Forecasting for Banking Using Time Series Methods

This course provides an introduction to time series methods for modeling and forecasting economic and financial variables within a banking context. Forecasting plays a crucial role in decision-making for both the public and private sectors and is especially significant for banking institutions, whether in management or supervision. Banks’ assets and liabilities are shaped by various factors, including economic and financial conditions, interest rates, and the prices of financial assets. These variables emerge from a vast, complex, dynamic, and stochastic system, making accurate forecasting a challenging task where errors are inevitable. However, by applying appropriate econometric models and methods, forecast errors can be minimised, and forecasting precision can be significantly improved.

Topics 

• Module 1: Modelling economic and financial variables (ARMA) + Empirical application in Eviews using simulated and actual economic and financial data 
• Module 2: Forecasting economic and financial variables using the ARMA model + Empirical application in Eviews using simulated and actual economic and financial data (focusing on forecasting and forecasting performance) 
• Module 3: Modelling and forecasting economic and financial variables using VAR models + Empirical application in Eviews using simulated and actual economic and financial data (focusing on forecasting and shock transmission) 
• Module 4: Cointegration and error correction models + Empirical application in Eviews using simulated and actual economic and financial data (focusing on cointegration analysis and forecasting)