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VaR Prediction for Cryptocurrencies Using GRF by Thi Minh Thi Nguyen is a document available to read on EtoBox.

This paper investigates the prediction of Value at Risk (VaR) for cryptocurrencies using Generalized Random Forests (GRF), demonstrating its superior performance compared to traditional methods during volatile market conditions. The study analyzes 105 major cryptocurrencies and identifies key predictors affecting VaR forecasts, particularly in unstable periods. A comprehensive simulation study confirms that GRF is effective for predicting VaR in both cryptocurrencies and standard financial returns.

Author

Thi Minh Thi Nguyen

Language

EN