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.
This paper introduces the R package arfurimaaparch version 0.1.0 for time series computations, big data analytics and estimation of Autoregressive Fractional Unit Root Integral Moving Average-Asymmetric Power Autoregressive Conditional Heteroscedasticity (ARFURIMA-APARCH) model. The fdr, arfurimaaparch, arfurimaaparchforecast, arfurimaaparchdiagnostic and arfurimaaparch.sim are the main functions of the package. An improved version of the arfurima package version 1.1.0 of Jibrin and Rahman (2019) for implementing Monte Carlo simulation is also presented. Daily Nigeria all share index and West Texas Intermediate (WTI) crude oil prices for the period 26th January 2004 to 31st December 2018 were used to explained the usage of the packages. When the arfurimaaparch package is compared with other long memory packages, It would produce better stationary process after transformation, appropriate fractional differencing values in the interval of , minimum Akaike Information Criteria values, larger log-likelihood values, minimum p-values of the ARFURIMA-APARCH parameters estimates and large p-values of the Ljung-Box, ARCH-LM and Jarque-Bera test. Findings show that both R packages and their functions are robust, simple and user-friendly. As conclusion, the R packages are suitable, good and reliable for time series analysis computations, statistical analysis and big data analytics.
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