Forecasting the price of gold using dynamic model averaging

Abstract

We develop several models to examine possible predictors of the return of gold, which embrace six global factors (business cycle, nominal, interest rate, commodity, exchange rate and stock price) extracted from a recursive principal component analysis (PCA) and two uncertainty and stress indices (the Kansas City Fed's financial stress index and the U.S. Economic policy uncertainty index). Specifically, by comparing alternative predictive models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform linear models (such as the random walk) as well as the Bayesian model averaging (BMA) model. The DMS is the best predictive model overall across all forecast horizons. Generally, all the predictors show strong predictive power at one time or another though at varying magnitudes, while the exchange rate factor and the Kansas City Fed's financial stress index appear to be strong at almost all horizons and sub-periods. However, the forecasting prowess of the exchange rate is supreme. JEL Classification codes: C11, C53, F37, F47, Q02 Abstract We develop several models to examine possible predictors of the return of gold, which embrace six global factors (business cycle, nominal, interest rate, commodity, exchange rate and stock price) extracted from a recursive principal component analysis (PCA) and two uncertainty and stress indices (the Kansas CityFed's financial stress index and the U.S. Economic policy uncertainty index). Specifically, by comparing alternative predictive models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform linear models (such as the random walk) as well as the Bayesian model averaging (BMA) model. The DMS is the best predictive model overall across all forecast horizons.Generally, all the predictors show strong predictive power at one time or another though at varying magnitudes, while the exchange rate factor and the Kansas City Fed's financial stress index appear to be strong at almost all horizons and sub-periods. However, the forecasting prowess of the exchange rate is supreme.JEL Classification codes: C11, C53, F37, F47, Q02

The Impact of Economic Policy Uncertainty on the Vehicle Miles Traveled (Vmt) in the U.S.

“…This study investigates the effects of uncertainty on vehicles miles travelled in the U.S.by using monthly data from the 1970s to 2014.EPU index developed by Baker et al (2015) reflects information about the media coverage of political uncertainty, tax expiration codes, and disagreement among economic forecasters. EPU has recently received increasing attention and it is used to assess the impact of policy uncertainty on various variables, such as the firm-level decisions (Francis et al 2014;Gulen and Ion, 2016;Kang et al, 2014;Wang et al 2014), the oil price (Bekiros et al 2015), the gold price (Aye et al 2015), macroeconomic indicators (Bhagat and Ghosh, 2014), and the stock prices (Antonakakis et al 2013;Ko and Lee, 2015;Demir and Oguz, 2015;Sum, 2013). To the best of our knowledge, there is no study that uses the EPU indexes to analyze the effects of policy uncertainty on the travel demand.…”

Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework

“…Therefore, the improvement gained from normalization can sometimes even decide whether a model can beat benchmark forecasts (i.e., produce smaller MSE). It is worth to notice that in the already performed financial applications of DMA [11,[13][14][15][16][17][18]] explicit data normalization has not been considered. The original time-series have usually been taken in 1st differences in order to obtain stationarity.…”

“…Indeed, matrices V t (k) and W t (k) are time-varying. In the previous applications of DMA to economic and financial problems their Authors have been using stationary data [14][15][16][17][18], but it was rather due to the common habit in economic research, than any real necessity for the applied method.…”

“…It is worth noting that such an approach has recently been applied in economics and finance [11][12][13][14][15][16][17]. However, DMA has still not been studied too extensively.…”

A real-time quantile-regression approach to forecasting gold returns under asymmetric loss

“…As a result, researchers have studied gold returns by applying flexible forecasting approaches that account for model uncertainty and model instability (see also Vrugt et al, 2007;Aye et al, 2015;Baur et al, 2014;Pierdzioch et al, 2014aPierdzioch et al, , 2014bPierdzioch et al, , 2015. Model uncertainty arises because gold returns may be linked to a potentially large number of determinants, none of which can be excluded a priori on economic grounds.…”