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Expected Log-Utility Maximization Under Incomplete Information and with Cox-Process Observations

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  • Kazufumi Fujimoto
  • Hideo Nagai
  • Wolfgang Runggaldier

Abstract

We consider the portfolio optimization problem for the criterion of maximization of expected terminal log-utility. The underlying market model is a regime-switching diffusion model where the regime is determined by an unobservable factor process forming a finite state Markov process. The main novelty is due to the fact that prices are observed and the portfolio is rebalanced only at random times corresponding to a Cox process where the intensity is driven by the unobserved Markovian factor process as well. This leads to a more realistic modeling for many practical situations, like in markets with liquidity restrictions; on the other hand it considerably complicates the problem to the point that traditional methodologies cannot be directly applied. The approach presented here is specific to the log-utility. For power utilities a different approach is presented in the companion paper (Fujimoto et al. in Appl Math Optim 67(1):33–72, 2013 ). Copyright Springer Japan 2014

Suggested Citation

  • Kazufumi Fujimoto & Hideo Nagai & Wolfgang Runggaldier, 2014. "Expected Log-Utility Maximization Under Incomplete Information and with Cox-Process Observations," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 21(1), pages 35-66, March.
    • Handle: RePEc:kap:apfinm:v:21:y:2014:i:1:p:35-66
    DOI: 10.1007/s10690-013-9176-1
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    References listed on IDEAS

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    1. Koichi Matsumoto, 2006. "Optimal portfolio of low liquid assets with a log-utility function," Finance and Stochastics, Springer, vol. 10(1), pages 121-145, January.
    2. Tomas Björk & Mark Davis & Camilla Landén, 2010. "Optimal investment under partial information," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 71(2), pages 371-399, April.
    3. Rüdiger Frey & Wolfgang J. Runggaldier, 2001. "A Nonlinear Filtering Approach To Volatility Estimation With A View Towards High Frequency Data," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 4(02), pages 199-210.
    4. Eckhard Platen & Wolfgang Runggaldier, 2007. "A Benchmark Approach to Portfolio Optimization under Partial Information," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 14(1), pages 25-43, March.
    5. Huyên Pham & Peter Tankov, 2008. "A Model Of Optimal Consumption Under Liquidity Risk With Random Trading Times," Mathematical Finance, Wiley Blackwell, vol. 18(4), pages 613-627, October.
    6. Jakv{s}a Cvitani'c & Robert Liptser & Boris Rozovskii, 2006. "A filtering approach to tracking volatility from prices observed at random times," Papers math/0612212, arXiv.org.
    7. Hideo Nagai, 2004. "Risky Fraction Processes and Problems with Transaction Costs," World Scientific Book Chapters, in: Jiro Akahori & Shigeyoshi Ogawa & Shinzo Watanabe (ed.), Stochastic Processes And Applications To Mathematical Finance, chapter 13, pages 271-288, World Scientific Publishing Co. Pte. Ltd..
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    Cited by:

    1. Agostino Capponi & José Figueroa-López & Andrea Pascucci, 2015. "Dynamic credit investment in partially observed markets," Finance and Stochastics, Springer, vol. 19(4), pages 891-939, October.

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