Understanding and Predicting Bitcoin Crashes with Probit Models

Abstract

Predicting cryptocurrency crashes is challenging due to their speculative dynamics, extreme volatility,and limited regulatory structure. This study investigates the predictability of Bitcoin crashesusing standard and dynamic probit models applied to daily data from 2017 to 2023. Crash indicatorsare constructed using a 3.09𝜎 tail event rule, and model performance is evaluated through anextensive grid search over multiple crash horizons and consolidation windows, ensuring robustnessagainst horizon dependent distortions. The empirical results show that hybrid models that combinesentiment, macro-financial variables, and Bitcoin specific returns consistently outperformsentimentonly specifications.The findings highlight the importance of combining behavioral indicators with global riskmeasures and cryptocurrency specific dynamics to capture the multifaceted drivers of Bitcoincrashes. The results have implications for investors, exchanges, and policymakers looking for earlywarning mechanisms for systemic risk in digital asset markets.