Probabilistic Forecasting of Cryptocurrency Volatility with Quantile Models
Summary
The study develops probabilistic forecasts of Bitcoin realized variance, extending beyond point forecasts to estimate conditional quantiles and represent uncertainty in possible volatility outcomes. It combines forecasts from statistical models such as HAR, GARCH, and ARFIMA with forecasts from machine learning methods, then applies a probabilistic stacking framework to the base-model predictions.
The reported comparison finds that Quantile Estimation through Residual Simulation performs consistently well, particularly when paired with linear base models trained on log-transformed realized volatility. The document presents this as evidence that simpler linear forecasting inputs can support effective probabilistic estimates, even against more complex alternatives. The analysis is specific to Bitcoin, and the supplied description gives no sample period, evaluation metrics, or detailed account of model settings. Its findings therefore motivate risk-aware volatility forecasting but do not establish that the same ranking will hold for other cryptocurrencies or market conditions.
Key ideas
- Probabilistic forecasts estimate a range of conditional realized variance outcomes rather than only a single point.
- The framework combines predictions from statistical and machine learning base models.
- Residual simulation is reported to perform consistently well, especially with linear models using log volatility data.
- The empirical findings focus on Bitcoin and do not establish performance across other cryptocurrencies.
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Full text
# Probabilistic Forecasting Cryptocurrencies Volatility: From Point to Quantile Forecasts # Probabilistic Forecasting Cryptocurrencies Volatility: From Point to Quantile Forecasts 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.
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