1 Bask M. Dimensions and Lyapunov exponents from exchange rateseries [J]. Chaos, Solitons and Fractals, 1996, 7:2199-2214.2 Bask M. A positive Lyapunov exponent in Swedish exchange rates? [J]. Chaos, Solitons and Fractals, 2002, 14: 1295-1304.3 Schwartz B, Yousefi S. On complex behavior and exchange ratedynamics [J]. Chaos, Solitons and Fractals, 2003, 18:503-523.4 Jonsson M. Studies in business cycles [D]. Stockholm: Institute forInternational Economic Studies, Stockholm University, 1997.5 Serletis A, Gogas P. Chaos in East European black market exchangerates [J]. Research in Economics, 1997, 5(51): 359-385.6 Gencay R, Dechert W D. An algorithm for the n Lyapunov exponentsof an n-dimensional unknown dynamical system [J]. PhysicaD, 1992, 59: 142-157.7 D\'\iaz F A, Grall-Carles P, Mangas E L. Nonlinearities in the exchangerates returns and volatility [J]. Physica A, 2002, 316:469-482.8 Cao L, Soofi A. Nonlinear deterministic forecasting of daily dollarexchange rates [J]. International Journal of Forecasting,1999, 15: 421-430.9 Soofi A S, Galka A. Measuring the complexity of currency markets byfractal dimension analysis [J]. International Journal ofTheoretical and Applied Finance, 2003, 6(6): 553-563.10 Kugiumtzis D. On the reliability of the surrogate data test fornonlinearity in the analysis of noisy time series [J]. International Journal of Bifurcation and Chaos, 2001, 11(7):1881-1896.11 Strozzi F, Comenges J M Z. Towards a non-linear trading strategy forfinancial time series [J]. Chaos, Solitons and Fractals,2006, 28: 601-615.12 Chiarella C, Peat M, Stevenson M. Detecting and modelingnonlinearity in flexible exchange rate time series [J]. AsiaPacific Journal of Management, 1994, 11(2): 159-186.13 Hsieh D A. Testing for nonlinear dependence in daily foreignexchange rates [J]. Journal of Business, 1989, 62(3):339-368.14 Cecen A A, Erkal C. Distinguishing between stochastic anddeterministic behavior in foreign exchange rate returns: Furtherevidence [J]. Economics Letters, 1996, 51: 323-329.15 Cecen A A, Erkal C. Distinguishing between stochastic anddeterministic behavior in high frequency foreign exchange ratereturns: Can non-linear dynamics help forecasting? [J] International Journal of Forecasting, 1996, 12: 465-473.16 Lai D. Comparison study of AR models of the Canadian lynx data: Aclose look at BDS statistic [J]. Computational Statistics& Data Analysis, 1996, 22: 409-423.17 Chu P K K. Study on the non-random and chaotic behavior of Chineseequities market [J]. Review of Pacific Basin Financial Marketsand Policies, 2003, 6(2): 199-222.18 Brock W A, Dechert W D, Scheinkman J A, et al. A test forindependence based on the correlation dimension [R]. Madison:University of Wisconsin, 1987.19 Brock W A, Hsieh D A, Lebaron B. Nonlinear dynamics, chaos andinstability: Statistical theory and economic evidence [M].Cambridge, USA: MIT Press, 1991.20 Barahona M, Poon C S. Detection of nonlinear dynamics in short,noisy time series [J]. Nature, 1996, 381: 215-217.21 Lei M, Meng G, Feng Z. Security analysis of chaotic communicationsystems based on Volterra-Wiener-Korenberg model [J]. Chaos,Solitons and Fractals, 2006, 28: 264-270.22 Theiler J, Eubank S, Longtin A, et al. Testing for nonlinearity intime series: The method of surrogate data [J]. Physica D,1992, 58: 77-94.23 Theiler J, Prichard D. Constrained-realization Monte-Carlo methodfor hypothesis testing [J]. Physica D, 1996, 94:221-235.24 Lima E J A, Tabak B M. Testing for inefficiency in emerging marketsexchange rates [J]. Chaos, Solitons and Fractals, 2007, 33: 617-622. |