Essays in cryptofinance

Abstract

This dissertation presents an empirical analysis of decentralized finance through three distinct studies. By harnessing the power of on-chain data, this research delves into the mechanics of DeFi, exploring how we assess financial risk and measure market efficiency. Furthermore, it directly addresses the significant economic exter nalities of the sector by measuring the annualized energy draw of Bitcoin’s global mining industry. The first article, Risk forecasting comparisons in decentralized fi nance: An approach in constant product market makers (this research was presented at the Annual Conference of the Banco Central do Brasil (2024) and subsequently published in the Journal of Economics and Business, Volume 133, 2025; 2024 JCR Impact Factor: 3.4), pioneers by comparing risk measures between centralized and decentralized exchanges. By employing a vast dataset from Uniswap V2 Liquid ity Pool (LP) and conducting a meticulous comparative analysis of Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts, using both parametric Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and the non para metric DeepAR neural network, it demonstrates that liquidity provision generally presents a statistically significant lower risk profile than an equivalent buy and hold strategy. A critical exception exists for stablecoin pairs, where protocol fees become the primary risk driver. This research provides a crucial empirical foundation for developing sophisticated, model informed risk management tools in Decentralized Finance (DeFi). The second article, Pricing efficiency in cryptocurrencies: the case of centralized and decentralized markets (published in the Journal of Economics and Business, Volume 133, 2025; 2024 JCR Impact Factor: 3.4), offers a comparative analysis of market efficiency between liquidity pool mechanisms and traditional order book systems. Utilizing Asymmetric Multifractal Detrended Fluctuation Analysis (asym metric MF-DFA) and the Thermal Optimal Path (TOP) method on data from Binance and Uniswap V2, it reveals that algorithmic LP can achieve superior weak form market efficiency compared to Centralized Exchanges (CEX) order books. The study conclusively identifies the Decentralized Exchanges (DEX) as the lead market in price discovery, transmitting signals to its centralized counterpart with an average lag of under 24 hours. This efficiency premium, driven by radical transparency and high velocity arbitrage, intensified significantly following the Ethereum 2.0 upgrade. The third article, Bitcoins halving events and the fractal nature of mining energy consumption, investigates the long term impact of Bitcoins programmed monetary policy on its mining energy consumption behavior. Applying asymmetric MF-DFA to data from the Cambridge Centre for Alternative Finance, it uncovers the com plex, multifractal nature of Bitcoins energy dynamics, showing an evolution from persistent, heterogeneous behavior before halving events toward more efficient and random consumption characteristics after each subsequent halving. The analysis provides the first documented evidence of a significant cross-chain effect, showing that Ethereum’s transition to Proof of Stake (PoS) consensus triggered an immediate and sustained decrease in the persistence of Bitcoin’s energy consumption patterns. This finding reveals previously unrecognized interconnectivity between seemingly independent networks and creates new pathways for assessing the environmental relationships within blockchain ecosystems.

Esta dissertação apresenta uma análise empírica de finanças descentralizadas por meio de três estudos distintos. Utilizando dados on chain, a pesquisa examina avaliação de risco financeiro, eficiência de mercado e consumo energético anualizado da mineração de Bitcoin. O primeiro artigo, Risk forecasting comparisons in de centralized finance an approach in constant product market makers (Computational Economics, 2025), desenvolve uma metodologia comparativa para medidas de risco entre CEX e DEX. Empregou-se dados de LP da DEX Uniswap V2, conduzindo uma análise comparativa de previsões de Value at Risk e Expected Shortfall. Para isso, fez-se uso tanto de modelos paramétricos GARCH quanto do modelo de rede neural não paramétrica DeepAR, os resultados demonstraram que a provisão de liquidez geralmente apresenta perfil de risco estatisticamente inferior à estratégia tradicional de buy and hold. Uma exceção crítica ocorre em pares de stablecoins, onde as taxas do protocolo tornam-se o principal fator de risco. Esta pesquisa fornece base em pírica crucial para desenvolver ferramentas sofisticadas de gestão de risco informadas por modelos em DeFi. O segundo artigo, Pricing efficiency in cryptocurrencies the case of centralized and decentralized markets (Journal of Economics and Business, 2025), oferece uma análise comparativa da eficiência de mercado entre os mecanismos de liquidity pools e o tradicional sistema de order books. Utilizando Asymmetric Multifractal Detrended Fluctuation Analysis e o método Thermal Optimal Path em dados da corretora Binance e do Uniswap V2, revelamos que os algorítmos contidos nos smart contracts, que regem as liquidity pools, são mais eficientes na weak form de eficiência de mercado quando comparados aos order books centralizados. O estudo também identifica o mecanismo decentralizado como líder na descoberta de preços, transmitindo sinais para sua contraparte centralizada com defasagem média inferior a 24 horas. Este prêmio de eficiência, impulsionado pela radical transparência e arbitragem, mostrou se intensificar após a atualização 2.0 do Ethereum. Oterceiro artigo, Bitcoins halving events and the fractal nature of mining energy consumption, investiga o impacto de longo prazo da política monetária programada do Bitcoin em seu comportamento de consumo energético de mineração. Aplicando Asymmetric Multifractal Detrended Fluctuation Analysis à dados do Cambridge Centre for Alternative Finance, desvenda-se a natureza multifractal complexa da dinâmica energética do Bitcoin. O estudo revela uma evolução nos padrões de con sumo energético, transicionando de um comportamento heterogêneo no período pré halving para características de consumo mais eficientes e estocasticamente aleatórias após cada evento de halving subsequente. A análise apresenta a primeira evidência documentada de um efeito cross-chain significativo, demonstrando que a transição do Ethereum para o Proof of Stake provocou uma redução imediata e persistente na regularidade dos padrões de consumo energético da mineração de Bitcoin. Essa constatação revela uma interconexão até então não reconhecida entre blockchains aparentemente independentes.