Valuing Real Options in the Volatile Real World - A Generalized Implied Binomial Tree Approach

38 Pages Posted: 5 Feb 2018

See all articles by Seiji Harikae

Seiji Harikae

University of Texas at Austin

Tianyang Wang

Colorado State University - Department of Finance & Real Estate

James Dyer

University of Texas at Austin

Date Written: January 26, 2018

Abstract

Motivated by the real-world challenges of real options evaluation faced by many companies when commodity prices exhibit dramatic volatility and project values can become negative, this paper presents a generalized framework for solving a multifactor real options problem by approximating the underlying stochastic process of project value with a displaced implied binomial tree. The proposed approach allows a flexible structure for stochastic processes with fat tail distributions such as jump diffusion or regime switch, and provides a more accurate estimate of the extreme downside risk by allowing negative values for the underlying project values. The value of a real option by the proposed approach is more accurate and stable than the alternative lattice-based approaches in the literature regardless of the underlying commodity process, which makes this a general and robust approach for valuing complex real options under multiple sources of uncertainty in the volatile real world.

Keywords: Extreme Downside Risk, Multifactor Real Options; Implied Binomial Trees; Simulation

JEL Classification: G10; G13

Suggested Citation

Harikae, Seiji and Wang, Tianyang and Dyer, James, Valuing Real Options in the Volatile Real World - A Generalized Implied Binomial Tree Approach (January 26, 2018). Available at SSRN: https://ssrn.com/abstract=3111077 or http://dx.doi.org/10.2139/ssrn.3111077

Seiji Harikae

University of Texas at Austin ( email )

2317 Speedway
Austin, TX 78712
United States

Tianyang Wang (Contact Author)

Colorado State University - Department of Finance & Real Estate ( email )

Finance and Real Estate Department
1272 Campus Delivery
Fort Collins, CO 80523
United States

James Dyer

University of Texas at Austin ( email )

2317 Speedway
Austin, TX 78712
United States

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