In this paper, we intend to gain an understanding of the drivers of stock convexity, also known as gamma. First, using a bottom-up – firm-level – approach, we showcase that stock fundamentals, in particular 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, we investigate the ties between the gamma premium and traditional risk factors. Second, we adopt a top-down – macroeconomic driven – framework, to understand which economic environment is the most favorable to convexity: we 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. We then evaluate the ability of different models to forecast future convexity premium dynamics. Finally, we 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 to a capitalization-weighted benchmark, especially in turbulent markets. Convexity exposure appears particularly relevant in a context of monetary policy normalization.
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