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In this paper we analyze whether presidential approval ratings can predict the S&P500 returns over the monthly period of 1941:07 to 2018:04, using a dynamic conditional correlation multivariate generalized autoregressive conditional heteroscedasticity (DCC-MGARCH) model. Our results show that, standard linear Granger causality test fail to detect any evidence of predictability. However, the linear model is found to be misspecified due to structural breaks and nonlinearity, and hence, the result of no causality from presidential approval ratings to stock returns cannot be considered reliable. When we use the DCC-MGARCH model, which is robust to such misspecifications, in 69 percent of the sample period, approval ratings in fact do strongly predict the S&P500 stock return. Moreover, using the DCC-MGARCH model we find that presidential approval rating is also a strong predictor of the realized volatility of S&P500. Overall, our results highlight that presidential approval ratings is helpful in predicting stock return and volatility, when one accounts for nonlinearity and regime changes through a robust time-varying model.

We analyse the dynamics of the causal interaction between the stock and foreign exchange markets for the United Kingdom using monthly data going as far back as 1791. First, we consider static causality tests, yielding mixed results. Given the evidence of structural breaks in the relationship between equity and currency returns, we use next the Dynamic Conditional Correlation-Multivariate Generalised Autoregressive Conditional Heteroskedasticity time-varying tests for Granger causality. The time-varying testing strategy we implement allows us to detect whether any causal relationship exists at each point in time between stock price and exchange rates returns. We find overwhelming evidence of time-varying information spillovers between the equity and currency returns. We check the robustness of our findings by running the entire battery of tests for two emerging market economies, namely, India and South Africa starting in 1920 and 1910 respectively. On the whole, the United Kingdom results are comparable to those in India and South Africa. As such, our results encompass the fragmented findings from our static tests as well as those in the extant literature.

The conduct of inflation targeting is heavily dependent on accurate inflation forecasts. Non-linear models have increasingly featured, along with linear counterparts, in the forecasting literature. In this study, we focus on forecasting South African inflation by means of non-linear models and using a long historical dataset of seasonally adjusted monthly inflation rates spanning from 1921:02 to 2013:01. For an emerging market economy such as South Africa, non-linearities can be a salient feature of such long data, hence the relevance of evaluating non-linear models’ forecast performance. In the same vein, given the fact that 1969:10 marks the beginning of a protracted rising trend in South African inflation data, we estimate the models for an in-sample period of 1921:02–1966:09 and evaluate 1, 4, 12, and 24 step-ahead forecasts over an out-of-sample period of 1966:10–2013:01. In addition, using a weighted loss function specification, we evaluate the forecast performance of different non-linear mo...

In this paper we analyze whether a news-based measure of financial stress index (FSI) in the US can predict West Texas Intermediate oil returns and (realized) volatility over the monthly period of 1889:01 to 2016:12, using a dynamic conditional correlation multivariate generalized autoregressive conditional heteroscedasticity (DCC-MGARCH) model. Our results show that, standard linear Granger causality test fail to detect any evidence of predictability. However, the linear model is found to be misspecified due to structural breaks and nonlinearity, and hence, the result of no causality from FSI to oil returns and volatility cannot be considered reliable. When we use the DCC-MGARCH model, which is robust to such misspecifications, in 75 percent and 80 percent of the sample periods, FSI in fact do strongly predict the oil returns and volatility respectively. Overall, our results highlight that FSI is helpful in predicting oil returns and volatility, when one accounts for nonlinearity and regime changes through a robust time-varying model.

This paper investigates the existence of significant spillovers from the housing sector onto the wider economy for eight OECD countries in a six-variable structural vector autoregressive model (SVAR). A housing demand shock is identified through the recursive Choleski decompostion and, subsequently by using Uhlig’s (2005) agnostic identification procedure. The latter allows a housing demand shock to be identified by imposing sign restrictions on the impulse responses of consumer prices, residential investment, real house prices and mortgage loans, while private consumption and nominal interest rate responses are left unrestricted. The results suggest that consumption responds positively and significantly to a house price shock in Canada, France, Japan, Spain and, the UK. A significant positive delayed response of nominal interest rates follows a house price shock in Germany, Japan, the UK and, the US, suggesting that while central banks do not seem to respond instantly and systematically to a housing demand shock, its repercussions on the economy tend to translate into higher policy rates after a few quarters.

This study investigates the trade effects of the EU-SA and SADC preferential trade agreements of which South Africa is a member. Using a panel data estimation of the gravity model of bilateral trade and based on data from 1994 to 2008, the study finds the EU-SA preferential trade agreement to have a significant trade expansion effect. The study further reveals that an informative conclusion on trade effects of the SADC preferential trade agreement can only be reached once the agreement has been fully operational. The study also recommends that trade policy in South Africa should increasingly be geared towards broad-based multilateral liberalisation. In addition, South Africa should promote regional economic stability and development through supporting regional trade agreements initiatives. Keywords: Trade creation, trade diversion, preferential trade agreement, panel data estimation, gravity model of bilateral trade

This study tests for house price bubbles in the South African housing market using quarterly data from 1969:Q2 to 2009:Q3, based on the unit root test developed by Phillips, Wu, and Yu (2010). The ...

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.

In ation forecasts are a key ingredient for monetary policymaking - especially in an in ation 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 in ation 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 in ation variables (both core and non-modeled) in comparison with forecasts reported by other models such as AR(1).

The conduct of in ation targeting is heavily dependent on accurate in ation forecasts. Non-linear models have increasingly featured, along with linear counterparts, in the forecasting literature. In this study, we focus on forecasting South African in ation by means of non-linear models and using a long historical dataset of seasonally-adjusted monthly in ation rates spanning from 1921:02 to 2013:01. For an emerging market economy such as South Africa, non-linearities can be a salient feature of such long data, hence the relevance of evaluating non-linear models' forecast performance. In the same vein, given the fact that 1969:10 marks the beginning of a protracted rising trend in South African in ation data, we estimate the models for an in-sample period of 1921:02-1966:09 and evaluate 24 step-ahead forecasts over an out-of-sample period of 1966:10-2013:01. In addition, using a weighted loss function specication, we evaluate the forecast performance of dierent non-linear models across various extreme economic environments and forecast horizons. In general, we nd that no competing model consistently and signicantly beats the LoLiMoT's performance in forecasting South African in ation.

The conduct of inflation targeting is heavily dependent on accurate inflation forecasts. Non-linear models have increasingly featured, along with linear counterparts, in the forecasting literature. In this study, we focus on forecasting South African infl ation by means of non-linear models and using a long historical dataset of seasonally-adjusted monthly inflation rates spanning from 1921:02 to 2013:01. For an emerging market economy such as South Africa, non-linearities can be a salient feature of such long data, hence the relevance of evaluating non-linear models' forecast performance. In the same vein, given the fact that 1969:10 marks the beginning of a protracted rising trend in South African inflation data, we estimate the models for an in-sample period of 1921:02-1966:09 and evaluate 24 step-ahead forecasts over an out-of-sample period of 1966:10-2013:01. In addition, using a weighted loss function specification, we evaluate the forecast performance of different non-linear models across various extreme economic environments and forecast horizons. In general, we find that no competing model consistently and significantly beats the LoLiMoT's performance in forecasting South African inflation.

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, 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).

This paper investigates the existence of significant spillovers from the housing sector onto the wider economy for the seven major OECD countries using Uhlig's (2005) agnostic identification procedure. This method allows a housing demand shock to be identified in a six-variable VAR model by imposing sign restrictions on the impulse responses of consumer prices, residential investment, real house prices and mortgage loans, while private consumption and nominal interest rate responses are left unrestricted. The results suggest that consumption responds positively and significantly to a house price shock in Canada, France, Japan and the UK. A significant positive delayed response of nominal interest rates follows a house price shock in Germany, Japan, the UK and the US, suggesting that while central banks do not seem to respond instantly and systematically to a housing demand shock, their repercussions on the economy tend to translate into higher policy rates after a few quarters.

The real interest rate is a very important variable in the transmission of monetary policy. It features in vast majority of financial and macroeconomic models. Though the theoretical importance of the real interest rate has generated a sizable literature that examines its long-run properties, surprisingly, there does not exist any study that delves into this issue for South Africa. Given this, using quarterly data (1960:Q2-2010:Q4) for South Africa, our paper endeavors to analyze the long-run properties of the ex post real rate (EPRR) by using tests of unit root, cointegration, fractional integration and structural breaks. In addition, we also analyze whether monetary shocks contribute to fluctuations in the real interest rate based on test of structural breaks of the rate of inflation as well as Bayesian change point analysis. Based on the tests conducted, we conclude that the South African EPPR can be best viewed as a very persistent but ultimately mean-reverting process. Also, the persistence in the real interest rate can be tentatively considered as a monetary phenomenon.