Abstract
This chapter explores whether the global markets are intrinsically unstable following the line of a growing body of the literature and inquires the possible nonlinear, particularly chaotic nature of these markets. The study finds all the markets to be deterministic. While over the first cycle nineteen markets were chaotic, the number increases to twenty four during the second one. Thus stock markets are inherently unstable; or are, at best, stable on knife-edge. Cycles and crashes are manifestations of this inherent instability: norms rather than aberrations. There is no determinate equilibrium and no external shock will be required to gear financial crisis at regular intervals which, in an integrated financial world, will reverberate across the globe in no time. The findings have significant theoretical and policy implications. The relevant equations of motion underlying the nonlinear global stock market return, no doubt can be determined, but it would be nearly impossible to forecast beyond a short time frame. Policy prescriptions, based on the presumption of linearity are likely to be ineffective when applied on a system which is actually nonlinear. At the theoretical level, a chaotic stock market puts efficient market hypothesis on trial and requires reframing of traditional asset pricing models.
You believe in a God who plays dice, and I in complete law and order..,
Albert Einstein
Albert Einstein, Letter to Max Born, September 1944.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baranger N (2001) Chaos, complexity and entropy: a physics talk for non physicists. Wesleyan University Physics Department Colloquium. http://www.necsi.org/projects/baranger/cce.html. Accessed 14 March 2011
Barnett WA, Gallant AR, Hinich MJ, Jungeilges J, Kaplan D, Jensen MJ (1997) A single-blind controlled competition among tests for non-linearity and chaos. J Econometrics 82:157–192
Brock WA, Hsieh DA, LeBaron B (1991) Nonlinear dynamics, chaos and instability: statistical theory and economic evidence. MIT Press, Cambridge
Brock WA, Dechert W, Scheinkman J (1996) A test for independence based on the correlation dimension. Economet Rev 15(3):197–235
Chakrabarti G (2010a) Dynamics of global stock market: crisis and beyond. Vdm-Verlag, Germany
Chakrabarti G (2010b) Propagator of global stock market volatility in recent years: Asian versus non-Asian markets. Empirical Econ Lett 9(4):397–403
Chakrabarti G, Sen C, Sarkar A (2010) Spot-forward rate relationship revisited: an analysis in the light of non-linearity and chaos. J Int Econ 1(2):73–87
Fraser AM, Swinney HL (1986) Independent coordinates for strange attractors from mutual information. Phys Rev A 33:1134–1140
Grassberger P, Procaccia I (1983) Measuring the strangeness of strange attractors. Phys D: Nonlinear Phenom 9(1–2):189–208
Hadamard JS (1898) Les surfaces à courbures opposées et leurs lignes géodésiques. J Math Pures et Appl 4:27–73
Kantz H, Schreiber T (2004) Dimension estimates and physiological data. Chaos 5:143–154
Kaplan DT, Glass L (1992) Direct test for determinism in a time series. Phys Rev Lett 68:427–430
Kennel MB, Brown R, Abarbanel HDI (1992) Determining embedding dimension for phase space reconstruction using a geometrical construction. Phys Rev A 45:3403–3411
Kodba S, Perc M, Marhl M (2004) Detecting chaos from a time series. Eur J Phys 26:205–215
Perc M (2005a) Nonlinear time series analysis of the human electrocardiogram. Eur J Phys 26:757–768
Perc M (2005b) The dynamics of human gait. Eur J Phys 26:525–534
Rhodes C, Morari M (1997) The false nearest neighbors algorithm: an overview. Comput Chem Eng 2(1):S1149–S1154
Sen C, Chakrabarti G, Sarkar A (2011) Evidence of chaos: a tale of two exchange rates. Empirical Econ Lett 10(8):777–784
Stewart I (2002) Does god play dice? the new mathematics of chaos, 2nd edn. Blackwell Publishers Inc, Malden
Takens F (1981) Detecting strange attractor in turbulence. In: Rand DA, Young LS (eds) Lecture notes in mathematics. Springer, Berlin
Theiler J (1986) Spurious dimension from correlation algorithms applied to limited time series data. Phys Rev A 34(3):2427–2432
Wolf A, Swift JB, Swinney HL, Vastano JA (1985) Determining Lyapunov exponents from a time series. Physica D 16:285–317
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2012 The Author(s)
About this chapter
Cite this chapter
Chakrabarti, G., Sen, C. (2012). Global Stock Market, Knife-Edge Stability and the Crisis. In: Anatomy of Global Stock Market Crashes. SpringerBriefs in Economics. Springer, India. https://doi.org/10.1007/978-81-322-0463-3_4
Download citation
DOI: https://doi.org/10.1007/978-81-322-0463-3_4
Published:
Publisher Name: Springer, India
Print ISBN: 978-81-322-0462-6
Online ISBN: 978-81-322-0463-3
eBook Packages: Business and EconomicsEconomics and Finance (R0)