Modeling the impact of financial innovation on the demand for money in Nigeria
Co-authored with G.O Odularu
The demand for money is a very crucial variable in determining of the effectiveness of monetary policy. This study... more The demand for money is a very crucial variable in determining of the effectiveness of monetary policy. This study attempts to analyse whether financial innovations that occurred in Nigeria after the Structural Adjustment Programme of 1986 has affected the demand for money using the Engle and Granger Two-Step Cointegration technique. Though the study revealed that demand for money conforms to theory in that, income is positively related to the demand for cash balances and interest rate has an inverse relationship with the demand for real cash balances, it is discovered that the financial innovations introduced into the financial system have not significantly affected the demand for money in Nigeria. Based on the results obtained, a policy of attracting more participants (non-government) and private sector funds to the money market is necessary as this will deepen the market and make the market more dynamic and amenable to monetary policy.
Hyperinflation in Zimbabwe: Money Demand, Seigniorage and Aid Shocks
Zimbabwe has recently experienced record hyperinflation of 80 billion percent a month. This paper uses new data from... more Zimbabwe has recently experienced record hyperinflation of 80 billion percent a month. This paper uses new data from Zimbabwe to investigate money demand under hyperinflation using an autoregressive distributed-lag model for the period 1980-2008. The results produce plausible convergence rates and long-run elasticities, indicating that real money balances are cointegrated with the inflation rate signifying an equilibrium relationship between the two series. Evidence is also presented that suggests prices are being driven by increases in the money supply rather than by changes in price setting behaviour. The paper additionally uses the estimated elasticity on the inflation variable to calculate the maximum level of seigniorage revenue that could be raised in the economy. Actual seigniorage levels increased dramatically after 2000, with inflation eventually exceeding the rate required to maximize this revenue stream. This is discussed in relation to international nancing constraints and the collapse of the domestic tax base.
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Download (.pdf) Forecasting Cash Money Withdrawals Using Wavelet Analysis and Wavelet Neural Networks
Co-authored with A. Zapranis. Published in the proc. of 5th Applied Financial Economics (AFE), Samos, Greece, 3-5 July, 2008.
In this paper we use wavelet neural networks to forecast cash money withdrawals in different locations in the UK. Cash... more In this paper we use wavelet neural networks to forecast cash money withdrawals in different locations in the UK. Cash demand needs to be forecasted accurately similarly to other products in vending machines, as an inventory of cash money needs to be ordered and replenished for a set period of time beforehand. If the forecasts are flawed, they induce costs: if the forecast is too high unused money is stored in the ATM incurring costs to the institution, similarly, if the ATM runs out of cash, profit is lost and customers are dissatisfied. Cash money demand represents a non-stationary, heteroscedastic process. The time series exhibits trends, singularities, seasonal and irregular structural components of the data as well as causal forces impacting on the data generating process. Having limited domain knowledge and no information on the causal forces we use wavelet analysis to extract the dynamics of the process. In order to evaluate our method we produce in-sample and out-of-sample forecasts in 11 different time series. The data provided by the Neural Network Association and first presented in the NN5 competition
Forecasting Cash Money Withdrawals Using Wavelet Analysis and Wavelet Neural Networks
Co-authored with A. Zapranis. Accepted to appear in the International Journal of Financial Economics and Econometrics, 2009.
In this paper we use wavelet neural networks to forecast cash money withdrawals in different locations in the UK. Cash... more In this paper we use wavelet neural networks to forecast cash money withdrawals in different locations in the UK. Cash demand needs to be forecasted accurately similarly to other products in vending machines, as an inventory of cash money needs to be ordered and replenished for a set period of time beforehand. If the forecasts are flawed, they induce costs: if the forecast is too high unused money is stored in the ATM incurring costs to the institution, similarly, if the ATM runs out of cash, profit is lost and customers are dissatisfied. Cash money demand represents a non-stationary, heteroscedastic process. The time series exhibits trends, singularities, seasonal and irregular structural components of the data as well as causal forces impacting on the data generating process. Having limited domain knowledge and no information on the causal forces we use wavelet analysis to extract the dynamics of the process. In order to evaluate our method we produce in-sample and out-of-sample forecasts in 11 different time series. The data provided by the Neural Network Association and first presented in the NN5 competition