Probabilistic Forecasting Cryptocurrencies Volatility: From Point to Quantile Forecasts

TL;DR

This paper develops and evaluates probabilistic methods for forecasting cryptocurrency volatility, combining multiple models to better capture uncertainty and improve risk management in highly volatile markets.

Contribution

It introduces a systematic approach to probabilistic volatility forecasting in cryptocurrencies using ensemble methods and evaluates their effectiveness with empirical data.

Findings

QRS method outperforms alternatives on Bitcoin data

Linear models on log-transformed data are most effective

Probabilistic stacking enhances uncertainty quantification

Abstract

Cryptocurrency markets are characterized by extreme volatility, making accurate forecasts essential for effective risk management and informed trading strategies. Traditional deterministic (point) forecasting methods are inadequate for capturing the full spectrum of potential volatility outcomes, underscoring the importance of probabilistic approaches. To address this limitation, this paper introduces probabilistic forecasting methods that leverage point forecasts from a wide range of base models, including statistical (HAR, GARCH, ARFIMA) and machine learning (e.g. LASSO, SVR, MLP, Random Forest, LSTM) algorithms, to estimate conditional quantiles of cryptocurrency realized variance. To the best of our knowledge, this is the first study in the literature to propose and systematically evaluate probabilistic forecasts of variance in cryptocurrency markets based on predictions derived from multiple base models. Our empirical results for Bitcoin demonstrate that the Quantile Estimation through Residual Simulation (QRS) method, particularly when applied to linear base models operating on log-transformed realized volatility data, consistently outperforms more sophisticated alternatives. Additionally, we highlight the robustness of the probabilistic stacking framework, providing comprehensive insights into uncertainty and risk inherent in cryptocurrency volatility forecasting. This research fills a significant gap in the literature, contributing practical probabilistic forecasting methodologies tailored specifically to cryptocurrency markets.