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

Abstract. This paper investigates the empirical association between stock market volatility and investor mood-proxies related to the weather (cloudiness, temperature and precipitation) and the environment (nighttime length). Overall, our results suggest that cloudiness and length of nighttime are inversely related to historical, implied and realized measures of volatility. The strength of association seems to vary with the location of an exchange on Earth with respect to the equator. Weather deviations from seasonal norms and dummies representing extreme weather conditions do not offer additional explanatory power in our datasets. JEL Classification: G14, G32Keywords: Stock market anomalies, Volatility, Sunshine effect, SAD effect, Behavioral Acknowledgements: We would like to thank Mark Kamstra, David Hirshleifer and an anonymous referee for providing us with valuable comments and suggestions which significantly improved the final version of the paper. We assume responsibility for any remaining errors.

We analyze the relationship between economic uncertainty and commodity market volatility. We find that commodity market volatility comoves strongly with economic and financial uncertainty, especially during recessions. Variables associated with credit risk, financial market stress and fluctuations in business conditions bear significant predictive ability for commodity market volatility. The documented predictability is mainly observed in the period after the financialization of commodity markets (i.e. post-2004) and it peaks during the 2008-2009 global financial crisis.

We analyze the variance risk of commodity markets. We construct synthetic variance swaps and find significantly negative realized variance swap payoffs in most markets. We find evidence of commonalities among the realized payoffs of commodity variance swaps. We also document comovements between the realized payoffs of commodity, equity and bond variance swaps. Similar results hold for expected variance swap payoffs. Furthermore, we show that both realized and expected commodity variance swap payoffs are distinct from the realized and expected commodity futures returns, indicating that variance risk is unspanned by commodity futures