For the past few years, with the propulsion of opening and reforming in China, the value of its currency has determined by the market step by step. Currently, because China’s economy occupies an important position in the global economy, the Renminbi (RMB) exchange rate has a pivotal position in the stability and development of the world economy. The results indicated that changes of exchange rate not only affect the national economic balance but also have an impact on the level of foreign trade, which further influences the economic development of the relevant country. Ding and Ying analyzed the relationship between exchange rate and export trade. As international trade and financial activities are closely related to exchange rate, the status of exchange rate is very prominent. With the persistent development of economic globalization, business contacts among nations get increasingly frequent. The results show that the prediction performance of the nonlinear combination model is better than the single models and the linear combination models, and the nonlinear combination model is suitable for the prediction of the special time series, such as the RMB exchange rate. The RMBĮxchange rate against US dollar (RMB/USD) and the RMB exchange rate against Euro (RMB/EUR) are used as the empirical examples to evaluate the performance of NCM. The basic idea of the nonlinear combination model (NCM) is to make the prediction more effective by combining different models’ advantages, and the weight of the combination model is determined by a nonlinear weighted mechanism. RMB exchange rate, we build a nonlinear combination model of the autoregressive fractionally integrated moving average (ARFIMA) model, the support vector machine (SVM) model, and the back-propagation neural network (BPNN) model to forecast the RMB exchange rate. In this paper, considering the complex characteristics of
These results would, therefore, be useful to both countries in making good portfolio decisions, assessing the efficacy of a monetary policy or programme meant to control inflation persistence and also serving as a tool for detecting volatility and its impact, for the Ghanaian and South African inflation rates and their economies at large.There are various models to predict financial time series like the RMB exchange rate. The results from the study provided evidence of persistence, mean reverting though, and asymmetric effect of economic shocks on the conditional mean of CPI inflation rate of the two countries. ARFIMA-GJR-GARCH under Generalised Error Distribution and Student-t Distribution respectively, provided the best fit for modelling the time-dependent heteroskedasticity and persistence in the conditional mean of CPI inflation rate of Ghana and South Africa. This paper contributes to the debate on inflation persistence by extending an ARFIMA process with GARCH and GJR-GARCH models to describe the time-dependent heteroskedasticity and persistence in the conditional mean of Consumer Price Index (CPI) inflation series of Ghana and South Africa, under three distributional assumptions (i.e., Normal, Student-t and Generalised Error Distributions).
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Publisher: African Review of Economics and Finance Modelling persistence in the conditional mean of inflation using the ARFIMA process with GARCH and GJRGARCH innovations: The case of Ghana and South AfricaĪlexander Boateng, Maseka Lesaoana, Hlengani Siweya, Abenet Belete And Lius Alberiko Gil-Alana.