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220 C.5. Triple bottom (TB) TB is characterized by a sequence of five extrema Ei (i = 1, . . . ,5) such that: 8 C.6. Rectangle top (RT) TB >< >: E1 < E2 pE2; ð E4Þ pE1; ð E5Þ h 0:9 Vt ðE2;E4Þ E1 E1 h 0:9 Vt ðE2;E4Þ E5 E5 j j tgð10Þ j j tgð10Þ 1:1 1 2 1 2 1 2 tE 2 td ðtftdÞ 3 tE4 tE2 ðtftdÞ 3 tf tE4 ðtftdÞ 3 ð Þ 2 Ptmax Ptd RT is characterized by a sequence of six extrema Ei (i =1, . . . ,6) such that: 8 RT C.7. Rectangle bottom (RB) >< >: E1 > E2 pE1; E5 pE2; E6 ð Þ 2 2 2 3 h j ð Þj 0:001 j ð Þj 0:001 Vt E3 E1;E5 E3 E4 Vt ðE2;E6Þ E4 ¼ 1 ¼ 1 2 ðPtdPtminÞ 3 RB is characterized by a sequence of six extrema Ei (i =1, . . . ,6) such that: 8 RB >< >: E1 < E2 pE2; E6 pE1; E5 E3 Vt ðE1;E5Þ E3 Vt ðE2;E6Þ E4 1:1 h j ð Þj 0:001 j ð Þj ¼ 1 0:001 ¼ 1 E4 2 ð Þ Ptmax Ptd 3 h W. B. Omrane, H. V. Oppens
The performance analysis of chart patterns 221 C.8. Broadening Top(BT) BT is characterized by a sequence of five extrema Ei (i=1, . . . ,5) such that: 8 < E1 > E2 BT : E3 > E1; E4 < E2; E5 > E3 2 ð Þ h C.9. Broadening bottom(BB) Ptd Ptmin BB is characterized by a sequence of five extrema Ei (i=1, . . . ,5) such that: 8 < E1 < E2 BB : E3 < E1; E4 > E2; E5 < E3 2 ð Þ h C.10. Triangle Top (TRIT) Ptmax Ptd TRIT is characterized by a sequence of four extrema Ei (i = 1, . . . ,4) such that: 8 TRIT >< >: E1 > E2 pE1; E3 0:9 jpE1;E3 ð Þj pE2;E4 ð Þ 1:1 ð Þ tgð 30Þ tf tE1 þ 0:75 tint ð h tE1Þ PtE 1 Ptmin where tint is the moment of support and resistance lines intersection: tint ¼ mint ðVtðE1; E3Þ VtðE2; E4Þ; t > tE4Þ. C.11. Triangle bottom(TRIB) TRIB is characterized by a sequence of four extrema Ei (i =1, . . . ,4) such that: 8 TRIB >< >: 3 3 2 3 E1 < E2 pE2; E4 0:9 jpE2;E4 ð Þj pE1;E3 ð Þ 1:1 ð Þ tgð 30Þ tf tE1 þ 0:75 tint ð h tE1Þ Ptmax PtE 1 where tint is the moment of support and resistance lines intersection: tint ¼ min tðVtðE2; E4Þ VtðE1; E3Þ; t > tE4Þ: 2 3
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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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Roman Liesenfeld . Ingmar Nolte . W
Page 175 and 176: Modelling financial transaction pri
Page 203: Modelling financial transaction pri
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
Page 210 and 211: 204 The extrema detection method ba
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
Page 222 and 223: 216 where σk is the standard devia
Page 224 and 225: 218 If we meet a particular case su
Page 228 and 229: 222 References W. B. Omrane, H. V.
Page 231 and 232: Juan M. Rodríguez-Poo · David Ver
Page 233 and 234: Semiparametric estimation for finan
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Page 260 and 261: 254 between price changes and durat
Page 262 and 263: 256 simply aggregated. Even after a
Page 264 and 265: 258 is to make use of the fact that
Page 266 and 267: 260 together with the restrictions
Page 268 and 269: Table 3 Estimated probabilities of
Page 270 and 271: 264 A. S. Tay, C. Ting More interes
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Page 274 and 275: 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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