In this paper, we investigate the dynamic relationship between different oil price shocks and the South African stock market using a sign restriction structural vector autoregression (VAR) approach for the period 1973:01 to 2011:07. The results show that for an oil-importing country like South Africa, stock returns only increase with oil prices when global economic activity improves. In response to oil supply shocks and speculative demand shocks, stock returns and the real price of oil move in opposite directions. The analysis of the variance decomposition shows that the oil supply shock contributes more to the variability in real stock prices. The main conclusion is that different oil price shocks affect stock returns differently and policy makers and investors should always consider the source of the shock before implementing policy and making investment decisions.JEL classifications: C32, C58, G1, Q43

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 addition to the set of variables used in Rapach and Zhou (2013), the forecasting ability of four other important variables: the US economic policy uncertainty, the equity market uncertainty, the University of Michigan's index of consumer sentiment, and the Kansas City Fed's financial stress index. Using a more recent dataset compared to that of Rapach and Zhou (2013), our results from predictive regressions show that the newly added variables do not play any significant statistical role in explaining the equity premium relative to the historical average benchmark over the out-of-sample horizon, even though they are believed to possess valuable informative content about the state of the economy and financial markets. Interestingly, however, barring the economic policy uncertainty index, the three other indexes considered in this study yields economically significant out-of-sample gains, especially during recessions, when compared to the historical benchmark.JEL classification: C22, C38, C53, C58, E32, G11, G12, G14, G17.

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 from 1997:01 to 2010:06. For the insample test, we use the t-statistic corresponding to the slope coefficient of the predictive regression model, and for the out-of-sample tests we employ the MSE-F and the ENC-NEW test statistics. When using multiple variables in a predictive regression model, the results become susceptible to data mining. To guard against this, we employ a bootstrap procedure to construct critical values that account for data mining. Further, we use a procedure that combines the in-sample general-to-specific model selection with tests of out-of-sample forecasting ability to examine the significance of each macro variable in explaining the stock returns behaviour. In addition, we use a diffusion index approach by extracting a principal component from the macro variables, and test the predictive power thereof. For the in-sample tests, our results show that different interest rate variables, world oil production growth, as well as, money supply have some predictive power at certain short-horizons. For the out-of-sample forecasts, only interest rates and money supply show short-horizon predictability. Further, the inflation rate shows very strong out-of-sample predictive power from 6-months-ahead horizons. A real time analysis based on a subset of variables that underwent revisions, resulted in deterioration of the predictive power of these variables compared to the fully revised data available for 2010:6. The diffusion index yields statistically significant results for only four specific months over the out-of-sample horizon. When accounting for data mining, both the insample and the out-of-sample test statistics for both the individual regressions and the diffusion index become insignificant at all horizons. The general-to-specific model confirms the importance of different interest rate variables in explaining the behaviour of stock returns, despite their inability to predict stock returns, when accounting for data mining.

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines general-to-specific model selection with out-of-sample tests of predictive ability to analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-ofsample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-ofsample predictability at longer horizons is due to data mining. The general-to-specific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon.

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. Our results 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. Our 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. JEL classifications: E13, E63, E66