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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.

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.

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).