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206 price 0.8565 0.8575 0.8585 0.8595 price 0.857 0.858 0.859 0.860 2170 2180 2190 2200 Fig. 2 The head and shoulders: observed chart pattern. This figure shows an observation window in which the Head and Shoulders chart pattern is detected through both M1 and M2 methods (detailed in Appendix B). The dashed lines in both graphs illustrates the smoothed price curves and the solid line, for the first graph, presents the original price curve. The second graph shows the original price series through bar charts. Each of them involves the maximum, the minimum, the open and the close price for each 5-min time interval The HS chart pattern is characterized by a sequence of five extrema Ei (i =1,..,5) such that: 8 E1 > E2 ðaÞ E3 > E1; E3 > E5 ðbÞ jpE1; ð E5Þj tgð10Þ ðcÞ jpE2; ð E4Þj tgð10Þ ðdÞ >< E1 Vt ðE2;E4Þ E1 hs 0:9 E5 Vt ðE2;E4Þ 1:1 ðeÞ E5 h 1:1 s 2:5 ðf Þ 1 tE td 2 2 2 ðgÞ tf tE4 1 tE tE 4 2 >: 2 m 2 ðhÞ 2 ðPtdPtminÞ 3 h ðiÞ where – h is the height of the head: h ¼ E3 VtE ðE2; E4Þ 3 – s is the average height of the two shoulders: s ¼ E1 Vt ðE2; E4Þ þ E5 Vt ðE2; E4Þ E1 E5 2 Head and Shoulders: Method M1 time Head and Shoulders: Method M2 2170 2180 2190 2200 time W. B. Omrane, H. V. Oppens
The performance analysis of chart patterns 207 – td is the starting time for the pattern: td ¼ maxt ðPtVtðE2; E4Þ; t < tE1Þ – tf is the ending time for the pattern: tf ¼ mint ðPtVtðE2; E4Þ; t > tE5Þ – td − (f − d) = td −(tf − td) – tf +(f− d) = tf +(tf − td) – m is the average time that the shoulders take for their total completion: m ¼ tE ð td 2 ÞþðtftE4Þ 2 – Ptmin is the smallest price observed in the time interval ¼ min ðPtÞ td ðfdÞ t td [t d − (f − d), t d]: Ptmin If a sequence of five extrema satisfies the above conditions, they build up a Head and Shoulders chart pattern. Theoretically, at the completion of this chart pattern, the price must go down for at least the height of the head, h. Furthermore, the objective price detected by the chart pattern has to be reached within the time interval [tf , tf +(f − d)]. In other words, the price has to reach at least P(obj) such that: 3.3 The performance measures PðobjÞ ¼ Ptf h: (3) Detected chart patterns are analyzed in terms of predictability and profitability. In other words, we study the capability of each chart pattern to predict the future price trend just after the chart completion and the profit that a dealer could realize when he applies a trading rule. 3.3.1 Predictability Following its completion, the chart pattern can be used to forecast the future price trend. More precisely, it predicts the price objective which has to be reached. We denote by h the predicted price variation, and by tf and t d , respectively, the time at the end and at the beginning of the chart pattern. If the pattern predicts a downward trend, the price objective is given by Eq. (3). This price objective has to be reached within the time interval [t f , t f +(f − d )]. In such cases, we can measure the actual price reached in this time interval by computing P a such that: Pa ¼ min Pt tf t tfþðfdÞ : (4) The value of the observed trend is then: trend ¼ Ptf Pa: (5) The predictability criterion is defined as follows: pred ¼ trend : (6) 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 5 (continued) BHP NAB NCP TLS
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Page 166 and 167: 160 specification which includes or
Page 168 and 169: 162 5.2.5 The impact of the bid-ask
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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
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Page 216 and 217: 210 originates. The most active tra
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Page 220 and 221: 214 6 Conclusion Using 5-min euro/d
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Page 226 and 227: 220 C.5. Triple bottom (TB) TB is c
Page 228 and 229: 222 References W. B. Omrane, H. V.
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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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