Modeling and forecasting Tapis crude oil price: A long memory approach

Rahman, Rosmanjawati Abdul; Jibrin, Sanusi Alhaji

doi:10.1063/1.5136393

Cited by 2 publications

(8 citation statements)

References 20 publications

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“…However, Hurvich and Chen (2000) and Erfani and Samimi (2009) have highlighted the repercussion of overdifferencing including loss of information, negative values of differenced series, 𝑑 0.5 and estimation of complex models. In view of these, Rahman and Jibrin (2019) resolved that the possible highest value of fractional difference is in the interval of 1 𝑑 2. Also, this type of time series exhibits very slow decaying ACF, which is slower than usual decay seen in the literature of time series and LM analysis.…”

Section: Methodsmentioning

confidence: 98%

“…Also, this type of time series exhibits very slow decaying ACF, which is slower than usual decay seen in the literature of time series and LM analysis. Having said this, Rahman and Jibrin (2019) named the DGP of 𝑦 to be the FURI process and defined its operator as:…”

Section: Methodsmentioning

confidence: 99%

“…where 𝑑 * 𝑑 1, 0 𝑑 * 1 and 1 𝑑 2. Details of the derivation and its R algorithm can be found in Rahman and Jibrin (2019).…”

Section: Methodsmentioning

confidence: 99%

“…The Autoregressive Fractional Unit Root Integrated Moving Average (ARFURIMA) model, suggested by Rahman and Jibrin (2019), provides a method for modelling FURI time series with fractional difference value in the interval of 1 𝑑 2. Therefore, in this paper, some of the basic properties of the ARFURIMA model were derived and presented as follows.…”

Section: Introductionmentioning

confidence: 99%

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ARFURIMA models: simulations of their properties and application

Jibrin

Rahman

2022

Statistics in Transition New Series

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This article defines the Autoregressive Fractional Unit Root Integrated Moving Average (ARFURIMA) model for modelling ILM time series with fractional difference value in the interval of 1 < d < 2. The performance of the ARFURIMA model is examined through a Monte Carlo simulation. Also, some applications were presented using the energy series, bitcoin exchange rates and some financial data to compare the performance of the ARFURIMA and the Semiparametric Fractional Autoregressive Moving Average (SEMIFARMA) models. Findings showed that the ARFURIMA outperformed the SEMIFARMA model. The study’s conclusion provides another perspective in analysing large time series data for modelling and forecasting, and the findings suggest that the ARFURIMA model should be applied if the studied data show a type of ILM process with a degree of fractional difference in the interval of 1 < d < 2.

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Section: Methodsmentioning

confidence: 98%

Section: Methodsmentioning

confidence: 99%

“…where 𝑑 * 𝑑 1, 0 𝑑 * 1 and 1 𝑑 2. Details of the derivation and its R algorithm can be found in Rahman and Jibrin (2019).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ARFURIMA models: simulations of their properties and application

Jibrin

Rahman

2022

Statistics in Transition New Series

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“…The Interminable Long Memory (ILM) is a type of long memory and can be considered a component of variation observed in large and non-stationary time series. According to Rahman & Jibrin (2019), ILM are time series that exhibits a strong long-range dependence, have slow decaying ACF, strong, positive and perfect autocorrelation, fractional difference values in the interval of 1 < 𝑑 < 2 and they are generated by Fractional Unit Root Integrated (FURI)process.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid ARFURIMA-APARCH model for modeling interminable long memory and asymmetric effect in time series

Jibrin¹,

Ibrahim²,

Munkaila³

2022

dujopas

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In this paper, we introduce a new hybrid model namely Autoregressive Fractional Unit Root Integrated Moving Average-Asymmetric Power Autoregressive Conditional Heteroscedasticity (ARFURIMA-APARCH) model. The Nigeria daily COVID19 records and Bitcoin to EURO exchange rate that exhibit a type of Long Memory (LM) called Interminable LM (ILM), volatility and asymmetric (leverage) effect were used to show the applications of the proposed ARFURIMA-APARCH model. The existing Autoregressive Fractional Integrated Moving Average-Asymmetric Power Autoregressive Conditional Heteroscedasticity (ARFIMA-APARCH) model were estimated and compared with the ARFURIMA-APARCH model. Results showed that the new hybrid model is better based on goodness-of-fit, serial correlation tests and forecast measures of accuracy. As a conclusion, our study showed that the ARFURIMA-APARCH model performed better compared to the ARFIMA-APARCH hybrid model. Therefore, the ARFURIMA-APARCH model is a better option for modeling ILM, volatility and leverage effect of health and financial data. Future study should focus on the application of the developed hybrid ARFURIMA-APARCH model using some major economic indicators, for example, Gross Domestic Product (GDP), currency exchange rate, stock price index, interest rate and other financial data.

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Modeling and forecasting Tapis crude oil price: A long memory approach

Cited by 2 publications

References 20 publications

ARFURIMA models: simulations of their properties and application

ARFURIMA models: simulations of their properties and application

A novel hybrid ARFURIMA-APARCH model for modeling interminable long memory and asymmetric effect in time series

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