Price Discovery in Cryptocurrency Markets

Paper: arXiv 2506.08718

Authors: Juan Plazuelo Pascual, Carlos Tardon Rubio, Juan Toro Cebada, Angel Hernando Veciana

Abstract

This document analyzes price discovery in cryptocurrency markets by comparing centralized and decentralized exchanges, as well as spot and futures markets. The study focuses first on Ethereum (ETH) and then applies a similar approach to Bitcoin (BTC). Chapter 1 outlines the theoretical framework, emphasizing the structural differences between centralized exchanges and decentralized finance mechanisms, especially Automated Market Makers (AMMs). It also explains how to construct an order book from a liquidity pool in a decentralized setting for comparison with centralized exchanges. Chapter 2 describes the methodological tools used: Hasbrouck’s Information Share, Gonzalo and Granger’s Permanent-Transitory decomposition, and the Hayashi-Yoshida estimator. These are applied to explore lead-lag dynamics, cointegration, and price discovery across market types. Chapter 3 presents the empirical analysis. For ETH, it compares price dynamics on Binance and Uniswap v2 over a one-year period, focusing on five key events in 2024. For BTC, it analyzes the relationship between spot and futures prices on the CME. The study estimates lead-lag effects and cointegration in both cases. Results show that centralized markets typically lead in ETH price discovery. In futures markets, while they tend to lead overall, high-volatility periods produce mixed outcomes. The findings have key implications for traders and institutions regarding liquidity, arbitrage, and market efficiency. Various metrics are used to benchmark the performance of modified AMMs and to understand the interaction between decentralized and centralized structures.

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced econometric techniques like Hasbrouck Information Share, Gonzalo-Granger decomposition, and Hayashi-Yoshida estimators, indicating high mathematical complexity, while the empirical analysis uses real cryptocurrency data (ETH/BTC) across multiple exchanges and time periods with specific event-driven backtests, demonstrating solid empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal: Price Discovery in Crypto Markets"] --> B["Data Collection<br>ETH: Binance & Uniswap v2<br>BTC: Spot & Futures"]
  B --> C["Methodology: Lead-Lag & Cointegration<br>Hasbrouck's IS, GG-PT, Hayashi-Yoshida"]
  C --> D["Computational Process<br>1. Construct Order Books<br>2. Estimate Lead-Lag Effects<br>3. Analyze Cointegration"]
  D --> E["Key Findings & Outcomes"]
  E --> F["Centralized Markets Lead ETH Price Discovery"]
  E --> G["Futures Lead in Normal Volatility<br>Mixed Outcomes in High Volatility"]
  E --> H["Implications for<br>Liquidity, Arbitrage, & Market Efficiency"]

Cited References (36)

1. Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models (2002)
2. An Overview of Market Manipulation (2019)
3. Spoofing and Layering (2020)
4. Modelling and measuring price discovery in commodity markets (2010)
5. One Security, Many Markets: Determining the Contributions to Price Discovery (1995)
6. High Frequency Lead/Lag Relationships—Empirical Facts (2014)
7. Likelihood-Based Inference in Cointegrated Vector Autoregressive Models (1996)
8. Lecture Notes (2024)

0 of 36 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.