On modelling of crude oil futures in a bivariate state-space framework

Peilun He; Karol Binkowski; Nino Kordzakhia; Pavel Shevchenko

doi:10.1007/978-3-030-78965-7_40

On modelling of crude oil futures in a bivariate state-space framework

Peilun He, Karol Binkowski, Nino Kordzakhia, Pavel Shevchenko

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

We study a bivariate latent factor model for the pricing of commodity futures. The two unobservable state variables representing the short and long term factors are modelled as Ornstein-Uhlenbeck (OU) processes. The Kalman Filter (KF) algorithm has been implemented to estimate the unobservable factors as well as unknown model parameters. The estimates of model parameters were obtained by maximising a Gaussian likelihood function. The algorithm has been applied to WTI Crude Oil NYMEX futures data.

Original language	English
Title of host publication	Mathematical and statistical methods for actuarial sciences and finance
Subtitle of host publication	eMAF2020
Editors	Marco Corazza, Manfred Gilli, Cira Perna, Claudio Pizzi, Marilena Sibillo
Place of Publication	Cham
Publisher	Springer, Springer Nature
Chapter	40
Pages	273-278
Number of pages	6
ISBN (Electronic)	9783030789657
ISBN (Print)	9783030789640
DOIs	https://doi.org/10.1007/978-3-030-78965-7_40
Publication status	Published - 2021

Keywords

Kalman filter
Kalman smoother
State-space model
Crude oil futures

Access to Document

10.1007/978-3-030-78965-7_40

Cite this

He, P., Binkowski, K., Kordzakhia, N., & Shevchenko, P. (2021). On modelling of crude oil futures in a bivariate state-space framework. In M. Corazza, M. Gilli, C. Perna, C. Pizzi, & M. Sibillo (Eds.), Mathematical and statistical methods for actuarial sciences and finance: eMAF2020 (pp. 273-278). Springer, Springer Nature. https://doi.org/10.1007/978-3-030-78965-7_40

@inbook{b6332354cf924f15a20281a544704998,

title = "On modelling of crude oil futures in a bivariate state-space framework",

abstract = "We study a bivariate latent factor model for the pricing of commodity futures. The two unobservable state variables representing the short and long term factors are modelled as Ornstein-Uhlenbeck (OU) processes. The Kalman Filter (KF) algorithm has been implemented to estimate the unobservable factors as well as unknown model parameters. The estimates of model parameters were obtained by maximising a Gaussian likelihood function. The algorithm has been applied to WTI Crude Oil NYMEX futures data.",

keywords = "Kalman filter, Kalman smoother, State-space model, Crude oil futures",

author = "Peilun He and Karol Binkowski and Nino Kordzakhia and Pavel Shevchenko",

year = "2021",

doi = "10.1007/978-3-030-78965-7\_40",

language = "English",

isbn = "9783030789640",

pages = "273--278",

editor = "Marco Corazza and Manfred Gilli and Cira Perna and Claudio Pizzi and Marilena Sibillo",

booktitle = "Mathematical and statistical methods for actuarial sciences and finance",

publisher = "Springer, Springer Nature",

address = "United States",

}

On modelling of crude oil futures in a bivariate state-space framework. / He, Peilun; Binkowski, Karol ; Kordzakhia, Nino et al.
Mathematical and statistical methods for actuarial sciences and finance: eMAF2020. ed. / Marco Corazza; Manfred Gilli; Cira Perna; Claudio Pizzi; Marilena Sibillo. Cham: Springer, Springer Nature, 2021. p. 273-278.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - On modelling of crude oil futures in a bivariate state-space framework

AU - He, Peilun

AU - Binkowski, Karol

AU - Kordzakhia, Nino

AU - Shevchenko, Pavel

PY - 2021

Y1 - 2021

N2 - We study a bivariate latent factor model for the pricing of commodity futures. The two unobservable state variables representing the short and long term factors are modelled as Ornstein-Uhlenbeck (OU) processes. The Kalman Filter (KF) algorithm has been implemented to estimate the unobservable factors as well as unknown model parameters. The estimates of model parameters were obtained by maximising a Gaussian likelihood function. The algorithm has been applied to WTI Crude Oil NYMEX futures data.

AB - We study a bivariate latent factor model for the pricing of commodity futures. The two unobservable state variables representing the short and long term factors are modelled as Ornstein-Uhlenbeck (OU) processes. The Kalman Filter (KF) algorithm has been implemented to estimate the unobservable factors as well as unknown model parameters. The estimates of model parameters were obtained by maximising a Gaussian likelihood function. The algorithm has been applied to WTI Crude Oil NYMEX futures data.

KW - Kalman filter

KW - Kalman smoother

KW - State-space model

KW - Crude oil futures

UR - https://www.scopus.com/pages/publications/85198104046

U2 - 10.1007/978-3-030-78965-7_40

DO - 10.1007/978-3-030-78965-7_40

M3 - Chapter

AN - SCOPUS:85198104046

SN - 9783030789640

SP - 273

EP - 278

BT - Mathematical and statistical methods for actuarial sciences and finance

A2 - Corazza, Marco

A2 - Gilli, Manfred

A2 - Perna, Cira

A2 - Pizzi, Claudio

A2 - Sibillo, Marilena

PB - Springer, Springer Nature

CY - Cham

ER -

On modelling of crude oil futures in a bivariate state-space framework

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this