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222 References W. B. Omrane, H. V. Oppens Allen H, Taylor MP (1992) The use of technical analysis in the foreign exchange market. J Int Money Financ 11:304–314 Andrada-Felix J, Fernandez-Rodriguez F, Sosvilla-Rivero S (1995) Technical analysis in the Madrid stock exchange, Foundation for Applied Economic Research (FEDEA) Working Paper, No. 99–05 Béchu T, Bertrand E (1999) L’analyse technique: pratiques et méthodes, Economica, gestion Blume L, Easley D, O’Hara M (1994) Market statistics and technical analysis: the role of volume. J Finance 49(1):153–181 Brock W, Lakonishok J, LeBaron B (1992) Simple technical trading rules and the stochastic properties of stock returns. J Finance 47:1731–1764 Chang PHK, Osler CL (1999) Methodical madness: technical analysis and the irrationality of exchange-rate forecasts. Econ J 109:636–661 Cheung Y-W, Wong CY-P (1999) Foreign exchange traders in Hong Kong, Tokyo and Singapore: a survey study. Adv Pac Basin Financ Mark 5:111–134 Chordia T, Roll R, Subrahmanyam A (2002) Evidence on the speed of Convergence to Market Efficiency, UCLA Working paper Dacorogna MM, Müller UA, Nagler RJ, Olsen RB, Pictet OV (1993) A geographical model for the daily and weekly seasonal volatility in the foreign exchange market. J Int Money Financ 12:413–438 Dacorogna M, Gençay R, Müller UA, Pictet OV (2001a) Effective return, risk aversion and drawdowns. Physica A 289:229–248 Dacorogna M, Gençay R, Müller UA, Olsen RB, Pictet OV (2001b) An introduction to high frequency finance, Academic Danielsson J, Payne R (2002) Real trading patterns and prices in the spot foreign exchange markets. J Int Money Financ 21:203–222 Dempster MAH, Jones CM (1998a) Can technical pattern trading be profitably automated? 1. Channel, Centre for Financial Research, Judge Institute of Management Studies, University of Cambridge, working paper Dempster MAH., Jones CM (1998b) Can technical pattern trading be profitably automated? 2. The head & shoulders, Centre for Financial Research, Judge Institute of Management Studies, University of Cambridge, working paper Detry PJ (2001) Other evidences of the predictive power of technical analysis: the moving averages rules on European indexes, EFMA 2001 Lugano Meetings Dooley M, Shafer J (1984) Analysis of short-run exchange rate behavior: March 1973 to 1981. Floating exchange rates and the state of world trade and payments, 43–70 Fiess N, MacDonald R (2002) Towards the fundamentals of technical analysis: analysing the information content of high, low and close prices. Econ Model 19:353–374 Gençay R (1998) The predictability of security returns with simple technical trading rules. J Empir Finance 5:347–359 Gençay R (1999) Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules. J Int Econ 47:91–107 Gençay R, Stengos T (1997) Technical trading rules and the size of the risk premium in security returns. Stud Nonlinear Dyn Econ 2:23–34 Gençay R, Ballocchi G, Dacorogna M, Olsen R, Pictet OV (2002) Real-time trading models and the statistical properties of foreign exchange rates. Int Econ Rev 43:463–491 Gençay R, Dacorogna M, Olsen R, Pictet OV (2003) Foreign exchange trading models and market behavior. J Econ Dyn Control 27:909–935 Jensen MC (1970) Random walks and technical theories: some additional evidence. J Finance 25 (2):469–482 LeBaron B (1999) Technical trading rule profitability and foreign exchange intervention. J Int Econ 49:125–143 Levich RM, Thomas LR (1993) The significance of the trading-rule profits in the foreign exchange market: a bootstrap approach. J Int Money Financ 12:1705–1765 Levy RA (1971) The predictive significance of five-point patterns. J Bus 41:316–323 Lo AW, Mamaysky H, Wang J (2000) Foundations of technical analysis: computational algorithms, statistical inference, and empirical implementation. J Finance 56:1705–1765
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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
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Modelling financial transaction pri
Page 177 and 178: 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 226 and 227: 220 C.5. Triple bottom (TB) TB is c
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
Page 276 and 277: 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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