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210 originates. The most active trading centers are New York, London, Frankfurt, Sydney, Tokyo and Hong Kong. A complete description of the FOREX market is given by Lyons (2001). To compute the mid prices used for the estimation of the models reported in Appendix A, we bought from Olsen and Associates a database made up of ‘tick-bytick’ euro/dollar quotes for the period ranging from May 15 to November 14, 2001 (i.e. 26 weeks and three days). This database includes 3,420,315 observations. As in most empirical studies on FOREX data, these euro/dollar quotes are market makers’ quotes and not transaction quotes (which are not widely available). 9 More specifically, the database contains the date, the time-of-day time stamped to the second in Greenwich mean time (GMT), the dealer bid and ask quotes, the identification codes for the country, city and market maker bank, and a return code indicating the filter status. According to Dacorogna et al. (1993), when trading activity is intense, some quotes are not entered into the electronic system. If traders are too busy or the system is running at full capacity, quotations displayed in the electronic system may lag prices by a few seconds to one or more minutes. We retained only the quotes that have a filter code value greater than 0.85. 10 From the tick data, we computed mid quote prices, where the mid quote is the average of the bid and ask prices. As we use 5-min time intervals, we have a daily grid of 288 points. Because of scarce trading activity during the week-end, we exclude all mid prices computed between Friday 21 h 05 min and Sunday 24 h. The mean of the mid-quotes is equal to 0.8853, the minimum and maximum are 0.8349 and 0.9329. 5 Empirical results W. B. Omrane, H. V. Oppens Table 1 presents the number of detected chart patterns for the extrema identification methods (M1 and M2). The results show the apparent existence of some chart patterns in the euro/dollar foreign exchange series. Using the first detection method (M1), we have more detected charts in the original price series than in the simulated one for six chart patterns (out of twelve), at the 5% significance level. When we implement the M2 method, we detect significantly only four chart patterns, which are also significantly detected by the method M1: DT, DB, RT and RB. By looking at the last column which represents the total number of detections, we can see that we have more detected chart patterns when only close prices are used (M1). These results confirm the idea that the presence of such chart patterns does not occur by chance, at least for some chart patterns, but it is due, amongst others, to a determined behavior of the chartist dealers. 9 Danielsson and Payne (2002) show that the statistical properties of 5-min dollar/DM quotes are similar to those of transaction quotes. 10 Olsen and Associates recently changed the structure of their HF database. While they provided a 0/1 filter indicator some time ago (for example in the 1993 database), they now provide a continuous indicator that lies between 0 (worst quote quality) and 1 (best quote quality). While a value larger than 0.5 is already deemed acceptable by Olsen and Associates, we choose a 0.85 threshold to have high quality data. We remove however almost no data records (Olsen and Associates already supplied us with data which features a filter value larger than 0.5), as most filter values are very close to 1.
The performance analysis of chart patterns 211 Table 1 Detected chart patterns Meth HS IHS DT DB TT TB RT RB BT BB TRIT TRIB ∑ M1 78 4 7** 12** 5 12 107** 89** 57* 135 38 73* 617 (0.42) (0.86) (0.00) (0.00) (0.96) (0.91) (0.00) (0.00) (0.02) (0.09) (0.18) (0.02) (0.12) 40% 25% 57%** 33%** 60% 42% 58% 73% 72% 67% 76% 74% 63% (0.92) (0.84) (0.00) (0.00) (0.51) (0.90) (0.79) (0.17) (0.39) (1.00) (0.38) (0.72) (0.66) M2 28 14 35** 44** 16 20 24** 33** 26 57 15 23 335 (1.00) (0.45) (0.00) (0.00) (0.36) (0.50) (0.00) (0.00) (0.92) (1.00) (1.00) (1.00) (0.50) 21% 21% 29%** 43%** 19% 35% 46% 45% 69%** 49% 53% 61% 42% (0.44) (0.64) (0.00) (0.00) (0.69) (0.34) (0.19) (0.24) (0.00) (0.10) (0.35) (0.60) (0.23) Entries are the number of detected chart patterns and the percentage of chart patterns that reached their price objective, according to the extrema detection methods M1 and M2 (described in Appendix B). The p-values, computed through a Monte-Carlo simulation, and given in parenthesis represent the percentage of times the results on the simulated series are greater than the one of the original price series. The last column presents results for the whole sample, whatever is the chart pattern ** and * indicate, respectively, significance at 1% and 5% The percentage of successful chart patterns (i.e. charts for which the price objective has been met) is given by the third and the seventh rows in Table 1. For example, 40% of Head and Shoulders (HS) detected by M1 succeed to meet their objective, but this result is not significant since for 92% of the simulated series we obtain more successful HS. For M1, only two charts, DT and DB present a significant successful percentage. 11 For M2, in addition to DT and DB, the chart pattern BT presents a significant percentage of success. Nevertheless, this measure of predictive power, i.e. the percentage of charts that succeed to meet their price objective, is too drastic. It does not allow to capture to what extent the price objective is not met or to what extent the price objective is outclassed. That is why we quantified the predictability through the ratio pred. Table 2 presents the average predictability pred for all detected chart patterns which succeed or fail to meet their objectives. For example, in the case of M1, HS has an average predictive power of 1,12. This average ratio is not significant at 5% since for 79% of the artificial series, we obtain a higher average ratio. However, the table shows that whatever the extrema detection methods implemented, more than one half of the whole chart patterns sample presents a predictability success statistically significant. At the 5% significance level, predictability varies from 0.86 to 9.45. The triangle chart patterns (TRIT and TRIB) offer the best predictability. These results are consistent with those obtained in Table 1 in which M1 exhibits more predictability. This observation is even more striking in Table 2. The last column shows that M1 provide on average, a predicted value more than twice larger than M2. This is confirmed by positive significant signs for the difference of means test presented in the last line of the Table 2. Comparatively, Table 1 shows a percentage of 63% of successful chart patterns using M1 against 42% provided by M2. Table 3 gives the maximum profitability that can be achieved by the use of chart patterns. It is computed in basis points (i.e.: 1/10,000) and provided for each of the twelve chart patterns. It corresponds to the implementation of the trading rule related to each chart pattern whatever its success level. The maximum profit is 11 Both chart patterns DT and DB have not been detected in any artificial series, whatever the extrema detection method implemented.
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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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Page 166 and 167: 160 specification which includes or
Page 168 and 169: 162 5.2.5 The impact of the bid-ask
Page 170 and 171: 164 contrast to those of Pascual an
Page 173 and 174: 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 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
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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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Page 233 and 234: Semiparametric estimation for finan
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Page 262 and 263: 256 simply aggregated. Even after a
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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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