Pricing Techniques for Crypto Asset Markets

This thesis explores how traditional asset pricing theory grounded in models such as the Capital Asset Pricing Model (CAPM), provides a foundation for understanding how assets are valued under equilibrium conditions that assume rational expectations and efficient markets. However, the emergence of decentralized markets, particularly in the context of crypto-assets, raises critical challenges to the applicability of these frameworks. Unlike traditional financial markets, crypto markets lack a clear fundamental value, exhibit fragmented information channels, limited institutional oversight, and are dominated by retail investors, which results in price formation processes that deviate significantly from classical theory. Crypto-assets are traded on platforms without centralized regulation or standard governance. Their prices are influenced not only by technological adoption or project fundamentals but also by asset-specific mechanisms, investor sentiment, and the amplification effects of digital media. These conditions generate high short-term volatility and hinder convergence toward long-term value, as arbitrage mechanisms are weakened by market fragmentation, heterogeneous transaction costs, and liquidity asymmetries. The central research question guiding this thesis is how prices are formed in crypto-asset markets in the absence of fundamental value. This question invites a reassessment of asset pricing principles under decentralized and speculative conditions. The study is structured around three key inquiries: the role of volatility and market risk premium in the absence of traditional valuation anchors; the impact of heterogeneous investor behavior and asymmetric information on speculative dynamics and price distortion; and the development of a pricing model that reflects the structural features of crypto markets, shaped by noise, sentiment, and fragmentation. Throughout the thesis, a comparative perspective is maintained to contrast traditional financial markets with crypto-asset markets. Traditional markets are characterized by institutional investors and regulatory oversight, providing a stable structure for risk pricing. In contrast, crypto markets operate without such anchors, and prices are often guided by behavioral reference points such as recent price trends or the relative performance of prominent tokens. These informal anchors enable market participants to form expectations and transact, despite the absence of intrinsic valuation measures. The resulting prices reflect speculative interactions among diverse investors navigating uncertainty, consistent with behavioral theories where prices emerge from the interplay between informed and heuristic-driven agents in inefficient informational settings. The contribution of this thesis lies in presenting a coherent alternative explanation of price formation in crypto markets, accounting for investor behavior and the decentralized structure of these ecosystems. The first chapter focuses on the lack of fundamental value in crypto markets and argues, through Prospect Theory, that investors use recent price levels as behavioral anchors, amplifying speculative dynamics and noise. The second chapter examines herding behavior and social learning mechanisms, employing statistical tools to assess the influence of collective behavior on short-term price movements. The final chapter introduces an asset pricing model based on the Heston stochastic volatility framework, modified to incorporate behavioral factors and market segmentation. This integrated approach addresses the limitations of traditional asset pricing models in the context of decentralized and speculative environments.

• Autores: Martínez Patiño, Manuel Andrés
• Tipo de recurso: Doctoral thesis
• Fecha de publicación: 2025
• Institución: Universidad de los Andes
• Repositorio: Séneca: repositorio Uniandes
• Idioma: eng
• OAI Identifier: oai:repositorio.uniandes.edu.co:1992/76378
• Acceso en línea: https://hdl.handle.net/1992/76378
• Palabra clave: Crypto-Asstes; Asset Pricing; Bayesian Satistics; Administración
• Rights: openAccess
• License: Attribution-NonCommercial-NoDerivatives 4.0 International