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The Impacts of Fragmented Volatilities by Learning about Predictability in the Real Options Approach

Author

Listed:
  • Kijima, Masaaki
  • Shibata
  • Takashi

Abstract

This paper examines the effects of uncertainty through dynamic learning about the firm's project value in the real options framework. We extend the real options framework with incomplete information by allowing an unobserved state variable that drives profits to follow a stochastic process with market uncertainty. Similar to the proposition in the standard real options approach where complete information is available, we find that in the situation with incomplete information the project value increases as the market uncertainty increases. Furthermore, we demonstrate that the project value increases as both information uncertainty decreases and estimation uncertainty increases

Suggested Citation

  • Kijima, Masaaki & Shibata & Takashi, 2004. "The Impacts of Fragmented Volatilities by Learning about Predictability in the Real Options Approach," Computing in Economics and Finance 2004 255, Society for Computational Economics.
    • Handle: RePEc:sce:scecf4:255
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    More about this item

    Keywords

    rreversible Investment Opportunity; Kalman-Filtering; Finite Difference Methods;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G31 - Financial Economics - - Corporate Finance and Governance - - - Capital Budgeting; Fixed Investment and Inventory Studies

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