Poitras, Geoffrey and Heaney, John (2015): Classical Ergodicity and Modern Portfolio Theory. Published in: Chinese Journal of Mathematics , Vol. 2015, No. Article ID 737905, (2015): pp. 1-17.
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Abstract
What role have theoretical methods initially developed in mathematics and physics played in the progress of financial economics? What is the relationship between financial economics and econophysics? What is the relevance of the “classical ergodicity hypothesis” to modern portfolio theory?This paper addresses these questions by reviewing the etymology and history of the classical ergodicity hypothesis in 19th century statistical mechanics. An explanation of classical ergodicity is provided that establishes a connection to the fundamental empirical problem of using non-experimental data to verify theoretical propositions in modern portfolio theory.The role of the ergodicity assumption in the ex post/ex ante quandary confronting modern portfolio theory is also examined.
Item Type: | MPRA Paper |
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Original Title: | Classical Ergodicity and Modern Portfolio Theory |
English Title: | Classical Ergodicity and Modern Portfolio Theory |
Language: | English |
Keywords: | Ergodicity; Modern Portfolio Theory |
Subjects: | C - Mathematical and Quantitative Methods > C0 - General C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods G - Financial Economics > G0 - General |
Item ID: | 113952 |
Depositing User: | Prof. Geoffrey Poitras |
Date Deposited: | 08 Aug 2022 10:21 |
Last Modified: | 08 Aug 2022 10:21 |
References: | [1] H.Markowitz, “Portfolio selection,” The Journal of Finance, vol. 7, pp. 77–91, 1952. [2] J. Y. Campbell, “Understanding risk and return,” Journal of Political Economy, vol. 104, no. 2, pp. 298–345, 1996. [3] P. Mirowski, “Physics and the ‘marginalist revolution’,” Cambridge Journal of Economics, vol. 8, no. 4, pp. 361–379, 1984. [4] F. Black and M. Scholes, “The pricing of options and corporate liabilities,” Journal of Political Economy, vol. 81, no. 3, pp. 637– 659, 1973. [5] B. M. Roehner, Patterns of Speculation, Cambridge University Press, Cambridge, UK, 2002. [6] F. Jovanovic and C. Schinckus, “Econophysics: a new challenge for financial economics?” Journal of the History of Economic Thought, vol. 35, no. 3, pp. 319–352, 2013. [7] C. Sprenkle, “Warrant prices as indicators of expectations and preferences,” Yale Economic Essays, vol. 1, pp. 178–231, 1961. [8] P. Samuelson, “Rational theory of Warrant pricing,” Industrial Management Review, vol. 6, pp. 13–31, 1965. [9] R. C. Merton, “The theory of rational option pricing,” The Bell Journal of Economics and Management Science, vol. 4, no. 1, pp. 141–183, 1973. [10] M. J. Brennan and E. S. Schwartz, “A continuous time approach to the pricing of bonds,” Journal of Banking and Finance, vol. 3, no. 2, pp. 133–155, 1979. [11] L. G. Epstein and S. Ji, “Ambiguous volatility and asset pricing in continuous time,” Review of Financial Studies, vol. 26, no. 7, pp. 1740–1786, 2013. [12] C. Schinckus, “Is econophysics a new discipline? The neopositivist argument,” Physica A, vol. 389, no. 18, pp. 3814–3821, 2010. [13] J. L. McCauley, Dynamics of Markets: Econophysics and Finance, Cambridge University Press, Cambridge, UK, 2004. [14] B. B. Mandelbrot, Fractals and Scaling in Finance: Discontinuity, Concentration, Risk, Springer, New York, NY, USA, 1997. [15] B. B. Mandelbrot and R. L. Hudson, The (Mis)Behavior of Markets: A Fractal View of Risk, Ruin, and Reward, Basic Books, New York, NY, USA, 2004. [16] Ergodic Theory, Encyclopedia of Mathematics, Springer, New York, NY, USA, 2002. [17] J. von Neumann, “A proof of the quasi-ergodic hypothesis,” Proceedings of the National Academy of Sciences of the United States of America, vol. 18, no. 3, pp. 255–263, 1932. [18] G. Birkhoff, “Proof of the ergodic theorem,” Proceedings of the National Academy of Sciences of the United States of America, vol. 17, no. 12, pp. 656–660, 1931. [19] J. L. Doob, Stochastic Processes, John Wiley & Sons, New York, NY, USA, 1953. [20] G.W.Mackey, “Ergodic theory and its significance for statistical mechanics and probability theory,” Advances in Mathematics, vol. 12, no. 2, pp. 178–268, 1974. [21] A. Kolmogorov, Foundations of the Theory of Probability, Springer, Berlin, Germany, 1933, (German), English edition, Chelsea, New York, NY, USA, 1950. [22] L. Arnold, Random Dynamical Systems, Springer Monographs in Mathematics, Springer, New York, NY, USA, 1998. [23] P.Mirowski, More Heat Than Light: Economics as Social Physics, Physics as Nature’s Economics, Cambridge University Press, Cambridge, UK, 1989. [24] E. R. Weintraub, How Economics Became a Mathematical Science, Science and Cultural Theory, Duke University Press, Durham, NC, USA, 2002. [25] J. C.Maxwell, “On the dynamical theory of gases,” Philosophical Transactions of the Royal Society, vol. 157, pp. 49–88, 1867. [26] N.Weiner, “The Ergodic theorem,” Duke Mathematical Journal, vol. 5, no. 1, pp. 1–18, 1939. [27] P. A. Samuelson, “Optimality of sluggish predictors under ergodic probabilities,” International Economic Review, vol. 17, no. 1, pp. 1–7, 1976. [28] U. Horst and J. Wenzelburger, “On non-ergodic asset prices,” Economic Theory, vol. 34, no. 2, pp. 207–234, 2008. [29] A. Dixit and R. Pindyck, Investment under Uncertainty, Princeton University Press, Princeton, NJ, USA, 1994. [30] E. Elton and M. Gruber, Modern Portfolio Theory and Investment Analysis, Wiley, New York, NY,USA, 2nd edition, 1984. [31] P. J. Dhrymes, Econometrics: Statistical Foundations and Applications, Springer, New York, NY, USA, 1974. [32] H. Theil, Principles of Econometrics,Wiley, New York, NY, USA, 1971. [33] R. F. Engle and C. W. Granger, “Co-integration and error correction: representation, estimation, and testing,” Econometrica, vol. 55, no. 2, pp. 251–276, 1987. [34] M.-C. Beaulieu, J.-M. Dufour, and L. Khalaf, “Identification, robust estimation and testing of the zero-beta CAPM,” Review of Economic Studies, vol. 80, no. 3, pp. 892–924, 2013. [35] D. F. Hendry, Dynamic Econometrics, Oxford University Press, Oxford, UK, 1995. [36] D. A. Dickey and W. A. Fuller, “Distribution of the estimators for autoregressive time series with a unit root,” Journal of the American Statistical Association, vol. 74, pp. 427–431, 1979. [37] G. Kapetanios and Y. Shin, “Testing the null hypothesis of nonstationary long memory against the alternative hypothesis of a nonlinear ergodic model,” Econometric Reviews, vol. 30, no. 6, pp. 620–645, 2011. [38] M. Bonomo, R. Garcia, N. Meddahi, and R. Tedongap, “Generalized disappointment aversion, long-run volatility risk, and asset prices,” Review of Financial Studies, vol. 24, no. 1, pp. 82– 122, 2011. [39] P. R. Halmos, “Measurable transformations,” Bulletin of the American Mathematical Society, vol. 55, pp. 1015–1034, 1949. [40] P. Davidson, “Is probability theory relevant for uncertainty? A post Keynesian perspective,” Journal of Economic Perspectives, vol. 5, no. 1, pp. 129–143, 1991. [41] E. Dimson, P. Marsh, and M. Staunton, Triumph of the Optimists, 101 Years of Global Investment Returns, Princeton University Press, Princeton, NJ, USA, 2002. [42] G. M. Constantinides, “Rational asset prices,” Journal of Finance, vol. 57, no. 4, pp. 1567–1591, 2002. [43] C. Chiarella, X.-Z. He, D.Wang, and M. Zheng, “The stochastic bifurcation behaviour of speculative financial markets,” Physica A: Statistical Mechanics and Its Applications, vol. 387, no. 15, pp. 3837–3846, 2008. [44] H. Crauel, P. Imkeller, and M. Steinkamp, “Bifurcations of one-dimensional stochastic differential equations,” in Stochastic Dynamics, H. Crauel and M. Gundlach, Eds., chapter 2, pp. 27– 47, Springer, New York, NY, USA, 1999. [45] W. Feller, “Diffusion processes in one dimension,” Transactions of the American Mathematical Society, vol. 77, pp. 1–31, 1954. [46] E. Wong, “The construction of a class of stationary Markoff processes,” in Proceedings of the 16th Symposium on Applied Mathematics, R. Bellman, Ed., American Mathematical Society, Providence, RI, USA, 1964. [47] I. Gihman and A. Skorohod, The Theorv of Stochastic Processes, vol. 1–3, Springer, New York, NY, USA, 1979. [48] H. Risken, The Fokker-Planck Equation:Methods of Solution and Applications, vol. 18 of Springer Series in Synergetics, Springer, New York, NY, USA, 1989. [49] V. Linetsky, “On the transition densities for reflected diffusions,” Advances in Applied Probability, vol. 37,no. 2, pp.435–460, 2005. [50] P. W. Berg and J. L. McGregor, Elementary Partial Differential Equations, Holden-Day, San Francisco, Calif, USA, 1966. [51] E. Hille, Lectures on Ordinary Differential Equations, Addison- Wesley, London, UK, 1969. [52] G. Birkhoff and G.-C. Rota, Ordinary Differential Equations, John Wiley & Sons, New York, NY, USA, 4th edition, 1989. [53] S. Karlin and H. M. Taylor, A Second Course in Stochastic Processes, Academic Press, New York, NY, USA, 1981. [54] D. R. Cox and H. D. Miller, The Theory of Stochastic Processes, Chapman & Hall, London, UK, 1965. [55] L.P.Hansen, J. A. Scheinkman, and N. Touzi, “Spectral methods for identifying scalar diffusions,” Journal of Econometrics, vol. 86, no. 1, pp. 1–32, 1998. [56] Y. A¨ıt-Sahalia, “Transition densities for interest rates and other nonlinear diffusions,” Journal of Finance, vol. 54, pp. 1361–1395, 1999. [57] D. Veerstraeten, “The conditional probability density function for a reflected Brownian motion,” Computational Economics, vol. 24, no. 2, pp. 185–207, 2004. [58] L. Cobb, “Stochastic catastrophe models and multimodal distributions,” Behavioral Science, vol. 23, no. 5, pp. 360–374, 1978. [59] L. Cobb, “The multimodal exponential families of statistical catastrophe theory,” in Statistical Distributions in Scientific Work, Vol. 4 (Trieste, 1980), C. Taillie, G. Patil, and B. Baldessari, Eds., vol. 79 of NATO Adv. Study Inst. Ser. C: Math. Phys. Sci., pp. 67–90, Reidel, Dordrecht, The Netherlands, 1981. [60] H. Crauel and F. Flandoli, “Additive noise destroys a pitchfork bifurcation,” Journal of Dynamics and Differential Equations, vol. 10, no. 2, pp. 259–274, 1998. [61] R. A. Fisher, “On the mathematical foundations of theoretical statistics,” Philosophical Transactions of the Royal Society A, vol. 222, pp. 309–368, 1921. [62] L. Cobb, P. Koppstein, and N. H. Chen, “Estimation and moment recursion relations for multimodal distributions of the exponential family,” Journal of the American Statistical Association, vol. 78, no. 381, pp. 124–130, 1983. [63] J. Elliott, “Eigenfunction expansions associated with singular differential operators,” Transactions of the American Mathematical Society, vol. 78, pp. 406–425, 1955. [64] W. Horsthemke and R. Lefever, Noise-Induced Transitions, vol. 15 of Springer Series in Synergetics, Springer, Berlin, Germany, 1984. [65] A.W.Matz, “Maximum likelihood parameter estimation for the quartic exponential distribution,” Technometrics, vol. 20, no. 4, pp. 475–484, 1978. [66] W. S. Jevons, The Principles of Science, Dover Press, New York, NY, USA, 2nd edition, 1877, (Reprint 1958). [67] S. Brush, Statistical Physics and the Atomic Theory of Matter: From Boyle and Newton to Landau and Onsager, Princeton University Press, Princeton, NJ, USA, 1983. [68] C. Cercignani, Ludwig Boltzmann: The Man who Trusted Atoms, Oxford University Press, Oxford, UK, 1998. [69] S. Brush, The Kind of Motion We Call Heat: A History of the Kinetic Theory of Gases in the 19th Century, North Holland, Amsterdam, The Netherlands, 1976. [70] G. Gallavotti, “Ergodicity, ensembles, irreversibility in Boltzmann and beyond,” Journal of Statistical Physics, vol. 78, no. 5-6, pp. 1571–1589, 1995. [71] P. Ehrenfest, Begriffliche Grundlagen der statistischen Auffassung in der Mechanik, B.G. Teubner, Leipzig, Germany, 1911. [72] M. Reed and B. Simon, Methods of Modern Mathematical Physics,Academic Press,New York,NY,USA, 2nd edition, 1980. [73] N. Georgescu-Roegen, The Entropy Law and the Economic Process,Harvard University Press, Cambridge,Mass,USA, 1971. [74] A.Medio, “Ergodic theory of nonlinear dynamics,” in Nonlinear Dynamical Systems in Economics, M. Lines, Ed., vol. 476 of CISM International Centre for Mechanical Sciences, pp. 67–101, Springer, Vienna, Austria, 2005. [75] C. A. Ball and A. Roma, “Detecting mean reversion within reflecting barriers: application to the European Exchange Rate Mechanism,” Applied Mathematical Finance, vol. 5, no. 1, pp. 1– 15, 1998. [76] F. de Jong, “A univariate analysis of European monetary system exchange rates using a target zone model,” Journal of Applied Econometrics, vol. 9, no. 1, pp. 31–45, 1994. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/113952 |