Abstract

In this article, the authors intend to gain an understanding of the drivers of stock convexity, also known as gamma. First, using a bottom-up—firm-level—approach, they show that stock fundamentals, particularly metrics related to value (captured by the price-to-book ratio) and historical volatility, allow us to efficiently discriminate between convex and concave stocks. Building on this result, they investigate the ties between the gamma premium and traditional risk factors. Second, they adopt a top-down—macroeconomic-driven—framework to understand which economic environment is the most favorable to convexity: They highlight the importance of the short-term interest rate, the VIX, but also oil price dynamics in a univariate cointegrating vector. These variables share long-term relationships. The authors then evaluate the ability of different models to forecast future convexity premium dynamics. Finally, they seek to employ these signals in the design of a systematic long convexity strategy and show that it leads to significantly improved risk-adjusted returns compared with a capitalization-weighted benchmark, especially in turbulent markets. Convexity exposure appears particularly relevant in a context of monetary policy normalization.

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