THE SIZE OF THE LARGEST FLUCTUATIONS IN A MARKET MODEL WITH MARKOVIAN SWITCHING

TitleTHE SIZE OF THE LARGEST FLUCTUATIONS IN A MARKET MODEL WITH MARKOVIAN SWITCHING
Publication TypeJournal Article
Year of Publication2009
AuthorsAPPLEBY, JOHNAD
Secondary AuthorsLYNCH, TERRY, MAO, XUERONG, WU, HUIZHONG
Secondary TitleCommunications in Applied Analysis
Volume13
Issue2
Start Page135
Pagination166
Date Published04/2009
Type of Workscientific: mathematics
ISSN1083–2564
AMS34D05, 60F15, 60H10, 60J25, 60J65, 91B28, 91B62, 91B70
Abstract
This paper considers the size of the large fluctuations of a stochastic differential equation with Markovian switching. We concentrate on processes which obey the Law of the Iterated Logarithm, or obey upper and lower iterated logarithm growth bounds on their almost sure partial maxima. The results are applied to financial market models which are subject to random regime shifts. We prove that the security exhibits the same long–run growth properties and deviations from the trend rate of growth as conventional geometric Brownian motion, and also that the returns, which are non–Gaussian, still exhibit the same growth rate in their almost sure large deviations as stationary continuous–time Gaussian processes.
URLhttp://www.acadsol.eu/en/articles/13/2/1.pdf
Short TitleMARKET MODEL WITH MARKOVIAN SWITCHING
Refereed DesignationRefereed
Full Text

REFERENCES

[1] Anderson, W. J., Continuous-Time Markov Chains, Springer, New York, 1991.
[2] Athans, M., Command and control (C2) theory: A challenge to control science, IEEE Trans. Automat. Contr., 32, 286–293, 1987.
[3] Cootner, P. H., The Random Character of Stock Market Prices, MIT Press, Cambridge, 1964.
[4] Fama, E. F., The Behavior of Stock-Market Prices. J. Business, 38 (1), 34-105, 1965.
[5] Fama, E. F., Market efficiency, long-term returns, and behavioral finance. J. Financial Economics, 49 (3), 283–306, 1998.
[6] Fouque, J.-P., Papanicolaou, G. and Sircar, K. R. Derivatives in financial markets with stochastic volatility. Cambridge University Press, Cambridge, 2000.
[7] Franses, P. H., and van Dijk, D., Nonlinear time series models in empirical finance, Cambridge University Press, Cambridge, 2000.
[8] Ghosh, M.K., Arapostathis, A. and Marcus, S.I., Optimal control of switching diffusions with application to flexible manufacturing systems, SIAM J. Control Optim. 35, 1183–1204, 1993.
[9] Ghosh, M.K., Arapostathis, A. and Marcus, S.I., Ergodic control of switching diffusions, SIAM J. Control Optim. 35, 1952–1988, 1997.
[10] Hamilton, J. D., A new approach to the economic analysis of nonstationary time series subject to changes in regime, Econometrica, 57, 357–384, 1989.
[11] Hull, J. C., Options, Futures and Other Derivatives. Sixth edition. Prentice Hall, New York, 2005.
[12] Hull, J. C. and White, A., The pricing of options on assets with stochastic volatilities. J. Financial and Quantitative Analysis, 23, 237–251, 1987.
[13] Karatzas, I. and Shreve, S. E., Methods of mathematical finance. Springer-Verlag, New York, 1998.
[14] Karatzas, I. and Shreve, S. E., Brownian motion and stochastic calculus. Second edition. Springer-Verlag, New York, 1991.
[15] Lo, A. and MacKinlay, A. C., A Non-Random Walk Down Wall Street, Princeton University Press, Princeton, 1999.
[16] Mao, X., Almost sure asymptotic bounds for a class of stochastic differential equations, Stochastics Stochastics Rep., 41, no 1-2, 57–69, 1992.
[17] Mao, X., Stochastic Differential Equations and Their Applications, Chichester: Horwood Pub., 1997.
[18] Mao, X. and Yuan, C., Stochastic Differential Equations with Markovian Switching, Imperial College Press, 2006.
[19] Merton, R. C., Theory of rational option pricing. Bell J. Econom. and Management Sci., 4, 141–183, 1973.
[20] Motoo, M., Proof of the law of iterated logarithm through diffusion equation, Ann. Inst. Statist. Math., 10, 21–28, 1958.
[21] Revuz, D. and Yor, M., Continuous martingales and Brownian motion. Third edition, Springer-Verlag, Berlin, 1999.
[22] Renault, E. and Touzi, N., Option hedging and implied volatilities in a stochastic volatility model. J. Math. Finance, 6 (3), 279–302, 1996.
[23] Sethi, S.P. and Zhang, Q., Hierarchical Decision Making in Stochastic Manufacturing Systems, Birkh ̈
auser, Boston, 1994.
[24] Sworder, D.D. and Rogers, R.O., An LQ-solution to a control problem associated with solar thermal central receiver, IEEE Trans. Automat. Contr. 28, 971–978, 1983.
[25] Yin, G. and Zhang, Q., Continuous-Time Markov Chains and Applications: A Singular Perturbation Approach, Springer-Verlag, New York, 1998.
[26] Yin, G., Liu, R.H. and Zhang, Q., Recursive algorithms for stock Liquidation: A stochastic optimization approach, SIAM J. Optim., 13, 240–263, 2002.
[27] Yin, G.G. and Zhou, X.Y., Markowitzs mean-variance portfolio selection with regime switching: from discrete-time models to their continuous-time limits, IEEE Transactions on Automatic Control, 49, 349–360, 2004.
[28] Willsky, A.S. and Levy, B.C., Stochastic stability research for complex power systems, DOE Contract, LIDS, MIT, Rep. ET-76-C-01-2295, 1979.
[29] Zhang, Q., Stock trading: An optimal selling rule, SIAM J. Control. Optim., 40, 64–87, 2001.