among others, show that this relationship is increasing but concave. In Easley and O’Hara’s (1992) model, higher trading intensity signals new information. Consistently, Easley et al. (1997) and Dufour and Engle (2000) find that shorter trade durations are associated with larger price impacts. Subrahmanyam (1997) finds that trades in the regional markets are less informative than NYSE trades, arguably because they attract liquidity-motivated traders (see Bessembinder and Kaufman 1997). Price instability means uncertainty about the true value of the stock (e.g., Bollerslev and Melvin 1994). Finally, a positive (negative) order imbalance between limit orders to sell and limit orders to buy may signal an overvalued (undervalued) stock (e.g., Huang and Stoll 1994). The following variables are defined so as to capture the relationships detailed above. The trade size (Vt) is measured in shares. Trade durations (Tt) are computed as the time in seconds between two consecutive trades. A dummy variable (Mt) identifies regional trades. Order imbalance (OIt) is computed as the difference between ask depth and bid depth. Finally, short-term volatility (Rt) is computed as the sum of the square changes of the quote midpoint Pz k¼1 ð qkÞ 2 in a 5-min interval before each trade. 13 Finally, we construct eight trading-time dummies for the NYSE session: one for trades during the first half-hour of trading, five for each trading hour between 10:00 A.M. and 3:00 P.M. and, finally, two for the last trading hour, divided in two halfhour intervals. Similarly, for the SSE session (9:00 A.M. to 5:30 P.M.), we construct nine dummy variables: one for the first half-hour of trading, another one for the second half-hour, six for each trading hour between 10:00 and 5:00 P.M. and, finally, one for the last half-hour. 5 Estimation of the baseline model for IBM in 1996 In this section, we present the details of estimating a restricted version of model Eq. j (3.7) described in Section 3 where fi (MCt,Dt)=1 for all i and j. We use data on a representative NYSE stock, IBM, in 1996. In the next section, we check whether the dynamic patterns about to be reported for IBM can be generalized to other stocks, other markets, other time periods, and other model specifications, including the unrestricted model Eq. (3.7). We consider two alternative specifications of the model. The first one uses the trade-sign indicators xt B S and xt to represent the trading process. The second one uses the trade-size indicators ex B t ¼ xBt log ðVtÞ and ex S t ¼ xSt log ðVtÞ. The polynomials in the autoregressive matrix A(L) are all truncated at lag five, as in 13 This variable is not defined for trades performed during the first 5 min of trading. In these cases, we treat volatility as missing.
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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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Page 38 and 39: 32 K. Bien et al. Although economet
Page 40 and 41: 34 K. Bien et al. 2.1 Copula functi
Page 42 and 43: 36 K. Bien et al. and x k t ≡ (xk
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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 58 and 59: 52 A. Escribano and R. Pascual Jang
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 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
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
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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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