International Journal of Statistics and Applied Mathematics
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International Journal of Statistics and Applied Mathematics

2022, Vol. 7, Issue 4, Part A

Mean: Reverting logistic Brownian motion with jump diffusion process on energy commodity prices


Author(s): Oduor D Brian

Abstract: Models that can describe strike prices of energy commodities that might seem costly to store are best modeled by mean – reversion and jump diffusion processes. Physical characteristics of energy commodities makes it very difficult to store due to their salient features hence there is need to incorporate jumps and mean – reversion to some stochastic volatility models and particularly in this paper, we forecast on logistic Brownian motion. I construct a real option model to predict prices of energy commodities. This study also examines some implications on assumptions that can be portrayed by mean – reverting logistic Brownian motion with jump diffusion process. This study uses Heave – side cover up method, logistic Brownian motion, jump diffusion models and mean – reverting models to derive a pricing process that can be used to predict prices of energy commodities.

Pages: 69-74 | Views: 7 | Downloads: 4

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How to cite this article:
Oduor D Brian. Mean: Reverting logistic Brownian motion with jump diffusion process on energy commodity prices. Int J Stat Appl Math 2022;7(4):69-74.
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