Among the most important tasks of central banks is to ensure the availability of cash to credit institutions and retailers. Forecasting the demand for cash on a granular level is crucial in the process to keep logistics costs low, while being resilient to demand or supply shocks. Whereas to...

This paper contributes a multivariate forecasting comparison between structural models and Machine-Learning-based tools. Specifically, a fully connected feed forward nonlinear autoregressive neural network (ANN) is contrasted to a well established dynamic stochastic general equilibrium (DSGE)...

Using two market-view variables, namely the regulatory forbearance fraction imbedded in the bank capital and the market-valued of the bank equity-to-assets ratio, derived from market equity and total liabilities from listed commercial banks in the U.S. and three countries (Japan, China, India)...

The issue of long-term forecasting for the Russian financial sector has come to the fore after the economy and the financial system have suffered substantial shocks in recent years. It is extremely important for economic agents to understand what the Russian financial system would be on the...

Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to selection of the set of forecasts to combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against...