## 3 Empirical Results
### Gold Prices  
Finally, we evaluate the ability of the GPR index in forecasting gold prices. Although gold is a commodity, its use as a \"safe-heaven\" investment during periods of economic and political turmoil renders the examination of gold prices based on geopolitical risk parameters very interesting. The dependent variable in all models is the price of gold, with explanatory variables being the lagged price of gold, and the respective GPR index of the developing country under examination. In Table 5, we report the forecasting performance of our forecasting models in forecasting the gold prices, using the GPR index and the lagged value of gold prices as regressors.  
Figure 6: Out-of-sample 1-month ahead exchange rate forecasts for the Shanghai Composite index based on the GPR index for the SVR and OLS model for the period Jan-2017 to May-2018.  
Figure 7: Out-of-sample 12-month ahead exchange rate forecasts for the Shanghai Composite index based on the GPR index for the SVR and OLS model for the period Jan-2017 to May-2018.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Forecasting Horizons** & \multicolumn{3}{c}{**1 Month**} & \multicolumn{3}{c}{**3 Months**} & \multicolumn{3}{c}{**6 Months**} & \multicolumn{3}{c}{**12 Months**} & \multicolumn{3}{c}{**24 Months**}  
As we observe, both models outperform the RW model for most countries and in most forecasting horizons up to 12 months. More specifically, in the one-month forecasting horizon, both models outperform the RW model in the case of Turkey, Korea, Russia, India, Brazil, China, Indonesia, Saudi Arabia, and Argentina. In eight out of these nine countries, the OLS model is more accurate than the SVR. In the three-month forecasting horizon, the list of the nine countries is expanded to 11 countries, adding Israel and Philippines. In nine out of these 11 countries, the OLS model is more accurate that the SVR one. In the six-month forecasting horizon, all models--except those for Malaysia--outperform the RW model, where again in nine out of the 13 countries, the OLS model is the most accurate, while in the 12-month horizon we revert to the initial list of countries of the one-month forecasting horizon. In the longer 24-month forecasting horizon, the ability of all models to outperform the RW model is completely lost. This finding suggests that the GPR indices can forecast gold prices with consistency based on the GPR index of most countries for up to 12 months ahead, while we do not find evidence in favor of using the SVR over the OLS model for one-month up to 12-months ahead forecasting. In Figures 8 and 9, we depict the gold price forecasts on one- and 12-months ahead for China. The selection of China is motivated by the fact that China is the largest gold producing country in the world, with 440 tons in 2017 as documented in [29].  
Apparently, in the shorter horizon, both models adhere more closely to gold prices than in the longest 12-month horizon. The ability of the GPR indices of emerging economies to forecast gold price fluctuations more accurately than the RW model is detected in the case of Turkey, Korea, Russia, India, Brazil, China, Indonesia, Saudi Arabia, and Argentina consistently up to 12-months ahead forecasting, suggesting the direct link between gold prices and risk management investing decisions.