Technical Uncertainty in Real Options with Learning

TL;DR

This paper presents a novel method for valuing investment options in commodity reserves by modeling technical uncertainty and learning over time using a continuous-time Markov chain, highlighting the impact of learning on investment decisions.

Contribution

It introduces a new approach to incorporate technical uncertainty and learning in real options valuation for commodity reserves using Markov chain models.

Findings

Learning reduces uncertainty in reserve estimates over time.

Incorporating learning significantly affects the timing and value of investment decisions.

The model quantifies the value of information gained through learning.

Abstract

We introduce a new approach to incorporate uncertainty into the decision to invest in a commodity reserve. The investment is an irreversible one-off capital expenditure, after which the investor receives a stream of cashflow from extracting the commodity and selling it on the spot market. The investor is exposed to price uncertainty and uncertainty in the amount of available resources in the reserves (i.e. technical uncertainty). She does, however, learn about the reserve levels through time, which is a key determinant in the decision to invest. To model the reserve level uncertainty and how she learns about the estimates of the commodity in the reserve, we adopt a continuous-time Markov chain model to value the option to invest in the reserve and investigate the value that learning has prior to investment.