| By: |
Fantazzini, Dean;
Kurbatskii, Alexey;
Mironenkov, Alexey;
Lycheva, Maria |
| Abstract: |
This paper investigates whether augmenting models with the variance risk
premium (VRP) and Google search data improves the quality of the forecasts for
real oil prices. We considered a time sample of monthly data from 2007 to 2019
that includes several episodes of high volatility in the oil market. Our
evidence shows that penalized regressions provided the best forecasting
performances across most of the forecasting horizons. Moreover, we found that
models using the VRP as an additional predictor performed best for forecasts
up to 6-12 months ahead forecasts, while models using Google data as an
additional predictor performed better for longer-term forecasts up to 12-24
months ahead. However, we found that the differences in forecasting
performances were not statistically different for most models, and only the
Principal Component Regression (PCR) and the Partial least squares (PLS)
regression were consistently excluded from the set of best forecasting models.
These results also held after a set of robustness checks that considered model
specifications using a wider set of influential variables, a Hierarchical
Vector Auto-Regression model estimated with the LASSO, and a set of
forecasting models using a simplified specification for Google Trends data. |
| Keywords: |
Oil price; Variance Risk Premium; Google Trends; VAR; LASSO; Ridge; Elastic Net; Principal compo-nents, Partial least squares |
| JEL: |
C22 C32 C52 C53 C55 C58 G17 O13 Q47 |
| Date: |
2022 |
| URL: |
http://d.repec.org/n?u=RePEc:pra:mprapa:118239&r=for |