---
title: Bitcoin Volatility Estimation Model
url: https://www.ml-quant.com/papers/repec/hig-ecohse-2022-4-6/
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:2022:4:6
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fej.hse.ru%2Fen%2F2022-26-4%2F800825434.html%3Bh%3Drepec%3Ahig%3Aecohse%3A2022%3A4%3A6
featured: 2023-10-12
citations: unknown
topic: Crypto & DeFi
---


# Bitcoin Volatility Estimation Model

The study suggests two semi-nonparametric GARCH models for more precise estimation of Bitcoin volatility dynamics, showing their superiority over traditional GARCH models.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fej.hse.ru%2Fen%2F2022-26-4%2F800825434.html%3Bh%3Drepec%3Ahig%3Aecohse%3A2022%3A4%3A6
- Identifier: RePEc:hig:ecohse:2022:4:6
- Released: 2022-03-09
- First featured: Quant Letter No. 20 (2023-10-12): https://www.ml-quant.com/issues/2023-10-12/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Crypto & DeFi

## Related

- [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.
- [Predicting Cryptocurrency Volatility](https://www.ml-quant.com/papers/repec/eee-finlet-v-67-y-2024-i-pa-s1544612324007876/): 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.
- [Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero](https://www.ml-quant.com/papers/arxiv/2511.22782/): The study examines the price factors influencing five major cryptocurrencies from 2010-2018, highlighting the roles of market conditions, long-term appeal, and the SP500 index.
- [Economics unchained: Investigating the role of cryptocurrency, blockchain and intricacies of Bitcoin price fluctuations](https://www.ml-quant.com/papers/arxiv/2310.09745/): The research conducts an economic analysis of Bitcoin, examining its impact, transaction volume, market capitalization, mining activities, and effects on traditional finance.
- [Legal NLP Meets MiCAR: Advancing the Analysis of Crypto White Papers](https://www.ml-quant.com/papers/arxiv/2310.10333/): The paper explores the use of Natural Language Processing in analyzing crypto-asset white papers for regulatory compliance under the EU's crypto-asset regulation, discussing the potential and challenges of this integration.
- [Assessing the solvency of virtual asset service providers: are current standards sufficient?](https://www.ml-quant.com/papers/arxiv/2309.16408/): Insights and Strategies: The article suggests a method to evaluate the financial stability of virtual asset service providers using data from crypto wallets, balance sheets, and regulatory bodies, emphasizing the importance of regular audits for crypto assets.