Mitigating Volatility Drag Using Machine Learning Models - Andersson,Douglas; Grahm,Carl Johan (2025)
We observe that, while predicting returns remain difficult and uncertain, volatility exhibit more persistence and predictability due to well-known patterns such as volatility clustering (Mandelbrot, 1967). We test whether short-horizon volatility forecasts can be used to time leverage in a passive index strategy so as to mitigate the growth penalty from volatility drag in leveraged exchange-traded funds (LETFs). Using OMXS30GI daily data (2012-2025), we train two ensemble models, Random Forest (RF) and Extreme Gradient Boosting (XGB), to forecast next-day volatility and map those forecasts to discrete leverage choices {1x, 1.5x, 2x } via a log-growth break-even rule. We compare against static 1.5x and 2x benchmarks as well as an unlevered index proxy. Out of sample (2022-2025), the models allocate to 2x 77 - 85% of days, de-risking after drawdowns relative to static 2x exposure. The mechanism is consistent with theory: when signals arrive with latency, the quadratic ( k222 ) penalty dominates. We conclude that price-only volatility forecasts are insufficient for effective leverage timing on Swedish data; future work should incorporate anticipatory volatility measures, market-trend/direction and asymmetric switching rules.
Keywords: Machine learning; Random Forest; Volatility Drag; XGBoost; LETF; Leverage