---
title: Comparing Cryptocurrency and Stock Market Volatility Forecasts
url: https://www.ml-quant.com/papers/repec/hig-ecohse-2023-1-3/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:hig:ecohse:2023:1:3
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fej.hse.ru%2Fen%2F2023-27-1%2F819340420.html%3Bh%3Drepec%3Ahig%3Aecohse%3A2023%3A1%3A3
featured: 2023-09-21
citations: unknown
topic: Crypto & DeFi
---


# Comparing Cryptocurrency and Stock Market Volatility Forecasts

The article finds that HAR models are more accurate than GARÑH models in predicting the volatility of Bitcoin and E-mini S&P 500 futures.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fej.hse.ru%2Fen%2F2023-27-1%2F819340420.html%3Bh%3Drepec%3Ahig%3Aecohse%3A2023%3A1%3A3
- Identifier: RePEc:hig:ecohse:2023:1:3
- Released: 2023-09-21
- First featured: Quant Letter No. 16 (2023-09-21): https://www.ml-quant.com/issues/2023-09-21/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Crypto & DeFi

## Related

- [Volatility Estimators for Cryptocurrencies](https://www.ml-quant.com/papers/repec/gam-jstats-v-6-y-2023-i-4-p-82-1370-d-1298480/): The paper studies the realized volatility of cryptocurrencies, showing that the best predictors for Bitcoin and Ethereum come from 30-day implied volatility.
- [Bitcoin Volatility Forecasting with PSO–GRU Model](https://www.ml-quant.com/papers/repec/kap-compec-v-63-y-2024-i-5-d-10-1007-s10614-023-10392-5/): Scientists have created a deep learning model that enhances the accuracy of Bitcoin's volatility predictions, offering investors a dependable risk warning system and trading strategy.
- [Bitcoin and Ethereum GARCH Volatility Forecasting](https://www.ml-quant.com/papers/ssrn/4921210/): The study demonstrates that Bitcoin and Ethereum returns have similar statistical characteristics to other financial returns, with the two-component GJR model being the most accurate for predicting future volatility.
- [Forecasting Volatility in Crypto-Winter](https://www.ml-quant.com/papers/repec/spr-digfin-v-6-y-2024-i-4-d-10-1007-s42521-024-00108-1/): The research expands the use of a volatility prediction framework using LSTM and rough volatility, demonstrating its superiority over traditional models in predicting cryptocurrency volatility.
- [Improved Cryptocurrency Volatility Predictions](https://www.ml-quant.com/papers/repec/eee-ecmode-v-144-y-2025-i-c-s0264999324003432/): The study reveals that combining different forecasting models can greatly enhance the accuracy of predicting cryptocurrency volatility. This can provide crucial information for investors looking to improve risk management strategies in cryptocurrency markets.
- [Pathwise Roughness of Bitcoin Realized Volatility: Stability Across Time, Sampling, and Volatility Measures](https://www.ml-quant.com/papers/arxiv/2507.00575/): Rough volatility models are found to be misaligned with Bitcoin volatility due to a multifractal structure that contradicts the homogeneity assumptions of rough volatility estimation.