We examine both in-sample and out-of-sample predictability of South African stock return using macroeconomic variables. We base our analysis on a predictive regression framework, using monthly data covering the in-sample period between 1990:01 and 1996:12, and the out-of sample period commencing...

The global financial crisis that began in 2007-08 demonstrated how severe the impact of financial markets’ stress on real economic activity can be. In the wake of the financial crisis policy-makers and decision-makers across the world identified the critical need for a better understanding of...

Inflation forecasts are a key ingredient for monetary policymaking - especially in an inflation targeting country such as South Africa. Generally, a typical Dynamic Stochastic General Equilibrium (DSGE) only includes a core set of variables. As such, other variables,e.g. such as alternative...

This article attempts to examine whether the equity premium in the United States can be predicted from a comprehensive set of 18 economic and financial predictors over a monthly out-of-sample period of 2000:2 to 2011:12, using an in-sample period of 1990:2-2000:1. To do so, we consider, in...

This paper analyses the out-of-sample forecasting performance of non-linear vs. linear models for the South African Rand against the United States dollar and the British Pound, in real terms. We compare the forecasting performance of point, interval and density forecasts for non-linear Band- TAR...

Traditionally, the literature on forecasting exchange rates with many potential predictors have primarily only accounted for parameter uncertainty using Bayesian Model Averaging (BMA). Though BMA-based models of exchange rates tend to outperform the random walk model, we show that when...

This paper analyses the out-of-sample forecasting performance of non-linear vs. linear models for the South African rand against the United States dollar and the British pound, in real terms. We compare the forecasting performance of point, interval and density forecasts for non-linear Band-TAR...

This paper analyzes whether a wealth of information contained in 126 monthly series used by large-scale Bayesian Vector Autoregressive (LBVAR) models, as well as Factor Augmented Vector Autoregressive (FAVAR) models, either Bayesian or classical, can prove to be more useful in forecasting real...

This paper develops large-scale Bayesian Vector Autoregressive (BVAR) models, based on 268 quarterly series, for forecasting annualized real house price growth rates for large-, medium- and small-middle-segment housing for the South African economy. Given the in-sample period of 1980:01 to...