Mitigating Volatility Drag Using Machine Learning Models
(2025) NEKH02 20251Department of Economics
- Abstract (Swedish)
- 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... (More) - 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. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9212648
- author
- Andersson, Douglas LU and Grahm, Carl Johan
- supervisor
- organization
- course
- NEKH02 20251
- year
- 2025
- type
- M2 - Bachelor Degree
- subject
- keywords
- Machine learning, Random Forest, Volatility Drag, XGBoost, LETF, Leverage
- language
- English
- id
- 9212648
- date added to LUP
- 2025-12-08 08:36:02
- date last changed
- 2025-12-08 08:36:02
@misc{9212648,
abstract = {{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.}},
author = {{Andersson, Douglas and Grahm, Carl Johan}},
language = {{eng}},
note = {{Student Paper}},
title = {{Mitigating Volatility Drag Using Machine Learning Models}},
year = {{2025}},
}