Estimating a timevarying financial conditions index for South Africa

This paper uses 39 monthly time series of the financial market observed from January 2000 to April 2017 to estimate a financial conditions index (FCI) for South Africa. The empirical technique used is a dynamic factor model with time-varying factor loadings proposed by Koop and Korobilis (2014) based on the principal component analysis and the Kalman smoother. In addition, we estimate a time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model, which includes, in addition to the FCI, two observed macroeconomic variables. The results show the ability of the estimated FCI to predict risks in the financial market emanating from both the domestic market and the global market. Furthermore, the TVP-FAVAR model outperforms the constant-loadings factor-augmented vector autoregressive (FAVAR) model and the traditional vector autoregressive (VAR) model in the out-of-sample forecasting of the inflation rate and the real gross domestic product (GDP) growth rate. Finally, tighter financial conditions contract the real economy and are deflationary at the same time. Importantly, the responses of macroeconomic variables vary over time.