An application of Ornstein-Uhlenbeck process to commodity pricing in Thailand

Nattiya Chaiyapo,Nattakorn Phewchean

doi:10.1186/s13662-017-1234-y

Abstract

In this paper, we examine an application of Ornstein-Uhlenbeck process to commodity pricing in Thailand. Prices of Tapioca Starch, Ribbed Smoke Sheet no. 3, and Thai Hom Mali Rice are investigated. We use three parameter estimation methods: least squares estimation, maximum likelihood estimation, and jackknife estimation in order to find the best estimation for the model. Jackknife technique is the most appropriate estimation for our commodity pricing model, which provides the least sum-squared error of commodity prices.

Highlights

In the economics, agricultural commodity prices have an important role due to the cost of production and services
From the results shown in Table, Table and Table, the jackknife technique is accurate for λand μin B Thai Hom Mali Rice % (BHMR)
5 Conclusion We have presented the use of Ornstein-Uhlenbeck process in pricing Thai commodity and the parameter estimations with least squares estimation, maximum likelihood estimation, and jackknife technique

Summary

Introduction

Agricultural commodity prices have an important role due to the cost of production and services. Paschke and Prokopczuk [ ] constructed the continuous-time autoregressive moving-average (CARMA) model in which the convenience yield follows an Ornstein-Uhlenbeck-type process of pricing the crude oil future market. We discuss the simulation results of the Ornstein-Uhlenbeck process and parameter estimations. Wt , where t is acceptably small, and Wt are independent identically distributed Wiener process We can use this formula to simulate the long-term expected value or commodity prices; see Smith [ ]. Figures (a)-(c) show the simulation results for the daily price for BHMR, RSS , and TS, respectively. The future prices of RSS follow a slow mean-reversion process. In Figure , the simulation results show that tendency of BHMR future price; in Figure (a), we see reversion to the mean

Simulation results with the parameter estimations of λ

Conclusion