Forecasting Currency in Circulation in Malaysia Using ARCH and GARCH Models

Razak, Nur Azreen Abdul; Khamis, Azme; Abdullah, Mohd; Sufahani, Suliadi Firdaus

doi:10.9734/cjast/2018/40358

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Interest rate forecasting in India was addressed by a group of researchers who came to the conclusion that different models (ARIMA-GARCH, LVAR, BVAR, VECM) should be used depending on the type of interest rate and the forecast horizon (Dua, P. 2008). In order to obtain the optimal model to forecast the short-term interest rate, the forecasting performance of the ARIMA, ARIMA-GARCH and ARIMA-EGARCH models was compared (Razak, N. A. A., et al, 2017).…”

Section: Forecast Results and Models Evaluationmentioning

confidence: 99%

Econometric Modeling and Forecasting of Interest Rates in Montenegro

Pejović,

Karadžić

2020

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In contemporary conditions, the econometric modeling and forecast have a growing importance in both the development of several theoretic models and approaches that may be used and in the necessity of forecasting to make proper decisions by authorities for their making. The paper tests the possibility of applying the Box-Jenkins approach and vector autoregressive models for modeling and forecasting interest rates in Montenegro. Box-Jenkins approach and vector autoregressive models are one of multiple, yet the most used approaches and models used for forecasting time series values. Thee comparison of forecasting models determines the more superior model. The time series of the interest rate to be modeled and forecasted is a monthly weighted average lending interest rate of banks on new loans in the period from December 2011 to January 2018. An example of the interest rate, which is extremely important in quite a bank-centric system in Montenegro, proved that the Box-Jenkins approach and VAR models may be used successfully for modeling and forecast. Moreover, the paper recommends the use of the Box-Jenkins approach and the assessed AR model for forecasting interest rate since it has better forecasting performances than the VAR model. Despite numerous limitations, primarily the inadequate statistical base, the AR model may find its application and help the decision-makers in the process of making economic decisions. Keywords: forecasting, autoregressive models, interest rate forecasting, Box-Jenkins, VAR, AR.

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Section: Forecast Results and Models Evaluationmentioning

confidence: 99%

Econometric Modeling and Forecasting of Interest Rates in Montenegro

Pejović,

Karadžić

2020

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“…Mihçi and Akkoyunlu-Wigley (2009) use fixed effect specification of panel data of 12 manufacturing industries in Turkey to investigate the effect of trade between EU countries and Turkey on the productivity of Turkish manufacturing sector. Under time series analysis, frequent utilization of Autoregressive Integrated Moving Average (ARIMA) method is also well-supported in productivity context to handle the non-stationarity of data (Paul et al, 2013;Samavati, 2013;Perone, 2020;Razak et al, 2017).…”

Section: A Variety Of Methodologies Used In Productivity-related Rese...mentioning

confidence: 99%

“…ARIMA is supported to be a generalized approach useful in explaining the dynamics of changes in time for various economical parameters and interruptions (Jarrett & Kyper, 2011;Kohlrausch & Brin, 2020). ARIMA has the autoregressive character with the advantage of handling nonstationarity in the data, hence being more flexible (Paul et al, 2013;Samavati, 2013;Perone, 2020;Razak et al, 2017;Sabry et al, 2007;Mandrikova et al, 2021;Singh, 2013). The systematic and phases of the study followed are depicted in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Labour Productivity Analysis of Manufacturing Sector in Turkey Against Eu

Balkan

Akyüz

2023

Journal of Business Economics and Management

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This study offers an in-depth analysis of labour productivity of manufacturing sector in Turkey and provides a comparison with EU27 and EA19 countries utilizing Eurostat time series data of 63 quarters covering 2005/first quarter-2020/third quarter time interval. Productivity trends are identified and interpreted by relating them with the key macroeconomic events and factors. Multiple linear and non-linear regression equations, and ARIMA model with different parameters are applied to the time series data considering the periods with and without covid effect. Future projections are made for the periods 2020–2023 for Turkey manufacturing sector based on the best fitting regression and ARIMA solutions and they are compared. Findings revealed that extreme covid conditions of even two quarters of data have significant impact on the forecasted values for Turkey, EU27 and EA19 countries. ARIMA analysis with 12 different parameter settings provided accurate results, supported by Thiel’s inequality coefficients and standard error measures. Analysis has shown consistent patterns between EA19 and EU27 countries. ARIMA results represent better compatibility with the regression results for Turkey. Study is valuable by providing comprehensive and comparative analysis, revealing future forecasts and covid effect and degree of recovery from the pandemic.

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Forecasting Currency in Circulation in Malaysia Using ARCH and GARCH Models

Cited by 2 publications

References 9 publications

Econometric Modeling and Forecasting of Interest Rates in Montenegro

Econometric Modeling and Forecasting of Interest Rates in Montenegro

Labour Productivity Analysis of Manufacturing Sector in Turkey Against Eu

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