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Short-Term Currency in Circulation Forecasting for Monetary Policy Purposes: the Case of Poland

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A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Koziński, Witold; Świst, Tomasz Article Short-term currency in circulation forecasting for monetary policy purposes: The case of Poland e-Finanse: Financial Internet Quarterly Provided in Cooperation with: University of Information Technology and Management, Rzeszów Suggested Citation: Koziński, Witold; Świst, Tomasz (2015) : Short-term currency in circulation forecasting for monetary policy purposes: The case of Poland, e-Finanse: Financial Internet Quarterly, ISSN 1734-039X, University of Information Technology and Management, Rzeszów, Vol. 11, Iss. 1, pp. 65-75, http://dx.doi.org/10.14636/1734-039X_11_1_007 This Version is available at: http://hdl.handle.net/10419/147121 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen Nutzungsbedingungen die in der dort Content Licence (especially Creative Commons Licences), you genannten Lizenz gewährten Nutzungsrechte. may exercise further usage rights as specified in the indicated licence. www.econstor.eu Financial Internet Quarterly „e-Finanse” 2015, vol.11 / nr 1, s. 65 - 75 DOI: 10.14636/1734-039X_11_1_007 SHORT-TERM CURRENCY IN CIRCULATION FORECASTING FOR MONETARY POLICY PURPOSES – THE CASE OF POLAND WITOLD KOzińsKI1 TOMASZ ŚWIST2 Abstract One of the most significant factors which influences the level of banking sector liquidity is Currency in Circulation. Although the central bank is in charge of distribution of the currency it can’t assess the demand for the currency, as that demand is generated by the customers of commercial banks. Therefore, the amount of Currency in Circulation has to be modelled and forecasted. This paper introduces ARIMA(2,1) and SARIMA(2,1)(5,0) models with dummy variables and discusses its applicability to the forecasting of Currency in Circulation. The forecasting performance of these models is compared. The results indicate that the performance of SARIMA(2,1)(5,0) is better and that both models might be applied for monetary policy purposes as supportive tools for banking sector liquidity forecasting. JEL classification: C01, C53, E52 Keywords: Currency in Circulation, Banking Sector Liquidity, Monetary Policy Received: 09.10.2014 Accepted: 12.06.2015 1 National Bank of Poland, [email protected]. 2 National Bank of Poland, [email protected]. The authors wish to express sincere gratitude to Mr Krzysztof Senderowicz (Director of Domestic Operations Depart- ment, NBP), Mrs Agnieszka Strużyńska (Supervisor of OMOs Planning Division, NBP), Mr Michał Adam (Senior Economist at Financial Markets Division, NBP) and other colleagues from the National Bank of Poland for creative discussion which led to improvement of this article. The opinions expressed in this paper are solely those of the authors and do not ne- cessarily reflect the views of the National Bank of Poland. www.e-finanse.com University of Information Technology and Management in Rzeszów 65 Witold Koziński, Tomasz Świst Short-term currency in circulation forecasting for monetary policy purposes – the case of Poland „e-Finanse” 2015, vol. 11/ nr 1 approach introduced by Harvey A., Koopman S. and Riani INTRODUCTION M. (1997). The forecasting performance of the models The gradual increments of short-term banking were assessed in the context of their impact on the sector liquidity in Poland since 2009 implied the need liquidity management of the Eurosystem. The presented to strengthen the liquidity management framework results suggest that two approaches are powerful and that by the National Bank of Poland and gave an apparent the ARIMA model has the best forecasting performance role to Open Market Operations (OMOs). In particular, over horizons of 5 days and more, while STS is the best it was necessary to update the existing daily liquidity for horizons of 1 to 4 days. The authors concluded that forecast techniques so that they could provide accurate the best forecasting performance could be achieved by information on short-term liquidity needs, which could combining the two approaches. be managed with the use of regular (as well as fine- In the Czech Republic, Hlaváček M., Michael tuning) OMOs. Koňák M. and Čada J. (2005) introduced a feedforward The main banking sector liquidity forecasting structured neural network model and discussed its horizon (of the daily liquidity forecasts) is the reserve applicability to the forecasting of Currency in Circulation. requirement maintenance period, which lasts from 4 The forecasting performance of the neural network model to 5 weeks. However, within each reserve maintenance was compared with an ARIMA model described by 71 period, the main open market operations are held on parameters. The results indicated that the performance a weekly basis (the NBP conducts them on Fridays), so of the neural network model was on average more the forecasting horizon of up to five working days is at a accurate than ARIMA, however comparison of predictive similar level of importance. accuracy with the use of a Diebold-Mariano test didn’t The daily banking sector liquidity forecast is show a statistically significant difference. determined by the accuracy of its individual components. Dheerasinghe R. (2006) modelled Currency in While some items are under the central bank’s control, Circulation for Sri Lanka with the use of three methods. others are influenced by external factors, which are Three separate models were estimated with daily, weekly under direct control of the central bank in the short- and monthly time series, assembling tools for forecasting run. Those autonomous factors determine the change trends, seasonal patterns and cycles in separate individual in demand and supply of commercial bank reserves that series. Trend and seasonal effects were identified by the central bank needs to balance by conducting open regressing on trend and seasonal dummies, while cyclical market operations. Especially important are currency in dynamics were captured by allowing for an ARMA effect circulation (with vault cash) and government deposits in the regression disturbances. The author concluded (in domestic currency), whose movements increase that all three models fit the data well and give very small or decrease the amount of commercial bank reserves estimation errors, which were less than 1% in all models and directly influence liquidity conditions. Whereas the tested in the post sample period. government deposit is the most unpredictable item in Lang M., Kunovac D., Basač S. and Štaudinger Ž. liquidity forecasting, currency in circulation shows some (2008) described two time series models for forecasting periodic patterns. This facilitates forecasting the short- Currency in Circulation outside banks in Croatia, i.e. term movement of currency. Due to the importance regression (REG) and ARIMA models. Both models of currency in circulation for conducting monetary generated good short-term forecasts, completing each policy, many central banks have developed and applied other in the sense that the REG outperformed the econometric models for forecasting purposes. ARIMA model on the interval of up to 5 days ahead and One of the first papers describing the importance of on the other hand, the ARIMA model outperformed the modelling Currency in Circulation was the publication by first one in longer horizons. The authors stated that due Cabrero A., Camba-Mendez G., Hirsch A., Nieto F. (2002), to differences in the approach of those two models, in which modelling the daily series of banknotes in combination of forecasts would outperform them. circulation in the context of the liquidity management of In Turkey, daily changes of Currency in Circulation the Eurosystem was presented. The authors applied two has been modelled by Güler H. and Talasli A. (2010). approaches, the ARIMA-based approach proposed by The authors introduced the ARIMA-GARCH(1,1) based Bell W. and Hillmer S. (1983) and Structural Time Series approach to model seasonality in daily time series and www.e-finanse.com 66 University of Information Technology and Management in Rzeszów Witold Koziński, Tomasz Świst Short-term currency in circulation forecasting for monetary policy purposes – the case of Poland „e-Finanse” 2015, vol. 11/ nr 1 evaluated forecasting performance. The results show 2) the day effect in the month (denoted further as: that the forecasting performance of the model is better D1, … , D31), than the expert judgement both in the long and short 3) the week effect in the month (denoted further run. Moreover the authors emphasized that the model as: W1, … , W5; where the 5th week is incomplete), has to be continuously revised to improve the quality of 4) the month effect in
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