Underlying, or core, inflation is likely the most important variable for monetary policy. It is considered to be the optimal nominal anchor as it is stable, excludes relative price shocks, and reflects underlying trends in the behaviour of price-setters and demand conditions in the economy. Despite its importance, there is sparse literature on estimating and forecasting core inflation in South Africa, with the focus still on measuring it. This paper emphasizes predicting core inflation from time-varying parameter vector autoregressive models (TVP-VARs), factor-augmented VARs (FAVAR), and structural break models using quarterly data from 1981Q1 to 2013Q4. We use mean squared forecast errors (MSFE) and predictive likelihoods to evaluate the forecasts. In general, we find that (i) time-varying parameter models consistently outperform constant coefficient models (ii) small TVP-VARs outperform all other models; (iii) models with heteroscedastic errors do better than models with homoscedastic errors; and (iv) allowing for structural breaks does not improve the predictability of core inflation. Overall, our results imply that additional information on the growth rate of the economy and the interest rate is sufficient to forecast core inflation accurately, but the relationship between these three variables needs to be modelled in a time-varying fashion.

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.

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.

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.

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

This paper analyses whether we can predict South African excess stock returns based on a measure of economic policy uncertainty (EPU) of South Africa and twenty other developed and emerging markets. In this regard, we use a Bayesian graphical model estimated over the sample period of 1998:01-2012:12. The model is also estimated in a rolling-window fashion over the monthly sample period of 2003:01-2012:03, using an initial sample period of 1998:01-2002:12. The Bayesian shrinkage approach allows us to simultaneously model the 21 EPUs, over and above 22 other standard financial and macroeconomic predictors. In addition, the Bayesian graphical model also provides both instantaneous and lagged relationships between the predictors and the equity premium. Our full sample results show that, in terms of instantaneous relationship, none of the EPUs play any role, and for the lagged relationship, only the EPU of Hong Kong and the Netherlands can be considered as important with posterior inclusion probabilities in excess of 0.50. Rolling estimates show that instantaneous relationships are quite constant and do not indicate any significant links from EPUs to the equity premium. On the other hand, rolling estimates are highly time-varying, and there is significant lagged impact from most of the EPUs in various sub-periods.

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 2000:2-2011:12, 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 yield economically significant out-of-sample gains, especially during recessions, when compared to the historical benchmark. (C) 2014 Elsevier B.V. All rights reserved.

In this paper, we test for the structural stability of both bivariate and multivariate predictive regression models for equity premium in South Africa over the period of 1990:01 to 2010:12, based on 23 financial and macroeconomic variables. We employ a wide range of methodologies, namely, the popular Andrews (1993) SupF statistic and the Bai (1997) subsample procedure in conjunction with the Hansen (2000) heteroskedastic fixed-regressor bootstrap. We also used the Elliott and Muller (2003) J statistic and Bai and Perron (1998, 2003a, 2004) methodologies. We find strong evidence of at least two structural breaks in 22 of 23 bivariate predictive regression models. We also obtain evidence of structural instability in the multivariate predictive regression models of equity premium. Our results also show that the predictive ability of the 23 variables can vary widely across different regimes.

In this paper, we investigate the dynamic relationship between different oil price shocks and the South African stock market using a sign restriction structural 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 a policy and making investment decisions.

Following the recent recession, major global economies are still experiencing weak recoveries. The likelihood that the global economy may experience a double-dip recession driven by poor performance by advanced economies stresses the need for predicting the behaviour of leading indicators such as stock returns and equity premium. An understanding of market behaviour helps in guiding both policy and trading decisions. The main objective of this thesis is to assess the predictability, spillovers and determinants of stock returns in South Africa. Stock returns are determined by a number of financial and macroeconomic variables including valuation ratios (price-earnings ratio and price-dividend ratio), payout ratio, interest rates, the term spread, stock returns of South Africa‟s major trading partners, the inflation rate, money stock, industrial production and the employment rate, world oil production, the refiner acquisition cost of imported crude oil, global activity index, industrial stock returns and financial stock returns. A number of econometric models are used in investigating the determinants, predictability and spillovers of the stock returns – including; predictive regressions using in-sample and out-ofsample test statistics (t-statistics, MSE-F and the ENC-NEW, , utility gains, forecasting encompassing test); exponential smooth-transition autoregressive; Monte Carlo simulations; data-mining-robust bootstrap procedure; in-sample general-to-specific model selection, bootstrap aggregating, combining method (simple averages, discounting, clusters, principal components, Bayesian regression methods under the Gaussian and doubleexponential prior); sign restriction VAR and a TVP-VAR model specification with stochastic volatility. The results show that firstly, the stock returns are determined by certain financial and macroeconomic variables (assessing both the statistical and economic significance). Secondly, South African stock returns react differently to different types of oil shocks – suggesting that the cause of the oil price shock is crucial in determining policy. The combination model forecasts, especially the Bayesian regression methods, outperform the benchmark model (AR(1)/random walk model). Further, the analysis does not only show evidence of significant spillovers to consumption and interest rate from the stock market, but, more importantly, it also highlights the fact that these effects have significantly varied over time.

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 in-sample 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-month-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 in-sample 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 in-sample general-to-specific model selection with out-of-sample tests of predictive ability to further 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-of-sample 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-of-sample 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 model also highlights the role of multiple variables in predicting stock returns at medium- to long run horizons.

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.

This paper assesses the impact of a monetary policy shock on 15 key macroeconomic variables of South Africa, in the pre- and post-inflation targeting periods. For this purpose, we use a Factor-Augmented Vector Autoregressive (FAVAR) model comprising of 107 monthly time series over two equal sub-samples of 1989:01–1997:12 and 2000:01–2008:12. The results, based on impulse response functions, are in line with economic theory and indicate no puzzling effects often observed with small-scale monetary Vector Autoregressive (VAR) models. More importantly, we find that the ability of monetary policy in affecting key macroeconomic variables, including inflation, has increased in the post-targeting period. But, majority of the effects are insignificant, which could, however, also be due to the shorter-lengths of the sub-samples relative to the number of variables used in this study, rather than depicting the inability of monetary policy to significantly affect the South African economy.