Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-22T07:42:44.006Z Has data issue: false hasContentIssue false

SOLUTIONS AND DIAGNOSTICS OF SWITCHING PROBLEMS WITH LINEAR STATE DYNAMICS

Published online by Cambridge University Press:  28 January 2016

J. HINZ*
Affiliation:
School of Mathematical and Physical Sciences, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia email [email protected]
N. YAP
Affiliation:
Finance Discipline Group, UTS Business School, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia email [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.

Optimal control problems of stochastic switching type appear frequently when making decisions under uncertainty and are notoriously challenging from a computational viewpoint. Although numerous approaches have been suggested in the literature to tackle them, typical real-world applications are inherently high dimensional and usually drive common algorithms to their computational limits. Furthermore, even when numerical approximations of the optimal strategy are obtained, practitioners must apply time-consuming and unreliable Monte Carlo simulations to assess their quality. In this paper, we show how one can overcome both difficulties for a specific class of discrete-time stochastic control problems. A simple and efficient algorithm which yields approximate numerical solutions is presented and methods to perform diagnostics are provided.

Type
Research Article
Copyright
© 2016 Australian Mathematical Society 

References

Andersen, L. and Broadie, M., “A primal–dual simulation algorithm for pricing multidimensional American options”, Manag. Sci. 50 (2004) 12221234; doi:10.1287/mnsc.1040.0258.Google Scholar
Bäuerle, N. and Rieder, U., Markov decision processes with applications to finance (Springer, Heidelberg, 2011); doi:10.1007/978-3-642-18324-9 2.Google Scholar
Belomestny, N., Kolodko, A. and Schoenmakers, J., “Regression methods for stochastic control problems and their convergence analysis”, SIAM J. Control Optim. 48 (2010) 35623588; doi:10.1137/090752651.Google Scholar
Bertsekas, D. P., Dynamic programming and optimal control (Athena Scientific, Belmont, MA, 2005).Google Scholar
Carmona, R. and Ludkovski, M., “Valuation of energy storage: an optimal switching approach”, Quant. Finance 10 (2010) 359374; doi:10.1080/14697680902946514.Google Scholar
Feinberg, E. A. and Schwartz, A., “Handbook of Markov decision processes”, Internat. Ser. Oper. Res. Management Sci. (2002); doi:10.1007/978-1-4615-0805-2.Google Scholar
Haugh, M. and Kogan, L., “Pricing American options: a duality approach”, Oper. Res. 52 (2004) 258270; doi:10.1287/opre.1030.0070.Google Scholar
Hinz, J., “Optimal stochastic switching under convexity assumptions”, SIAM J. Control Optim. 52 (2014) 164188; doi:10.1137/13091333X.Google Scholar
Hinz, J. and Yap, N., “Algorithms for optimal control of stochastic switching systems”, Theory Probab. Appl. (to appear).Google Scholar
Longstaff, F. and Schwartz, E., “Valuing American options by simulation: a simple least-squares approach”, Rev. Financ. Stud. 14 (2001) 113147http://links.jstor.org/sici?sici=0893-94542820012129143A13C1133AVAOBSA3E2.0.CO3B2-W.CrossRefGoogle Scholar
Powell, W. B., Approximate dynamic programming: solving the curses of dimensionality (Wiley, Hoboken, NJ, 2007).Google Scholar
Puterman, M. L., Markov decision processes: discrete stochastic dynamic programming (Wiley, New York, 1994).Google Scholar
Rogers, L. C. G., “Monte Carlo valuation of American options”, Math. Finance 12 (2002) 271286; doi:10.1111/1467-9965.02010.Google Scholar
Rogers, L. C. G., “Pathwise stochastic optimal control”, SIAM J. Control Optim. 46 (2007) 11161132; doi:10.1137/050642885.CrossRefGoogle Scholar
Ye, F. and Zhou, E., “Optimal stopping of partially observable Markov processes: a filtering-based duality approach”, IEEE Trans. Automat. Control 58 (2013) 26982704; doi:10.1109/TAC.2013.2257970.Google Scholar