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Can the Stock Market be Linearized?

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  • Politis, D N

Abstract

The evolution of financial markets is a complicated real-world phenomenon that ranks at the top in terms o fdifficulty of modeling and/or prediction. One reason for this difficulty is the well-documented nonlinearity that is inherently at work. The state-of-the-art on the nonlinear modeling of financial returns is given by the popular ARCH (Auto-Regressive Conditional Heteroskedasticity) models and their generalization but they all have their short-comings. Foregoing the goal of finding the "best" model, we propose an exploratory, model-free approach in trying to understand this difficult type of data. In particular, we propose to transform the problem into a more manageable setting such as the setting of linearity. The form and properties of such a transformation are given, and the issue of one-step-ahead prediction using the new approach is explicitly addressed.

Suggested Citation

  • Politis, D N, 2006. "Can the Stock Market be Linearized?," University of California at San Diego, Economics Working Paper Series qt8th5q5hq, Department of Economics, UC San Diego.
    • Handle: RePEc:cdl:ucsdec:qt8th5q5hq
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    References listed on IDEAS

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    1. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    2. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    3. Politis, Dimitris N., 2004. "A heavy-tailed distribution for ARCH residuals with application to volatility prediction," University of California at San Diego, Economics Working Paper Series qt7r89639x, Department of Economics, UC San Diego.
    4. Dimitris N. Politis, 2004. "A Heavy-Tailed Distribution for ARCH Residuals with Application to Volatility Prediction," Annals of Economics and Finance, Society for AEF, vol. 5(2), pages 283-298, November.
    5. Melvin J. Hinich, 1982. "Testing For Gaussianity And Linearity Of A Stationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 3(3), pages 169-176, May.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Politis, Dimitris N., 2003. "Model-Free Volatility Prediction," University of California at San Diego, Economics Working Paper Series qt0648834b, Department of Economics, UC San Diego.
    8. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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