---
title: Liquidity Provision in Crypto Markets
url: https://www.ml-quant.com/papers/ssrn/4721609/
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: SSRN 4721609
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4721609
featured: 2024-02-14
citations: unknown
topic: Crypto & DeFi
---


# Liquidity Provision in Crypto Markets

A study reveals that the liquidity provision premium in cryptocurrency markets can be predicted using factors like the VIX index and Tether liquidity, and is influenced by stock market premiums globally.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4721609
- Identifier: SSRN 4721609
- Released: 2022-03-21
- First featured: Quant Letter No. 37 (2024-02-14): https://www.ml-quant.com/issues/2024-02-14/
- 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.
- [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.
- [Emoji driven crypto assets market reactions](https://www.ml-quant.com/papers/ssrn/4722627/): Research using GPT4 and a BERT model shows that Twitter emoji sentiment can predict cryptocurrency market trends and help avoid major downturns.
- [Stylized Facts of High-Frequency Bitcoin Time Series](https://www.ml-quant.com/papers/arxiv/2402.11930/): An analysis of the Bitcoin market index from 2019 to 2022 reveals two periods of volatility, suggesting greater market efficiency at shorter time scales.
- [Modelling crypto markets by multi-agent reinforcement learning](https://www.ml-quant.com/papers/arxiv/2402.10803/): A multi-agent reinforcement learning model simulates crypto markets using Binance's daily closing prices of 153 cryptocurrencies from 2018 to 2022, accurately emulating crypto market microstructure.
- [A Data Engineering Framework for Ethereum Beacon Chain Rewards: From Data Collection to Decentralization Metrics](https://www.ml-quant.com/papers/arxiv/2402.11170/): A study of consensus reward data from the Ethereum Beacon chain offers insights into reward distribution and evolution, aiding in the assessment and refinement of blockchain systems' decentralization, security, and efficiency.
