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Walid Ben Omrane . Hervé Van Oppens The performance analysis of chart patterns: Monte Carlo simulation and evidence from the euro/dollar foreign exchange market Abstract We investigate the existence of chart patterns in the euro/dollar intradaily foreign exchange market. We use two identification methods of the different chart patterns: one built on 5-min close prices only, and one based on both 5-min low and high prices. We look for twelve types of chart patterns and we study the detected patterns through two criteria: predictability and profitability. We run a Monte Carlo simulation to compute the statistical significance of the obtained results. We find an apparent existence of some chart patterns in the currency market. More than one half of detected charts present a significant predictability. Nevertheless, only two chart patterns imply a significant profitability which is however too small to cover the transaction costs. The second extrema detection method provides higher but riskier profits than the first one. Keywords Foreign exchange market . Chart patterns . High frequency data . Technical analysis JEL Classification C13 . C14 . F31 1 Introduction Technical analysis is the oldest method for analyzing market behavior. It is defined by Murphy (1999) as the study of market action, primarily through the use of charts, for the purpose of forecasting future price trends. The term ‘market action’ includes three main sources of information available to the technician: price, volume and open interest. Béchu and Bertrand (1999) distinguish three categories of technical analysis. Traditional analysis is entirely based on the study of charts and the location of technical patterns like the Head and Shoulders pattern. Modern W. B. Omrane (*) . H. V. Oppens IAG Business School, Université Catholique de Louvain, 1 place des Doyens, 1348 Louvain-la-Neuve, Belgium E-mail: benomrane@fin.ucl.ac.be
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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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248 J. M. Rodríguez-Poo et al. (B.
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250 J. M. Rodríguez-Poo et al. (A.
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Anthony S. Tay . Christopher Ting I
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Intraday stock prices, duration, an
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Table 3 (continued) Volume (percent
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David Veredas Macroeconomic surpris
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Macroeconomic surprises and short-t
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Table A2 ISM • ✓ 10 min −0.09
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Table A4 Unemployment rate • ✓
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Valeri Voev Dynamic modelling of la
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Dynamic modelling of large-dimensio
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