Adaptive Supervised Learning for Volatility Targeting Models (Ecml Pkdd Midas 2021 Presentation Slides)

17 Pages Posted: 20 Sep 2021

See all articles by Eric Benhamou

Eric Benhamou

Université Paris Dauphine; EB AI Advisory; AI For Alpha

David Saltiel

Université Paris Dauphine; A.I. Square Connect; AI For Alpha

Serge Tabachnik

Lombard Odier Investment Managers

Corentin Bourdeix

Ecole Polytechnique Fédérale de Lausanne

François Chareyron

Lombard Odier Investment Managers

Beatrice Guez

AI For Alpha

Date Written: September 18, 2021

Abstract

In the context of risk-based portfolio construction and pro-active risk management, finding robust predictors of future realised volatility is paramount to achieving optimal performance. Volatility has been documented in economics literature to exhibit pronounced persistence with clusters of high or low volatility regimes and to mean-revert to a normal level, underpinning Nobel prize-winning work on Generalized Autoregressive Heteroskedastic (GARCH) models. From a Reinforcement Learning (RL) point of view, this process can be interpreted as a model-based RL approach where the goal of the models is twofold: first, to represent the volatility dynamics and forecast its term structure and second, to compute a resulting allocation to match a given target volatility: hence the name "volatility targeting method for risk-based portfolios". However, the resulting volatility model-based RL approaches are hard to distinguish as each model results in similar performance without a clear dominant one. We therefore present an innovative approach with an additional supervised learning step to predict the best model(s), based on historical performance ordering of RL models. Our contribution shows that adding a supervised learning overlay to decide which model(s) to use provides improvement over a naive benchmark consisting in averaging all RL models. A salient ingredient in this supervised learning task is to adaptively select features based on their significance, thanks to minimum importance filtering. This work extends our previous work on combining model-free and model-based RL. It mixes different types of learning procedures, namely model-based RL and supervised learning opening new doors to combine different machine learning approaches.

Keywords: Volatility targeting, Supervised learning, Best ordering, Model-based and Portfolio allocation, Walk-forward and Features selection.

JEL Classification: G12,G13

Suggested Citation

Benhamou, Eric and Saltiel, David and Tabachnik, Serge and Bourdeix, Corentin and Chareyron, François and Guez, Beatrice, Adaptive Supervised Learning for Volatility Targeting Models (Ecml Pkdd Midas 2021 Presentation Slides) (September 18, 2021). Available at SSRN: https://ssrn.com/abstract=3926218 or http://dx.doi.org/10.2139/ssrn.3926218

Eric Benhamou (Contact Author)

Université Paris Dauphine ( email )

Place du Maréchal de Tassigny
Paris, Cedex 16 75775
France

EB AI Advisory ( email )

35 Boulevard d'Inkermann
Neuilly sur Seine, 92200
France

AI For Alpha ( email )

35 boulevard d'Inkermann
Neuilly sur Seine, 92200
France

David Saltiel

Université Paris Dauphine ( email )

Place du Maréchal de Tassigny
Paris, Cedex 16 75775
France

A.I. Square Connect ( email )

35 Boulevard d'Inkermann
Neuilly sur Seine, 92200
France

AI For Alpha ( email )

35 boulevard d'Inkermann
Neuilly sur Seine, 92200
France

Serge Tabachnik

Lombard Odier Investment Managers ( email )

6, avenue des Morgines
Petit-Lancy, 1213
Switzerland

Corentin Bourdeix

Ecole Polytechnique Fédérale de Lausanne ( email )

Quartier UNIL-Dorigny, Bâtiment Extranef, # 211
40, Bd du Pont-d'Arve
CH-1015 Lausanne, CH-6900
Switzerland

François Chareyron

Lombard Odier Investment Managers ( email )

Avenue des Morgines 6
Geneva, 1213
Switzerland

Beatrice Guez

AI For Alpha ( email )

35 boulevard d'Inkermann
Neuilly sur Seine, 92200
France

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