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Optimal long term investment model with memory

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  • Akihiko Inoue
  • Yumiharu Nakano

Abstract

We consider a financial market model driven by an R^n-valued Gaussian process with stationary increments which is different from Brownian motion. This driving noise process consists of $n$ independent components, and each component has memory described by two parameters. For this market model, we explicitly solve optimal investment problems. These include (i) Merton's portfolio optimization problem; (ii) the maximization of growth rate of expected utility of wealth over the infinite horizon; (iii) the maximization of the large deviation probability that the wealth grows at a higher rate than a given benchmark. The estimation of paremeters is also considered.

Suggested Citation

  • Akihiko Inoue & Yumiharu Nakano, 2005. "Optimal long term investment model with memory," Papers math/0506621, arXiv.org, revised May 2006.
    • Handle: RePEc:arx:papers:math/0506621
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    References listed on IDEAS

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    1. L. C. G. Rogers, 1997. "Arbitrage with Fractional Brownian Motion," Mathematical Finance, Wiley Blackwell, vol. 7(1), pages 95-105, January.
    2. Cherian, Joseph A., 1998. "Investment science : David G. Luenberger, ISBN 0-19-510809-4 New York, 1998, price in US: US $70," Journal of Economic Dynamics and Control, Elsevier, vol. 22(4), pages 645-646, April.
    3. W. H. Fleming & S. J. Sheu, 2000. "Risk‐Sensitive Control and an Optimal Investment Model," Mathematical Finance, Wiley Blackwell, vol. 10(2), pages 197-213, April.
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    Cited by:

    1. Urbanowicz, Krzysztof & Richmond, Peter & Hołyst, Janusz A., 2007. "Risk evaluation with enhanced covariance matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 468-474.

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