The Application of Structured Feedforward Neural Networks to the Modelling of the Daily Series of Currency in Circulation

Hlaváček, Marek; Čada, Josef; Hakl, F.

doi:10.1007/11539087_163

Cited by 8 publications

(3 citation statements)

References 11 publications

(4 reference statements)

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“…The suggested neural network's forecasting abilities were contrasted with those of the conventional ARIMA model. The results show that neural networks perform better than ARIMA models, and both models may be used, at the very least as auxiliary tools for forecasting liquidity (Hlavacek, Konak, and Cada, 2005). Three models linear forecasting, regression, and seasonal ARIMA models were used to estimate and predict the daily and weekly CIC for the state of Qatar.…”

Section: Empirical Review Of Literaturementioning

confidence: 99%

Predictive Modeling of Nigeria’s Currency in Circulation Using X-12 Autoregressive Integrated Moving Average Method

Bamanga

Adams

2023

DUTCH J FINANCE MANA

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In Nigeria, the average monthly quantity of currency in circulation (CIC) has increased by 269 billion nairas, reaching 2.13 trillion as of 2019 and 2.41 trillion as of 2020. The current value of currency in circulation is expected to be 2.88 trillion naira. The economy of Nigeria is impacted by the seasonal fluctuations in its currency, and it is unavoidable that the economy would need to be adjusted. The purpose of this study was to adjust the seasonal effect of eight days to Easter and Muslim holidays on CIC, model and predict the CIC in Nigeria using the United State Census Bureau's X-12 ARIMA Seasonal adjustment software. The data utilized in the study was the monthly amount of money in circulation that was taken from the Central Bank of Nigeria (CBN) Bulletin between January 2012 and March 2022. Natural logarithm was used to standardize the data, and series seasonality was removed using seasonal differencing. Based on these data, it is clear that X-12-ARIMA (2 1 1)(0 1 1) is the most accurate forecasting approach for Nigeria's CIC. The money in circulation in Nigeria from April 2022 through December 2022 will rise at a positive rate of 2.8% growth rate each month, with a predicted monthly mean CIC of 3.40 trillion by the end of the year 2022, according to this method's predictions. This is the first study on modeling and forecast of CIC in Nigeria that have utilize the United State Census Bureau X-12-ARIMA software, the findings can be extrapolated to the coming year, Nigerians may want to get ready for an increase in the amount of money in circulation during this time.

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Section: Empirical Review Of Literaturementioning

confidence: 99%

Predictive Modeling of Nigeria’s Currency in Circulation Using X-12 Autoregressive Integrated Moving Average Method

Bamanga

Adams

2023

DUTCH J FINANCE MANA

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“…The integrated ARMA model has been used extensively for the non‐stationary time series. To our best knowledge, recent central bank research papers including Cabrero et al (2002), Hlavàcek et al (2005) and Basac et al (2006) used the seasonal ARIMA models and obtained accurate estimations for the CIC.…”

Section: Empirical Modelsmentioning

confidence: 99%

“…Cabrero et al (2002) noted that the error in forecasting banknotes in circulation never exceeds €1 billion in either models mentioned above and they concluded that econometric models are able to explain an important part of the variation in the currencies in the circulation. Hlavàcek et al (2005) forecasted the CIC for Czech Republic using both linear (ARIMA) and non‐linear techniques. Their study provided satisfactory forecasts using the ARIMA models both for long‐term and short‐term horizons[3].…”

Section: Introductionmentioning

confidence: 99%

Modelling the currency in circulation for the State of Qatar

Balli

Elsamadisy

2012

International Journal of Islamic and Middle Eastern Finance and Management

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The main concern of this report is to model the daily and weekly forecasting of the currency in circulation (CIC) for the State of Qatar. The time series of daily observations of the CIC is expected to display marked seasonal and cyclical patterns daily, weekly or even monthly basis. We have compared the forecasting performance of typical linear forecasting models, namely the regression model and the seasonal ARIMA model using daily data. We found that seasonal ARIMA model performs better in forecasting CIC, particularly for short-term horizons. JEL classification: C45, E47, E58.Keywords: Currency in Circulation, Linear Forecasting, ARIMA * We are thankful to Hatice Ozer Balli, Syed Abul Basher, for useful comments on preliminary drafts. We also thank Megan Foster for help with proofreading. This paper was written during Faruk Balli's visit to Central Bank of Qatar. The views expressed here are those of the authors and do not reflect the official view of the Qatar Central Bank. Data and computer code are available on request. The usual disclaimer applies.

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Impact of Payment Technology on Seasonality of Currency in Circulation: Evidence from the USA and India

Bhattacharya

Singh

2016

J. Quant. Econ.

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The Application of Structured Feedforward Neural Networks to the Modelling of the Daily Series of Currency in Circulation

Cited by 8 publications

References 11 publications

Predictive Modeling of Nigeria’s Currency in Circulation Using X-12 Autoregressive Integrated Moving Average Method

Predictive Modeling of Nigeria’s Currency in Circulation Using X-12 Autoregressive Integrated Moving Average Method

Modelling the currency in circulation for the State of Qatar

Impact of Payment Technology on Seasonality of Currency in Circulation: Evidence from the USA and India

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