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

We compare the predictive ability and economic value of implied, realized, and GARCH volatility models for 13 equity indices from 10 countries. Model ranking is similar across countries, but varies with the forecast horizon. At the daily horizon, the Heterogeneous Autoregressive model offers the most accurate predictions, whereas an implied volatility model that corrects for the volatility risk premium is superior at the monthly horizon. Widely used GARCH models have inferior performance in almost all cases considered. All methods perform significantly worse over the 2008-09 crisis period. Finally, implied volatility offers significant improvements against historical methods for international portfolio diversification.

We employ a large dataset of physical inventory data on 21 different commodities for the period 1993-2011 to empirically analyze the behaviour of commodity prices and their volatility as predicted by the theory of storage. We examine two main issues. First, we explore the relationship between inventory and the shape of the forward curve. Low (high) inventory is associated with forward curves in backwardation (contango), as the theory of storage predicts. Second, we show that price volatility is a decreasing function of inventory for the majority of commodities in our sample. This effect is more pronounced in backwardated markets. Our findings are robust with respect to alternative inventory measures and over the recent commodity price boom period.JEL classification: C22, C58, G00, G13

We employ a large dataset of physical inventory data on 21 different commodities for the period 1993-2011 to empirically analyze the behaviour of commodity prices and their volatility as predicted by the theory of storage. We examine two main issues. First, we explore the relationship between inventory and the shape of the forward curve. Low (high) inventory is associated with forward curves in backwardation (contango), as the theory of storage predicts. Second, we show that price volatility is a decreasing function of inventory for the majority of commodities in our sample. This effect is more pronounced in backwardated markets. Our findings are robust with respect to alternative inventory measures and over the recent commodity price boom period.JEL classification: C22, C58, G00, G13

We compare the performance of popular covariance forecasting models in the context of a portfolio of major European equity indices. We find that models based on high-frequency data offer a clear advantage in terms of statistical accuracy. They also yield more theoretically consistent predictions from an empirical asset pricing perspective, and, lead to superior out-of-sample portfolio performance. Overall, a parsimonious Vector Heterogeneous Autoregressive (VHAR) model that involves lagged daily, weekly and monthly realised covariances achieves the best performance out of the competing models. A promising new simple hybrid covariance estimator is developed that exploits option-implied information and high-frequency data while adjusting for the volatility risk premium. Relative model performance does not change during the global financial crisis, or, if a different forecast horizon, or, intraday sampling frequency is employed. Finally, our evidence remains robust when we consider an alternative sample of U.S. stocks.