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202 W. B. Omrane, H. V. Oppens of stock market and foreign exchange market returns by using past buy and sell signals, and they find an evidence of nonlinear predictability of such returns. In addition to these simple trading rules, technical analysis abounds of methods in order to predict future price trends. These methods have also been considered in empirical research. Jensen (1970) tests empirically the ‘relative strength’ trading rule. 1 The estimated profit provided by this trading rule is not significantly bigger than the one obtained by the ‘Buy and Hold’ strategy. 2 Osler (2000) finds that the support and resistance technique provides a predictive success. Other studies make use of genetic programs to develop trading rules likely to realize significant profits (e.g., Neely et al. (1997); Dempster and Jones (1998a) and Neely and Weller (1999)). Furthermore, Blume et al. (1994) demonstrate that sequences of volume can be informative. This would explain the widespread use by practitioners of technical analysis based upon volumes. The different studies mentioned above have mainly focused on linear price relations. However, other researchers have oriented their investigations to nonlinear price relations. Technical patterns, also called chart patterns, are considered as non-linear patterns. Both Murphy (1999) and Béchu and Bertrand (1999), argue that these kinds of patterns present a predictive success which allows traders to acquire profit by developing specific trading rules. In most studies, technical patterns are analyzed through their profitability. Levy (1971) focuses on the predictive property of the patterns based on a sequence of five price extrema and conclude, after taking into account the transaction costs, to the unprofitability of such configurations. Osler (1998) analyzes the most famous chart pattern, the head and shoulders pattern. 3 She underlines that agents who adopt this kind of technical pattern in their strategy must be qualified as noise traders because they generate important order flow and their trading is unprofitable. Dempster and Jones (1998b) and Chang and Osler (1999) obtain the same conclusion regarding the non profitability of the trading rules related to chart patterns. In contrast, Lo et al. (2000) show that the informational content of chart patterns affects significantly future stock returns. Some studies go beyond the scope of testing the performance of trading models. For example, Gençay et al. (2002, 2003) employ a widely used commercial realtime trading model as a diagnostic tool to evaluate the statistical properties of foreign exchange rates. They consider that the trading model on real data outperforms some sophisticated statistical models implying that these latter are not relevant for capturing the data generating process. They add that in financial markets, the data generating process is a complex network of layers where each layer corresponds to a particular frequency. In our paper we choose to deal with high frequency data, believing that all our results are sensitive to the time scale. The results carried out from 1 h or 30 min time scale are certainly different from those triggered by 5 min frequency. However, the goal of our study is to analyze the performance of some chart pattern at a specific time scale without generalizing our results to other frequencies. Our 1 Once computing the ratio Pt=Pt where Pt corresponds to the mean of prices preceding the moment t, the relative strength trading rule consists in buying the asset if the ratio is bigger than a particular value and selling it when the ratio reaches a specific threshold. 2 This strategy consists in buying the asset at the beginning of a certain period and keeping it until the end. 3 This chart pattern is defined in Section 3.2.
The performance analysis of chart patterns 203 choice of high frequency data, as emphasized by Gençay et al. (2003), is motivated by two main reasons. First, any position recommended by our strategy (defined below in subsection 3.3.2) have to be closed quickly within a short period following the chart completion. The stop-loss objectives need to be satisfied and the high frequency data provides an appropriate platform for this requirement. Second, the trading positions and strategies, can only be replicated with a high statistical degree of accuracy by using high frequency data in a real time trading model. However in practice, technical analysts often combine high and low time scales in order to monitor their positions in the short (5-min to 1 h) and long run (one day to one month). 3 Methodology The methodology adopted in this paper consists in identifying regularities in the time series of currency prices by extracting nonlinear patterns from noisy data. We take into consideration significant price movements which contribute to the formation of a specific chart pattern and we ignore random fluctuations considered as noise. We do this by adopting a smoothing technique in order to average out the noise. The smoothing technique allows to identify significant price movements which are only characterized by sequences of extrema. In the first subsection we present two methods used to identify local extrema. Then, we explain the pattern recognition algorithm which is based on the quantitative definition of chart patterns. In the third subsection, we present the two criteria chosen for the analysis of the detected charts: predictability and profitability. The last subsection is dedicated to the way we compute the statistical significance of our results. It is achieved by running a Monte Carlo simulation. 3.1 Identification of local extrema Each chart pattern can be characterized by a sequence of local extrema, that is by a sequence of alternate maxima and minima. Two methods are used to detect local extrema. The first method, largely used in the literature, is based on close prices, i.e. prices which take place at the end of each time interval. The second method, which is one of the contribution of this paper to the literature, is built on the highest and the lowest prices in the same time intervals. We examine the usefulness of using high and low prices in the identification process of chart patterns. Taking these prices into account is more in line with practice as dealers use bars or candlestick charts to build their technical trading rules. 4 Moreover, Fiess and MacDonald (2002) show that high and low prices carry useful information about the level of future exchange rates. 4 Béchu and Bertrand (1999) stipulate that line charts are imprecise because they do not display all the information available as they are only based upon close prices of each time interval. In contrast, bar charts and candlesticks involve the high, low, open and close prices of 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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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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Page 162 and 163: 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 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 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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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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