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218 If we meet a particular case such that a minimum and a maximum occur at the same first moment, then we retain arbitrarily the maximum. To build the alternate series, we have to know the type of the last extremum introduced into the series. If it is a maximum (minimum) then the next extremum has to be a minimum (maximum) selected from the low (high) series such that: n o E ¼ minLj;k j ¼ min tm Lj;k ; tm Lj;k > tE j;kjEðj 1Þ;k2 maxH ðj 1Þ;k j;k n o ¼ maxHj;k j ¼ min tM Hj;k ; tM Hj;k > tE ; ðj 1Þ;k E j;kjEðj 1Þ;k2 minL j;k where E j,k is the extremum detected on original series. Finally, the obtained series is scanned by the recognition pattern algorithms to identify an eventual chart pattern. C. Definition of chart patterns C.1. Inverse head and shoulders (IHS): IHS is characterized by a sequence of five extrema Ei (i=1, . . . , 5) such that: 8 where IHS E1 < E2 E3 < E1; E3 < E5 jpE2; ð E4Þj tgð10Þ jpE1; ð E5Þj tgð10Þ >< Vt ðE2;E4Þ E1 E1 0:9 Vt ðE2;E4Þ 1:1 E5 E5 h 1:1 s 2:5 1 tE td 2 2 2 tf tE4 1 tE tE 4 2 >: 2 m 2 2 ð Þ h Ptmax Ptd – h is the height of the head: h ¼ VtE ðE2; E4Þ E3 3 – s is the height average of the two shoulders: s ¼ Vt ðE2;E4Þ E1 þ Vt ðE2;E4Þ E5 E1 E5 2 – Ptmax is the highest price observed into the time interval [td−(f−d), td]: Ptmax ¼ maxðPtÞ j td ðfdÞ t td – td is the starting time for the pattern – tf is the ending time for the pattern – td − (f− d)=t d−(t f −td) – tf +(f− d)=t f +(tf −td) – m is the average time which the shoulders take for their total completion 3 W. B. Omrane, H. V. Oppens
The performance analysis of chart patterns 219 C.2. Double top (DT) DT is characterized by a sequence of three extrema Ei (i=1 , . . , 3), such that: DT 8 >< >: E1 > E2 E1 E2 Vt ðE1;E3Þ ¼ 1 E2 E2 E3 E2 Vt ðE1;E3Þ ¼ 1 E2 E2 1 tE td 2 2 2 ðtftdÞ 2 1 tf tE2 2 2 ðtftdÞ 2 2 ðPtdPtminÞ 3 ðVtE ðE1; E3Þ 2 E2Þ C.3. Double bottom (DB) DB is characterized by a sequence of three extrema Ei (i =1, . . . ,3), such that: DB 8 >< >: E1 < E2 E2 E1 E2 Vt ðE1;E3Þ ¼ 1 E2 E2 E3 E2 Vt ðE1;E3Þ ¼ 1 E2 1 tE td 2 2 2 ðtftdÞ 2 1 tf tE2 2 2 ðtftdÞ 2 2 ðPtmax PtdÞ 3 ðE2 VtE ðE1; E3ÞÞ 2 C.4. Triple top (TT) TT is characterized by a sequence of five extrema Ei (i =1, . . . ,5) such that: 8 TT >< >: E1 > E2 jpE1; ð E5Þj tgð10Þ jpE2; ð E4Þj tgð10Þ h 0:9 E1 Vt ðE2;E4Þ 1:1 E1 h 0:9 E5 Vt ðE2;E4Þ 1:1 E5 1 2 1 2 1 2 tE 2 td ðtftdÞ 3 tE4 tE2 ðtftdÞ 3 tf tE4 ðtftdÞ 3 ð Þ 2 Ptd Ptmin 2 2 2 3 h
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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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96 Table 2 First stage GMM results
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Table 4 First stage GMM results bas
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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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Page 206 and 207: 200 W. B. Omrane, H. V. Oppens anal
Page 208 and 209: 202 W. B. Omrane, H. V. Oppens of s
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Page 212 and 213: 206 price 0.8565 0.8575 0.8585 0.85
Page 214 and 215: 208 We distinguish three possible c
Page 216 and 217: 210 originates. The most active tra
Page 218 and 219: 212 Table 2 Predictability of the c
Page 220 and 221: 214 6 Conclusion Using 5-min euro/d
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Page 228 and 229: 222 References W. B. Omrane, H. V.
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Page 262 and 263: 256 simply aggregated. Even after a
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Page 270 and 271: 264 A. S. Tay, C. Ting More interes
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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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