Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets

Abstract

This paper characterizes the dynamics of jumps and analyzes their importance for volatility forecasting. Using high‐frequency data on four prominent energy markets, we perform a model‐free decomposition of realized variance into its continuous and discontinuous components. We find strong evidence of jumps in energy markets between 2007 and 2012. We then investigate the importance of jumps for volatility forecasting. To this end, we estimate and analyze the predictive ability of several Heterogenous Autoregressive (HAR) models that explicitly capture the dynamics of jumps. Conducting extensive in‐sample and out‐of‐sample analyses, we establish that explicitly modeling jumps does not significantly improve forecast accuracy. Our results are broadly consistent across our four energy markets, forecasting horizons, and loss functions. © 2015 Wiley Periodicals, Inc. Jrl Fut Mark 36:758–792, 2016

Forecasting realized volatility of oil futures market: A new insight

“…Nevertheless, the HAR-RV model is of great interest to us here because there is an extremely limited strand of the literature focusing on the oil price-realized volatility forecasting using this model (Degiannakis & Filis, 2017;Haugom, Langeland, Molnár, & Westgaard, 2014;Liu & Wan, 2012;Ma, Wahab, Huang, & Xu, 2017;Prokopczuk, Symeonidis, & Wese Simen, 2016;Sévi, 2014). Recently, Patton and Sheppard (2015) used the realized semi-variances proposed by Barndorff-Nielsen, Kinnebrock, and Shephard (2010), which decompose the realized variance into a component that relates only to positive high-frequency returns ("good" volatility) and into a component that relates only to negative high-frequency returns ("bad" volatility).…”

Measuring the output gap in Switzerland with linear opinion pools

“…Point forecasts are the conditional means of the forecast densities. 13 We use a Newey and West (1987) HAC variance estimator and set the bandwidth parameter equal to h − 1 (see also Patton and Sheppard (2015), Prokopczuk et al (2016) who use the same bandwidth setting strategy). 14 Note that the evaluation period is reduced in length when the forecast horizon is larger than (h=1).…”

The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility

“…Several studies have considered the use of jumps and signed jumps to forecast realized volatility. (See, for instance, Andersen, Bollerslev, & Diebold, 2007;Busch, Christensen, & Nielsen, 2011;Corsi et al, 2010;Duong & Swanson, 2015;Forsberg & Ghysels, 2007;Ghysels & Sohn, 2009;Giot & Laurent, 2007;Martens, Van Dijk, & De Pooter, 2009;Patton & Sheppard, 2015;Prokopczuk, Symeonidis, & Wese Simen, 2016;Sévi, 2014, and references therein. ).…”

“…In contrast to these positive findings, many researchers report that jumps do not significantly improve volatility forecasts. For instance, Forsberg and Ghysels (2007), Giot and Laurent (2007), Martens et al (2009), Busch et al (2011, Sévi (2014), and Prokopczuk et al (2016) consider the use of both total and signed jumps to forecast future volatility. Their results suggest that the inclusion of jumps produces a better fitting in-sample model, but does not generate any significant out-of-sample forecasting gains.…”