Forecasting the South African Financial Cycle: A Linear and Non-Linear Approach

Identifying optimal models to forecast economic cycles has been a point of great consideration in literate. A key point of debate in the literature is whether linear or non-linear models perform best at forecasting economic cycles. The literature largely forces on the forecasting of business cycles, and very limited work has been done on financial cycle forecasting. Given the proven destructiveness of financial cycles, the ability to accurately forecast future financial cycle movements in an economy could aid policymakers in managing such cycles. This article evaluates the forecasting performance of both the non-linear Markov Regime-Switching Autoregressive methodology and Smooth Transition Autoregressive methodology relative to the benchmark ARIMA model in forecasting the aggregate South African financial cycle over different time horizons. A fixed window rolling forecast approach is followed, whereby the performance of forecasting the aggregate South African financial cycle 3-steps forward, 6-steps forward, 12-steps forward, 18-steps forward and 24-steps forward is evaluated. The findings indicate that the linear ARIMA model outperforms the non-linear MSMV-AR and LSTAR models at forecasting short periods ahead such as 3–6 months ahead. However, both the MSMV-AR and LSTAR models outperform the ARIMA model, given a longer time horizon such as 12–24 months. Hence, to forecast the aggregate South African financial cycle 3–6 months ahead policymakers should use an ARIMA. However, the MSMV-AR and LSTAR models should be used to forecast the aggregate South African financial cycle 12–24 months ahead.