Is idiosyncratic volatility priced in commodity futures markets?

Abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent AbstractThis article investigates the relationship between expected returns and past idiosyncratic volatility in commodity futures markets. Measuring the idiosyncratic volatility of 27 commodity futures contracts with traditional pricing models that fail to account for backwardation and contango leads to the puzzling finding that idiosyncratic volatility is significantly negatively priced cross-sectionally. However, idiosyncratic volatility is not priced when the phases of backwardation and contango are suitably factored in the pricing model. A time-series portfolio analysis similarly suggests that failing to recognize the fundamental risk associated with the inexorable phases of backwardation and contango leads to overstated profitability of the idiosyncratic volatility mimicking portfolios.JEL classifications: G13; G14.

Anomalies in Commodity Futures Markets

“…These variables include the commodity market return (e.g., Yang, 2013), the shape of the term structures, hedging pressure (e.g., De Roon et al, 2000;Basu & Miffre, 2013), or a combination of these (e.g., Erb & Harvey, 2006;Gorton & Rouwenhorst, 2006;Szymanowska et al, 2014;Fernandez-Perez et al, 2018;Bakshi et al, 2019). 6 Some previous studies also examine the performance of strategies developed in equity markets on commodity markets (e.g., Erb & Harvey, 2006;Gorton & Rouwenhorst, 2006;Asness et al, 2013;Szymanowska et al, 2014;Fernandez-Perez et al, 2016). Bakshi et al (2019) develop a model for the cross-section of commodity markets, which, among others, also includes a momentum factor.…”

Risk‐neutral skewness and commodity futures pricing

“…We employ more risk factors discussed in the commodity literature along with the baseline model to clarify whether alpha generated from the RNSK sorted long–short portfolio is still significant. In particular, we consider the following: (1) IDIOSKEW (IDIOVOL), long–short portfolio sorted by skewness (volatility) calculated on the residuals (regression of asset return on the traditional commodity baseline model); (2) QuantileSK, long–short portfolio sorted by the difference of return quantiles; (3) CV, long–short portfolio sorted by variance‐over‐mean of daily futures returns over prior 36 months; (4) LIQUID, long–short portfolio sorted by prior 2‐month dollar volume over an absolute return; (5) OI, long–short portfolio sorted by the change of entire open interest of commodity futures; and (6) Fama_French five factors, motivated by the equity market: Mkt.RF, SMB, HML, RMW, and CMA (Amihud et al, 1997; Bowley, 1920; Erb & Harvey, 2006; Fernandez‐Perez et al, 2018, 2016; Hong & Yogo, 2012; Locke & Venkatesh, 1997).…”

The economic importance of rare earth elements volatility forecasts

“…Moreover, Chernov (2007) shows that implied volatility inferred from at-the-money options is an inefficient and biased forecast of future realized volatility. Furthermore, Fernandez-Perez, Fuertes and Miffre (2016) show that idiosyncratic volatility seems to be negatively priced when using pricing models that do not account for backwardation and contango in commodity futures markets.…”