Essays on the estimation of jump, volatility, and liquidity for cryptocurrency
Creators: name: Miao, Hang
Publisher: name: No Publisher Supplied
Year: 2024

description: Decentralized finance (DeFi) has emerged as a cornerstone of the financial ecosystem, offering transparent, non-custodial alternatives to traditional finance through decentralized applications and smart contracts. Despite its rapid growth and innovation, critical areas—such as cryptocurrency price jump detection, accurate asset volatility measurement, and liquidity estimation—remain underdeveloped. This thesis introduces novel approaches to address these challenges, recognizing that traditional methods used in centralized finance cannot be directly applied to DeFi due to the inherent differences in their structure and operation. Accurate asset volatility measurement is crucial for derivatives valuation, market making, risk management, and portfolio allocation. However, existing methods, designed for traditional finance, are often inadequate for decentralized finance and cryptocurrency assets. Crypto markets operate continuously, with liquidity managed by automated strategies, and frequently experience unpredictable price jumps. These challenges are further exacerbated by manipulation risks specific to blockchain environments, such as Miner Extractable Value (MEV) attacks and flash loan exploits. Furthermore, the absence of direct fiat USD markets for many cryptocurrencies complicates collateral evaluation for lending protocols, necessitating innovative approaches to volatility estimation. In this dissertation, we propose a novel method for price jump detection that leverages a machine learning clustering algorithm in conjunction with wavelet transforms. Building on this framework, we enhance a Fourier-based estimator to achieve more accurate measurement of price (co-)volatility, which effectively filtering out microstructure noise and handling asynchronously observed data. Furthermore, we introduce a new approach for measuring liquidity in decentralized exchanges (DEXs), focusing on the distinctive mechanisms of automated market makers (AMMs). we derived the closed-form or semi-closed-form solutions for market depth across four major AMM decentralized exchang—Uniswap V1/V2, Balancer, Curve, and Uniswap V3—and incorporate critical factors such as order flow imbalance, liquidity provider (LP) duration, and LP diversification. By addressing these complexities, the proposed model bridges the gap between traditional liquidity metrics and the unique dynamics of DEXs. Empirical validation of the proposed methods is conducted by analyzing the (co-)volatility of the USDC stablecoin during its depegging event, following the collapse of Silicon Valley Bank and Signature Bank. By examining both on-chain data trading in DEX and off-chain data trading from centralized exchanges (CEX), it is found that only a limited number of centralized exchanges with fiat on-ramp capabilities offer a direct USDC/USD trading pair, which accounts for a small fraction of USDC-related trading volume. As a result, most market participants convert USDC to USD via intermediary cryptocurrencies like ETH, BTC, and USDT. The proposed wavelet-based jump detection method, combined with volume-weighted average price (VWAP), reconstructs USDC’s market price through cross-triangulation. Applying the Fourier-based co-volatility estimation to these triangulated pairs yields more accurate results than direct methods, with findings suggesting that co-volatility estimates may serve as early indicators of depeg events. Additionally, I used on-chain DEX data to demonstrate the practicality of the proposed liquidity measurement approach. In conclusion, improved jump detection and (co-)volatility estimation, combined with a robust liquidity measurement framework, provide a comprehensive solution to enhance market efficiency by improving pricing mechanisms, supporting more accurate collateral assessments for lending protocols, and enabling the development of innovative financial products. These advancements also promote transparency and trust within the DeFi ecosystem, attracting more participants and fostering greater liquidity, ultimately contributing to a more stable and resilient DeFi ecosystem.