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In this note, we address the problem of probabilistic forecasting using anadaptive volatility method based on classical time-varying volatility modelsand stochastic optimization algorithms. These principles were successfullyapplied in the recent M6 financial forecasting competition for bothprobabilistic forecasting and investment decision-making under the team namedAdaGaussMC. The key points of our strategy are: (a) apply a univariatetime-varying volatility model, called AdaVol, (b) obtain probabilisticforecasts of future returns, and (c) optimize the competition metrics usingstochastic gradient-based algorithms. We claim that the frugality of themethods implies its robustness and consistency.

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