Pricing of wrapped Bitcoin and Ethereum on-chain options

Battle Count: 77

The paper is primarily diagnostic and protocol-design oriented rather than proposing a trading strategy. However, it provides actionable insights for quantitative traders: (1) IV vs. GARCH volatility comparison identifies potential delta-neutral volatility trading opportunities, (2) understanding of how order size, moneyness, maturity, and volatility affect on-chain option mispricing informs execution strategies, (3) the FGLS framework could be adapted for real-time mispricing detection. The practical trading relevance is moderate because the paper explicitly states its goal is diagnosis and protocol tuning rather than proposing a trading strategy, and DeFi execution frictions (fees, slippage, depth, oracle latency) constrain realized profits.

The methodology involves multiple complex components: (1) two-regime MS-AR estimation via maximum likelihood with L-BFGS optimization, (2) regime-conditional GJR-GARCH with skewed-t innovations and BIC-based model selection, (3) Black-Scholes pricing with regime-switching volatility, (4) Brent root-finding for IV inversion, (5) two-step FGLS with heteroskedasticity modeling and HAC standard errors. Additionally, on-chain data extraction from Arbitrum for Hegic transactions requires blockchain-specific tooling. The combination of econometric modeling, options pricing, and DeFi data infrastructure makes implementation moderately to highly complex.

The paper provides detailed methodology with explicit equations for MS-AR, GJR-GARCH, BS pricing, and FGLS. Data sources are identified (CryptoCompare API for BTC/ETH prices, on-chain Hegic data on Arbitrum). However, no code repository is provided, and the specific Hegic transaction data extraction process is not fully detailed. The sample period (Oct 24, 2022 - May 21, 2024) and parameter selection criteria (BIC over p,q in {1,...,10}, o in {0,...,5}) are specified. Reproducibility is moderate given the specialized on-chain data requirements.