Mampho P. Modise

Do Stock Prices Impact Consumption and Interest Rate in South Africa? Evidence from a Time-Varying Vector Autoregressive Model

This article investigates the existence of spillovers from stock prices onto consumption and the interest rate for South Africa using a time-varying parameter vector autoregressive (TVP-VAR) model with stochastic volatility. In this regard, we estimate a three-variable TVP-VAR model comprising real consumption growth rate, the nominal three-months Treasury bill rate and the growth rate of real stock prices. We find that the impact of a real stock price shocks on consumption is in general positive, with large and significant effects observed at the one-quarter-ahead horizon. However, there is also evidence of significant negative spillovers from the stock market to consumption during the financial crisis, at both short and long horizons. The monetary policy response to stock price shocks has been persistent, and strong especially post the financial liberalisation in 1985, but became weaker during the financial crisis. Overall, we provide evidence of significant time-varying spillovers on consumption and interest rate from the stock market. JEL Classification: C11, C15, C32, E31, E32, E44, E52

Valuation Ratios and Stock Return Predictability in South Africa: Is It There?

Using monthly South African data for January 1990 through October 2009, this paper, to the best of our knowledge, is the first to examine the predictability of real stock return based on valuation ratios, namely, price-dividend and price-earnings ratios. We cannot detect either short-horizon or long-horizon predictability; that is, the hypothesis that the current value of a valuation ratio is uncorrelated with future stock price changes cannot be rejected at both short and long horizons based on bootstrapped critical values constructed from linear representations of the data. We find, via Monte Carlo simulations, that the power to detect predictability in finite samples tends to decrease at long horizons in a linear framework. Although Monte Carlo simulations applied to exponential smooth-transition autoregressive models of the price-dividend and price-earnings ratios show increased power, the ability of the nonlinear framework in explaining the pattern of stock return predictability in the data does not show any promise at either short or long horizons, just as in the linear predictive regressions.

The impact of oil shocks on the South African economy

ABSTRACT The recent increases in oil prices have raised the importance of studying the effects of oil supply and demand shocks on an economy. The purpose of this paper is to investigate the impact of the oil supply and demand shocks on the South African economy using a sign restriction-based structural Vector Autoregressive (VAR) model. The results of this study show that an oil supply shock has a short-lived significant impact only on the inflation rate, while the impact on the other variables is statistically insignificant. Supply disruptions result in a short-term increase in the domestic inflation rate with no reaction from the monetary policy. An aggregate demand shock results in short- to medium-term improvements in domestic output and the real exchange rate. The effect is statistically insignificant for the inflation rate as well as the monetary policy instrument. The inflation rate and the real exchange rate react negatively to an oil-specific demand shock, while output is positively related to unanticipated changes in oil price due to speculations. This study’s results highlight the importance of understanding the source of the oil price movements, since an oil price increase necessarily does not imply a negative effect on the economy.

Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors

ABSTRACT This article uses a predictive regression framework to examine the out-of-sample predictability of South Africa’s equity premium, using a host of financial and macroeconomic variables. We employ various methods of forecast combination, bootstrap aggregation (bagging), diffusion index (principal component), and Bayesian regressions to allow for a simultaneous role of the variables under consideration, besides individual predictive regressions. We assess both the statistical and economic significance of the individual predictive regressions, combination methods, bagging, principal components, and Bayesian regressions. Our results show that forecast combination methods and principal component regressions improve the predictability of the equity premium relative to the benchmark autoregressive model of order one (AR[1]). However, the Bayesian predictive regressions are found to be the standout performers with the models outperforming the individual regressions, forecast combination methods, bagging and principal component regressions, both in terms of statistical (forecasting) and economic (utility) gains.

DSGE Model-Based Forecasting of Modeled and Non-Modeled Inflation Variables in South Africa

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 measures of inflation that might be of interest to policymakers, do not feature in the model. Given this, we implement a closed-economy New Keynesian DSGE model-based procedure which includes variables that do not explicitly appear in the model. We estimate such a model using an in-sample covering 1971Q2 to 1999Q4, and generate recursive forecasts over 2000Q1-2011Q4. The hybrid DSGE performs extremely well in forecasting inflation variables (both core and non-modeled) in comparison with forecasts reported by other models such as AR(1).

DSGE model-based forecasting of modelled and nonmodelled inflation variables in South Africa

Inflation forecasts are a key ingredient for monetary policy-making – 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, for example alternative measures of inflation that might be of interest to policy-makers, do not feature in the model. Given this, we implement a closed-economy New Keynesian DSGE model-based procedure which includes variables that do not explicitly appear in the model. We estimate such a model using an in-sample covering 1971Q2 to 1999Q4 and generate recursive forecasts over 2000Q1 to 2011Q4. The hybrid DSGE performs extremely well in forecasting inflation variables (both core and nonmodelled) in comparison with forecasts reported by other models such as AR(1). In addition, based on ex-ante forecasts over the period 2012Q1–2013Q4, we find that the DSGE model performs better than the AR(1) counterpart in forecasting actual GDP deflator inflation.