Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank

Cabrero, Alberto; Camba-Méndez, Gonzalo; Hirsch, Astrid; Nieto, Fernando

doi:10.1002/for.1118

Cited by 17 publications

(16 citation statements)

References 23 publications

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“…Cabrero et al (2009) identify weekly, monthly and annual seasonal patterns, which resemble a certain regularity in payments and behavior. Similarly, the trading day effect results in an increase in the amount of banknotes in circulation just before the weekend that reverses after the weekend.…”

Section: Datamentioning

confidence: 99%

“…Cabrero et al (2009) Diebold and Mariano (1995). Their empirical results suggest the two econometric models explain large parts of the variations in the daily series with the ARIMA model yielding a lower accuracy over forecasting horizons of up to 4 days and higher accuracy for forecasting horizons of more than 4 days compared to the structural time series model.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Daily Demand in Cash Supply Chains

Wagner¹

2010

American Journal of Economics and Business Administration

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Problem statement: Previous studies focused on explaining the long run determinants of currency demand offering limited insight into the short-run determinants and co-variability of daily demand in cash supply chains. Approach: This study contrasted competing techniques of forecasting daily demand in cash supply chains in order to determine the overall performance and the potential of joint forecasting for integrated planning. A joint forecasting approach was compared with wellestablished causal forecasting techniques, namely, a vector time series model and a seasonal ARIMA model using simple methods as benchmarks. Evaluation was based on multiple time series obtained from mid-size European bank with forecasting horizons of up to 28 days. Forecasting accuracy was measured using the mean absolute percentage error. Results: The seasonal ARIMA model resulted in a higher forecasting accuracy compared to the vector time series model. Variability in demand was mainly attributed to the day-of-the-week effect. Co-variability is captured by seasonality and calendar effects limiting the potential of joint forecasting. Cumulative forecasts for periods of 14 days are very robust with mean percentage errors of approximately two percent. Conclusion: The results confirmed the benefit of advanced forecasting techniques for daily forecasts. However, the study suggested that the role of information sharing is limited to coordination of replenishments across the cash supply chain and does not yield more accurate forecasts based on joint forecasting.

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Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Daily Demand in Cash Supply Chains

Wagner¹

2010

American Journal of Economics and Business Administration

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“…Autonomous factors (or autonomous liquidity factors) are components of the central bank balance sheet other than its monetary policy operations and banks reserves. They are called autonomous because they are outside the control of the central bank(Cabrero, Camba-Mendez, Hirsh, and Nieto (2002)), and the main role of the liquidity management is often to offset the liquidity shocks induced by autonomous factors fluctuations.©International Monetary Fund. Not for Redistribution…”

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confidence: 99%

“…Cassino, Misich, and Barry (1997).16 Cabrero, Camba-Mendez, Hirsh, and Nieto (2002).©International Monetary Fund. Not for Redistribution…”

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confidence: 99%

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Monetary Policy and Models of Currency Demand

Khatat

2018

IMF Working Papers

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Banknote Life in India: A Survival Analysis Approach

Tagat,

Özmen,

Markowsky

2024

J. Quant. Econ.

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India’s currency-to-GDP ratio indicates a strong and persistent demand for cash. Despite the withdrawal of two high-value banknotes in 2016 and the recent economic contraction due to the COVID-19 pandemic, cash continues to rule in India. Although there is extensive work on the demand for cash in India, relatively little is known about the quality and supply of banknotes. This paper provides an overview of currency management policies in India using publicly available annual aggregate issuances and disposals data from the Reserve Bank of India (RBI). We model the life of a banknote using survival analysis models, finding that low-value banknotes (INR 10 and 20) have a median life of 4–5 years. The estimates of longevity of a banknote are significantly associated with velocity of circulation as well as exogenous shocks. We demonstrate the value of survival analysis methods in informing currency management policies in India and providing avenues for future work in this domain.

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Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank

Cited by 17 publications

References 23 publications

Forecasting Daily Demand in Cash Supply Chains

Forecasting Daily Demand in Cash Supply Chains

Monetary Policy and Models of Currency Demand

Banknote Life in India: A Survival Analysis Approach

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