An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition
Authors: Joseph de Vilmarest, Nicklas Werge
Abstract: In this note, we address the problem of probabilistic forecasting using an adaptive volatility method based on classical time-varying volatility models and stochastic optimization algorithms. These principles were successfully applied in the recent M6 financial forecasting competition for both probabilistic forecasting and investment decision-making under the team named AdaGaussMC. The key points of our strategy are: (a) apply a univariate time-varying volatility model, called AdaVol, (b) obtain probabilistic forecasts of future returns, and (c) optimize the competition metrics using stochastic gradient-based algorithms. We claim that the frugality of the methods implies its robustness and consistency.
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