---
title: Predicting Cryptocurrency Volatility
url: https://www.ml-quant.com/papers/repec/eee-finlet-v-67-y-2024-i-pa-s1544612324007876/
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:eee:finlet:v:67:y:2024:i:pa:s1544612324007876
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1544612324007876%3Bh%3Drepec%3Aeee%3Afinlet%3Av%3A67%3Ay%3A2024%3Ai%3Apa%3As1544612324007876
featured: 2024-09-18
citations: unknown
topic: Crypto & DeFi
---


# Predicting Cryptocurrency Volatility

The SHARV-MGJR model, which includes volatility leverage effects and current return data, is suggested for better prediction of cryptocurrency market volatility, surpassing GARCH-type models in tests.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1544612324007876%3Bh%3Drepec%3Aeee%3Afinlet%3Av%3A67%3Ay%3A2024%3Ai%3Apa%3As1544612324007876
- Identifier: RePEc:eee:finlet:v:67:y:2024:i:pa:s1544612324007876
- Released: 2024-09-18
- First featured: Quant Letter No. 66 (2024-09-18): https://www.ml-quant.com/issues/2024-09-18/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Crypto & DeFi

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