Forecasting oil prices with penalized regressions, variance risk premia and Google data

Maria Lycheva (), Alexey Mironenkov (), Alexey Kurbatskii () and Dean Fantazzini
Additional contact information
Maria Lycheva: ICEF, HSE university, Moscow;
Alexey Mironenkov: Moscow School of Economics, Moscow State University
Alexey Kurbatskii: Moscow School of Economics, Moscow State University
Authors registered in the RePEc Author Service: Alexey Nikolaevich Kurbatskiy

Applied Econometrics, 2022, vol. 68, 28-49

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 components; partial least squares (search for similar items in EconPapers)
JEL-codes: C22 C32 C52 C53 C55 C58 G17 O13 Q47 (search for similar items in EconPapers)
Date: 2022
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Working Paper: Forecasting oil prices with penalized regressions, variance risk premia and Google data (2022)
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