Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-22T05:28:33.444Z Has data issue: false hasContentIssue false

Optimal Stopping of the Maximum Process

Published online by Cambridge University Press:  30 January 2018

Luis H. R. Alvarez*
Affiliation:
University of Turku
Pekka Matomäki*
Affiliation:
University of Turku
*
Postal address: Department of Accounting and Finance, Turku School of Economics, University of Turku, Turku, FI-20500, Finland.
∗∗ Email address: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

We consider a class of optimal stopping problems involving both the running maximum as well as the prevailing state of a linear diffusion. Instead of tackling the problem directly via the standard free boundary approach, we take an alternative route and present a parameterized family of standard stopping problems of the underlying diffusion. We apply this family to delineate circumstances under which the original problem admits a unique, well-defined solution. We then develop a discretized approach resulting in a numerical algorithm for solving the considered class of stopping problems. We illustrate the use of the algorithm in both a geometric Brownian motion and a mean reverting diffusion setting.

Type
Research Article
Copyright
© Applied Probability Trust 

References

Abramowitz, M. and Stegun, I. A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables (National Bureau Standards App. Math. Ser. 55). U.S. Government Printing Office, Washington, D.C. Google Scholar
Alvarez, L. H. R. (2003). On the properties of r-excessive mappings for a class of diffusions. Ann. Appl. Prob. 13, 15171533.Google Scholar
Alvarez, L. H. R. and Lempa, J. (2008). On the optimal stochastic impulse control of linear diffusions. SIAM J. Control Optimization 47, 703732.CrossRefGoogle Scholar
Babbs, S. (2000). Binomial valuation of lookback options. J. Econom. Dynam. Control 24, 14991525.CrossRefGoogle Scholar
Beibel, M. and Lerche, H. R. (1997). A new look at optimal stopping problems related to mathematical finance. Statistica Sinica 7, 93108.Google Scholar
Beibel, M. and Lerche, H. R. (2000). A note on optimal stopping of regular diffusions under random discounting. Teor. Veroyat. Primenen. 45, 657669 (in Russian).Google Scholar
Borodin, A. N. and Salminen, P. (2002). Handbook of Brownian Motion—Facts and Formulae, 2nd edn. Birkhäuser, Basel.Google Scholar
Christensen, S., Salminen, P. and Ta, B. Q. (2013). Optimal stopping of strong Markov processes. Stoch. Process. Appl. 123, 11381159.Google Scholar
Conze, A. and Viswanathan, R. (1991). Path dependent options: the case of lookback options J. Finance 46, 18931907.Google Scholar
Douady, R., Shiryaev, A. N. and Yor, M. (2000). On probability characteristics of “downfalls” in a standard Brownian motion. Theory Prob. Appl. 44, 2938.Google Scholar
Föllmer, H. and Knispel, T. (2007). Potentials of a Markov process are expected suprema. ESAIM Prob. Statist. 11, 89101.Google Scholar
Glover, K., Hulley, H. and Peskir, G. (2013). Three-dimensional Brownian motion and the golden ratio rule. Ann. Appl. Prob. 23, 895922.Google Scholar
Graversen, S. E. and Peškir, G. (1998). Optimal stopping and maximal inequalities for linear diffusions. J. Theoret. Prob. 11, 259277.Google Scholar
Guo, X. and Zervos, M. (2010). π options. Stoch. Process. Appl. 120, 10331059.Google Scholar
Hobson, D. (2007). Optimal stopping of the maximum process: a converse to the results of Peskir. Stochastics 79, 85102.CrossRefGoogle Scholar
Jiang, L. and Dai, M. (2004). Convergence of binomial tree methods for European/American path-dependent options. SIAM J. Numer. Anal. 42, 10941109.Google Scholar
Karatzas, I. and Shreve, S. E. (1988). Brownian Motion and Stochastic Calculus. Springer, New York.Google Scholar
Kyprianou, A. and Ott, C. (2014). A capped optimal stopping problem for the maximum process. Acta Appl. Math. 129, 147174.CrossRefGoogle Scholar
Lerche, H. R. and Urusov, M. (2007). Optimal stopping via measure transformation: the Beibel–Lerche approach. Stochastics 79, 275291.Google Scholar
Magdon-Ismail, M., Atiya, A. F., Pratap, A. and Abu-Mostafa, Y. S. (2004). On the maximum drawdown of a Brownian motion. J. Appl. Prob. 41, 147161.Google Scholar
Matomäki, P. (2013). Optimal stopping and control near boundaries. Preprint. Available at http://uk.arxiv.org/abs/1308.2478.Google Scholar
Obłój, J. (2007). The Maximality Principle Revisited: On Certain Optimal Stopping Problems (Lecture Notes Math. 1899). Springer, Berlin, pp. 309328.Google Scholar
Ott, C. (2013). Optimal stopping problems for the maximum process. . University of Bath.Google Scholar
Pedersen, J. L. (2000). Discounted optimal stopping problems for the maximum process. J. Appl. Prob. 37, 972983.CrossRefGoogle Scholar
Peskir, G. (1998). Optimal stopping of the maximum process: the maximality principle. Ann. Prob. 26, 16141640.Google Scholar
Peskir, G. and Shiryaev, A. (2006). Optimal Stopping and Free-boundary Problems. Birkhäuser, Basel.Google Scholar
Shepp, L. and Shiryaev, A. N. (1993). The Russian option: reduced regret. Ann. Appl. Prob. 3, 631640.Google Scholar