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

Masaaki Kijima, Shibata and Takashi

No 255, Computing in Economics and Finance 2004 from Society for Computational Economics

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

Keywords: rreversible Investment Opportunity; Kalman-Filtering; Finite Difference Methods (search for similar items in EconPapers)
JEL-codes: D81 D83 G31 (search for similar items in EconPapers)
Date: 2004-08-11
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf4:255

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