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Financial Markets Analysis: Can Nonlinear Science Contribute?

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Intelligence for Nonlinear Dynamics and Synchronisation

Part of the book series: Atlantis Computational Intelligence Systems ((ATLANTISCIS,volume 3))

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Abstract

Instability, complexity and chaotic behavior are distinguishing features of financial markets. As a result, financial analysis has greatly benefited from the application of concepts and tools from nonlinear science. In addition, the need to analyze huge amounts of financial data necessitates the utilization of computer-intensive methods. This chapter aims to provide an overview of the diverse research domains, in financial analysis, where nonlinear science, combined with computational intelligence, could find application.

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Bibliography

  1. Y.S. Abu-Mostafa, A.F. Atiya, M. Magdon-Ismail and H. White, “Introduction to the Special Issue on Neural Networks in Financial Engineering”, IEEE Transaction on Neural Networks, 12, (2001).

    Google Scholar 

  2. Y. Achdou and O. Pirroneau, Computational Methods for Option Pricing, (SIAM 2005).

    Google Scholar 

  3. W. Allegretto, Y. Lin, and H. Yang, “Finite Element Error Estimates for a Nonlocal Problem in American Option Valuation”, SIAM Journal of Numerical Analysis, 39, (2001).

    Google Scholar 

  4. Altman, (1990).

    Google Scholar 

  5. P. Amster, C.G. Averbuj, M.C. Mariani, and D. Rial, “A Black-Scholes Option Pricing Model with Transaction Costs”, Journal of Mathematical Analysis and Applications, 303, (2005).

    Google Scholar 

  6. N. Aslanidis, D. Osborn and M. Sensier, “Co-movements between US and UK stock prices: The Roles of Macroeconomic Information and Time-Varying Conditional Correlations”, Manchester University Discussion Papers, (2008).

    Google Scholar 

  7. M. Avellaneda, A. Careli, and F. Stella, “A Bayesian Approach for Constructing Implied Volatility Surfaces Through Neural Networks”, The Journal of Computational Finance 4 (2000).

    Google Scholar 

  8. G. Barles and H.M. Soner, “Option Pricing with Transaction Costs and a Nonlinear Black- Scholes Equation”, Finance and Stochastics, 2, (1998).

    Google Scholar 

  9. E.G. Baranoff, T.W. Sager, and T.S. Shively, “A Semiparametric Stochastic Spline Model as a Managerial Tool for Potential Insolvency”, The Journal of Risk and Insurance, 67, (2000).

    Google Scholar 

  10. Y. Bengio, V. Lauzon, and R. Ducharme, “Experiments on the Application of IOHMMs to Model Financial Returns Series”, IEEE Transactions on Neural Networks, 12, (2001).

    Google Scholar 

  11. J. Beran and Y. Feng, “Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent errors”, Annals of the Institute of Statistical Mathematics, 54, (2001).

    Google Scholar 

  12. S. Bianconcini, “A Reproducing Kernel Perspective of Smoothing Spline Estimators”, University of Bologna Research Paper, (2008).

    Google Scholar 

  13. F. Black and M. Scholes, “The Pricing of Options and Corporate Liabilities”, The Journal of Political Economy, 81, (1973).

    Google Scholar 

  14. F. Black, “How to Use the Holes in Black-Scholes” in R. Kolb (ed.), Practical Readings in Financial Derivatives, (Cornwall: Blackwell 1998).

    Google Scholar 

  15. T.P. Bollerslev, “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 31, (1986).

    Google Scholar 

  16. G. Box and G. Jenkins, Time Series Analysis: Forecasting and Control, (Holden-Day 1976).

    Google Scholar 

  17. R. A. Brealey and S.C. Myers, Principles of Corporate Finance, (McGraw-Hill 2000).

    Google Scholar 

  18. T. Brenner, “Agent Learning Representation: Advice on Modeling Economic Learning” in L. Tesfatsion, K. Judd (eds.), Handbook of Computational Economics, (Elsevier 2006).

    Google Scholar 

  19. U. Cetin, R. Jarrow, and P. Protter, “Liquidity Risk and Arbitrage Pricing Theory”, Finance and Stochastics, 8, (2004).

    Google Scholar 

  20. C. Chatfield, The Analysis of Time Series: An Introduction, (Chapman&Hall 2004).

    Google Scholar 

  21. L. Chollete, A. Heinen, and A. Valdesogo, “Modeling International Financial Returns with a Multivariate Regime Switching Copula”, Discussion Paper, (2008).

    Google Scholar 

  22. D. Colander (ed.), Post-Walrasian Macroeconomics, (Cambridge University Press 2006).

    Google Scholar 

  23. Company et al., (2008).

    Google Scholar 

  24. R. Cont, “Empirical Properties of Asset Returns: Stylized Facts and Statistical Features”, Quantitative Finance, 1, (2001).

    Google Scholar 

  25. R. Cont, “Long Range Dependence in Financial Markets” in E. Lutton and J. Vehel (eds.), Fractals in Engineering, (Springer 2005).

    Google Scholar 

  26. G. Cornuejols and R. T¨ut¨unc¨u, Optimization Methods in Finance, (Cambridge University Press 2007).

    Google Scholar 

  27. D.J. Duffy, Finite Difference Methods in Financial Engineering (John Wiley&Sons 2006a).

    Google Scholar 

  28. D.J. Duffy, “Agent-Based Models and Human Subject Experiments” in L. Tesfatsion, K. Judd (eds.) Handbook of Computational Economics, (Elsevier 2006b).

    Google Scholar 

  29. Dupire, (1992).

    Google Scholar 

  30. B. Dupire, “Pricing with a Smile”, Risk, 7, (2002).

    Google Scholar 

  31. E.J. Elton, M.J. Gruber, S.J. Brown, and W.N. Goetzman, Modern Portfolio Theory and Investment Analysis, (John Wiley & Sons 2007).

    Google Scholar 

  32. R.F. Engle, “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation”, Econometrica, 50, (1982).

    Google Scholar 

  33. R.F. Engle and J. Mezrich, “GARCH for Groups”, Risk (1996).

    Google Scholar 

  34. E. Fama, “Efficient Capital Markets: A Review of Theory and Empirical Work”, Journal of Finance, 40, (1970).

    Google Scholar 

  35. J. Fan and I. Gijbels, “Data-driven bandwidth selection in local polynomial fitting: variable bandwidth and spatial adaptation”, Journal of the Royal Statistical Society B, 57, (1995).

    Google Scholar 

  36. J. Fan and Q. Yao, Nonlinear Time Series, (Springer 2005).

    Google Scholar 

  37. R. Frey and P. Patie, “Risk Management for Derivatives in Illiquid Markets: A Simulation Studies”, EFA Conference Berlin, (2002).

    Google Scholar 

  38. L.R. Glosten, R. Jagannathan, and D.E. Runkle, “On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks”, Journal of Finance, 48, (1993).

    Google Scholar 

  39. D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, (Addison- Wesley 1989).

    Google Scholar 

  40. G. Gonzalez-Rivera, “Smooth-Transition GARCH models”, Studies in Nonlinear Dynamics & Econometrics, 3, (1998).

    Google Scholar 

  41. C.W. Granger, “Strategies for Modelling Nonlinear Time-Series Relationships”, The Economic Record, 69, (1993).

    Google Scholar 

  42. W.H. Greene, Econometric Analysis, (Prentice Hall 2003).

    Google Scholar 

  43. Guan, (2005).

    Google Scholar 

  44. M. Haas, S.Mittnik, andM.S. Paolella, “A New Approach toMarkov-Switching GARCHMod els”, Journal of Financial Econometrics, 2, (2004).

    Google Scholar 

  45. J.D. Hamilton, “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle”, Econometrica, 57 (1989).

    Google Scholar 

  46. J.D. Hamilton, “What’s Real About the Business Cycle?”, Federal Reserve Bank of St. Lious Review, 87, (2005).

    Google Scholar 

  47. S. Haykin, Neural Networks, (Prentice Hall, New Jersey 1999).

    Google Scholar 

  48. R.D. van der Hilst, M.V. De Hoop, P. Wang, S.H. Shim, P. Ma and L. Tenorio, “Seismostratigraphy and Thermal Structure of Earth’s Core-Mantle Boundary Region”, Science, 315, (2007).

    Google Scholar 

  49. K. Huarng and T.H. Ju, “Ratio-Based Lengths of Intervals to Improve Fuzzy Time Series Forecasting”, IEEE Transactions on Man and Cybernetics B, 36, (2006).

    Google Scholar 

  50. J.C. Hull, Risk Management and Financial Institutions, (Prentice Hall 2007).

    Google Scholar 

  51. A. Kindl, “Pricing American-Style Options by Simulation”, Financial Markets and Portfolio Management, 19, (2005).

    Google Scholar 

  52. A. Kirsch, An Introduction to the Mathematical Theory of Inverse Problems, (Springer Verlag 1996).

    Google Scholar 

  53. R.W. Kolb and J.A. Overdahl, Futures, Options and Swaps, (Blackwell 2007).

    Google Scholar 

  54. H. Krozlig, “Markov-Switching Procedures for Dating the Euro-Zone Business Cycle”, Vierteljahrshefte zur Wirtschaftsforschung, 70, (2001).

    Google Scholar 

  55. Y. Kwon and B. Moon, “A Hybrid Neurogenetic Approach for Stockk Forecasting”, IEEE Transactions on Neural Networks, 18, (2007).

    Google Scholar 

  56. B. LeBaron, Arthur, and Palmer, Journal of Economic Dynamics & Control, Vol. 23, (1999).

    Google Scholar 

  57. B. LeBaron, “Agent-Based Computational Finance” in L. Tesfatsion, K. Judd (eds.) Handbook of Computational Economics, (Elsevier 2006a).

    Google Scholar 

  58. B. LeBaron, “Agent-Based Financial Markets: Matching Stylized Facts with Style” in Colander (Ed.) Post-Walrasian Economics, (Cambridge University Press 2006b).

    Google Scholar 

  59. A. Lendasse, E. de Bodt, V. Wertz and M. Verleysen, “Non-linear Financial Time Series Forecasting – Application to the Bel 20 StockMarket Index”, European Journal of Economic and Social Systems, 14, (2000).

    Google Scholar 

  60. A.W. Lo and A.C. MacKinlay, A Non-Random Walk Down the Wall-Street, (Princeton University Press 1999).

    Google Scholar 

  61. A.W. Lo, H.Mamaysky, and J.Wang, “Foundations of Technical Analysis”, Journal of Finance, 55, (2000).

    Google Scholar 

  62. M. Magdon-Ismail, “The Equivalent Martingale Measure: An Introduction to Pricing Using Expectations”, IEEE Transactions on Neural Networks, 12, (2001).

    Google Scholar 

  63. A. Mas-Collel, M.D. Whinston, and J.R. Green, Microeconomic Theory, (Oxford University Press 1995).

    Google Scholar 

  64. B.B.Mandelbrot, “The variation of certain speculative prices”, Journal of Business, 36, (1963).

    Google Scholar 

  65. G. McQueen and K. Vorkink, “Whence GARCH? A preference-based explanation for conditional volatility”, Review of Financial Studies, 17, (2004)

    Google Scholar 

  66. R. Merton, “Rational Theory of Option Pricing”, Bell Journal of Economics and Management Science, 4 (1973)

    Google Scholar 

  67. J.F. Muth, “Rational Expectations and the Theory of Price Movements”, Econometrica, (1961).

    Google Scholar 

  68. E.A. Nadaraya, “On Estimating Regression”, Theory of Probability and Its Applications, 9, (1964).

    Google Scholar 

  69. C. Necula, “A Framework for Derivative Pricing in the Fractional Black-Scholes Market”, SSRN Working Paper (2007).

    Google Scholar 

  70. W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling, Numerical Recipes in C: The Art of Scientific Computing, (Cambridge University Press 1988).

    Google Scholar 

  71. B.K. Ray and R.S. Tsay, “Bandwidth Selection for Kernel Regression with Long-Range De pendence”, Biometrika, 84, (1997).

    Google Scholar 

  72. D. Ryta and B. Gabrys, “Neural Network Ensembles for Time Series Prediction”, International Conference on Neural Networks, (2007).

    Google Scholar 

  73. P. A. Samuelson, “Proof that Properly Anticipated Prices Fluctuate Randomly”, IndustrialManagement Review, 6 (1965).

    Google Scholar 

  74. T. Sargent, Bounded Rationality in Macroeconomics, (Oxford University Press 1993).

    Google Scholar 

  75. C. Schittenkopf and G. Dorffner, “Risk-Neutral Extraction of Option Prices: Improved Pricing with Mixture Density Networks”, IEEE Transactions on Neural Networks, 12, (2001).

    Google Scholar 

  76. E. Screpanti and S. Zamagni, An Outline of the History of Economic Thought, (Oxford University Press 2005).

    Google Scholar 

  77. R. Shiller, “From Efficient Market Theory to Behavioral Finance”, Journal of Economic Perspectives, 17, (2003).

    Google Scholar 

  78. S. Singh, “A Long Memory Pattern Modeling and Recognition System for Financial Forecasting”, Pattern Analysis and Applications, 2, (1999).

    Google Scholar 

  79. A.S. Tay and K.F.Wallis, “Density Forecasting: A Survey”, Journal of Forecasting, 19, (2000).

    Google Scholar 

  80. F.E.H. Tay and L.J. Cao, “Application of Support Vector Machines in Financial Time Series Forecasting”, Omega, 29, (2001).

    Google Scholar 

  81. J.W. Taylor and R. Buizza, “Density Forecasting forWeather Derivatives Pricing”, International Journal of Forecasting, 22, (2006).

    Google Scholar 

  82. A. Vouldis, C.N.Kechribaris, C., Maniatis, T., Nikita, K., and Uzunoglu, N., “Investigating the enhancement of three-dimensional diffraction tomography by using multiple illumination planes”, Journal of the Optical Society of America A, 22, (2005).

    Google Scholar 

  83. G.S. Watson, “Smooth Regression Analysis”, Sankhya, 26, (1964).

    Google Scholar 

  84. J.R. Woldberg, Expert Trading Systems: Modeling Financial Markets with Kernel Regression, (Wiley 2000).

    Google Scholar 

  85. J.R. Woldberg, R. Kimche, and D. Aronson, “Modeling the United States Stock Market with Kernel Regression”, IASTED Conference on Modeling and Simulation, Benaldama, Spain, (2005).

    Google Scholar 

  86. B. Xu and J. Watada, “Liquidity Impact on Sector Returns of Stock Market: Evidence from China”, Asia Pacific Journal of Finance and Banking Research, 2, (2008).

    Google Scholar 

  87. D. Zhang, Y. Liu, and Y. Jiang, “Financial Forecasting Using PatternModeling and Recognition System Based on Kernal Regression”, WSEAS Transactions on Computers, 4, (2007).

    Google Scholar 

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Authors and Affiliations

  1. UADPhilEcon, Department of Economics, University of Athens, Athens, Greece

    Angelos T. Vouldis

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Vouldis, A.T. (2010). Financial Markets Analysis: Can Nonlinear Science Contribute?. In: Intelligence for Nonlinear Dynamics and Synchronisation. Atlantis Computational Intelligence Systems, vol 3. Atlantis Press. https://doi.org/10.2991/978-94-91216-30-5_7

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