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76 We also show that NYSE buyer-initiated trades are more informative that sellerinitiated trades, both in 1996 and in 2000. This pattern persists even when we consider different model specifications, including different time periodicities. This finding, however, cannot be generalized to the SSE case. This paper has also evidenced that market frictions like the minimum price variation are not enough to explain the violation of the symmetry assumption. We find similar asymmetric patterns in ask and bid responses in 1996, with a US$1/8 tick, and in 2000, with a US$0.01 tick. In addition, asymmetries are found in markets with very different microstructures, like the NYSE and the SSE. Since the specialist contribution is less essential in frequently traded NYSE stocks (Madhavan and Sofianos 1998), it sounds interesting to extend the analysis in this paper by considering a larger sample of NYSE and SSE stocks, stratified by trading frequency. This analysis would clarify the role that market makers play in explaining the asymmetric dynamics between ask and bid quotes. Moreover, our findings suggest that ask (bid) quotes may lead the price discovery process after buyer (seller) initiated trades. An interesting topic for future research would be evaluating the relative contribution of ask and bid quotes to price discovery, conditional on variables like recent market trends, accumulated net volume, order imbalances, and so on. Finally, the intriguing finding that buys are more informative Table 6 The base-line VEC model for aggregated data Equation Variable Panel A: 1-minute intervals Panel B: 5-minute intervals Coefficients average Std. (+) (−) Coefficients average Std. (+) (−) bt −0.2231 0.0752 0 11 −0.3290 0.1177 0 11 a 0.0633 0.0556 10 1 −0.0297 0.0473 10 1 x B 0.0172 0.0089 11 0 0.1316 0.0886 11 0 x S −0.0133 0.0080 0 11 −0.1135 0.0746 0 11 bt 0.2608 0.0710 11 0 0.4421 0.0870 11 0 b 0.0593 0.0696 9 2 −0.0326 0.0529 9 2 x B 0.0121 0.0074 11 0 0.1240 0.0846 11 0 x S −0.0173 0.0089 0 11 −0.1241 0.0819 0 11 x B t s 2.1055 2.3079 10 1 0.0361 0.4750 10 1 a −1.2400 1.2389 0 11 0.0155 0.3778 0 11 x B 0.6748 0.1089 11 0 0.6472 0.1317 11 0 x S 0.2963 0.0879 11 0 0.3579 0.1307 11 0 x S t s 1.9812 2.0402 10 1 0.1100 0.6099 10 1 b 0.4601 1.8280 4 7 0.0033 0.1560 4 7 x B 0.3985 0.1223 11 0 0.4275 0.1433 11 0 x S 0.5396 0.1476 11 0 0.5601 0.1496 11 0 Obs. 23,484 186 4,688 62 R2 A. Escribano and R. Pascual a t 0.1621 0.0400 0.1913 0.0725 b t 0.1847 0.0387 0.2030 0.0730 x B t 0.9532 0.0222 0.9945 0.0015 x S t 0.9328 0.0382 0.9938 0.0015
Asymmetries in bid and ask responses to innovations in the trading process 77 Table 6 (continued) Absolute IRF×100 Buy/ask – buy/bid Diference p-value t-test Diference p-value t-test Mean 1.7480 0.0017* 2.3780 0.0111** Std. (1.5230) (2.9193) Sell/bid – sell/ask Mean 6.9870 0.0007* 49.1156 0.0024* Std. (2.7803) (25.8798) Buy/ask – sell/bid Mean 1.9127 0.0105** 11.0851 0.0099* Std. (2.3180) (13.2844) This 0 table summarizes 1 the estimation of the VEC model, 1 0 AaB * AaB * at B 0 1 AbB * AbS * C bt B C @ 0 0 1 0 A ~x 0 0 0 1 B t ~x S 0 1 B C B C @ A t ¼ EC a L ð Þ EC b L 0 1 B ð ÞC B C @ BðLÞ A SðLÞ st 0 B 1 þ AL ð ÞB @ 0 1 AaaðLÞ 0 AaBðLÞ AaSðLÞ B 0 AbbðLÞ AbBðLÞ AbSðLÞ C strictions, AL ð Þ ¼ B C @ ABaðLÞ 0 ABBðLÞ ABSðLÞA 0 ASbðLÞ ASBðLÞ ASSðLÞ than sells in the NYSE but not in the SSE suggests that microstructure differences may be playing a role. This is a possibility that deserves a more exhaustive analysis. 1 Appendix I 1.1 Derivation of the VEC model (3.7) From Eq. (3.2) 1 at 1 bt 1 ~x B t 1 ~x S t 1 a mL at ð mtÞ ¼ Ax;tðLÞ 0 xt þ EC ð Þþ" a t : 1 C A þ a at 1 bt 1 0 B @ ua t ub t u B t u S t 1 C Awith the re- The model is defined in 1-minute (Panel A) and 5-minute intervals (Panel B), and truncated at 3 lags. We use data on 11 NYSE-listed stocks from January to March 2000. The model is estimated by SURE. We report for each variable the cross-sectional average of the sum of all lags whenever the coefficients are statistically significant at the 1% level. We also provide the cross-sectional average R 2 for each equation in the system. We include the number of stocks for which the coefficient of the corresponding variable is statistically positive/negative at the 1% level. Finally, we compare (a) the impact of a unitary buyer-initiated shock on the ask and bid quotes; (b) the impact of unitary seller-initiated shock on the ask and bid quotes, and (c) the impact of a unitary buyer-initiated shock on the ask quote with the impact of a unitary seller-initiated shock on the bid quote. We report the differences in the absolute impulse-response functions (IRF) and the result of a t-test on the null of equal IRFs against the alternative of a strictly positive difference. The errorcorrection term is the bid-ask spread. The endogenous variables are the change in the ask quote (Δa t) and the bid quote (Δb t) in each time interval, and trade-size indicators for buys ex B t and for sells ex S t are computed as the accumulated volume of buyer-initiated trades and sellerinitiated trades, respectively, in each time interval
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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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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 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 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 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
Page 117 and 118: 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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