Ensemble Kalman Filter for Crude Oil Price Estimation

Katias, Puspandam and Karya, Denis Fidita and Herlambang, Teguh and Khusnah, Hidayatul (2019) Ensemble Kalman Filter for Crude Oil Price Estimation. Journal of Physics: Conference Series, 1211. pp. 1-8. ISSN 1742-6596

Preview	PDF Ensemble Kalman Filter for Crude Oil Price Estimation.pdf Download (841kB) | Preview
Preview	PDF peer review puspandam katias.pdf Download (650kB) | Preview
Preview	PDF peer review denis fidita karya.pdf Download (940kB) | Preview
Preview	PDF peer review hidayatul khusnah.pdf Download (658kB) | Preview
Preview	PDF turnitin puspandam katias.pdf Download (1MB) | Preview
Preview	PDF turnitin denis fidita karya.pdf Download (2MB) | Preview
Preview	PDF turnitin hidayatul khusnah.pdf Download (2MB) | Preview

Abstract

Oil has become the most important sector for the world economic sector. Since 1983, oil has been the main focus of economists after the known impact of oil prices on the economy of the United States after the Second World War. Oil has also been the key role to a world economy although its nature changes over time. The relationship between capital markets and commodities is one of the most challenging problems for investors. Turmoil in one market can affect other market price indexes. Crude Oil Prices are influenced by political conditions and weather-related factors, which can create an unexpected shift in influencing supply and demand. Oil price volatility can be resolved by estimating world crude oil prices so that economists can predict when world oil prices fall or rise and set policies in the purchase and use of crude oil. Estimates are made because a problem can normally be resolved using previous information or data related to the problem. The Kalman filter is a method of estimating the state variables from a discrete linear dynamic system that minimizes estimated covariance errors. The objective of this study is to estimate the price of crude oil using the Kalman Filter (KF) and Ensemble Kalman Filter (EnKF) method. The simulation results show that the EnKF method has a high accuracy of less than 2% and KF method has accuracy of less than 8%.

Item Type:	Article
Subjects:	H Social Sciences > HF Commerce > HF5601 Accounting
Divisions:	Faculty of Economics and Business > Program Study of Accounting
Depositing User:	Mr. . Aji
Date Deposited:	03 Sep 2021 03:24
Last Modified:	20 Dec 2022 06:15
URI:	http://repository.unusa.ac.id/id/eprint/6653

Actions (login required)

View Item