An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition

In this paper, we address the problem of probabilistic forecasting using an adaptive volatility method rooted in classical time-varying volatility models and leveraging online stochastic optimization algorithms. These principles were successfully applied in the M6 forecasting competition under the team named AdaGaussMC. Our approach takes a unique path by embracing the Efficient Market Hypothesis (EMH) instead of trying to beat the market directly. We focus on evaluating the efficient market and emphasize the importance of online forecasting in adapting to the dynamic nature of financial markets. The three key points of our approach are: (a) apply the univariate time-varying volatility model AdaVol, (b) obtain probabilistic forecasts of future returns, and (c) optimize the competition metrics using stochastic gradient-based algorithms. We contend that the simplicity of our approach contributes to its robustness and consistency. Our performance in the M6 competition resulted in an overall 7^th place, with a 5^th place in the forecasting task. Considering our approach’s perceived simplicity, this achievement underscores the efficacy of our adaptive volatility method in probabilistic forecasting.