CAPOPTIX: An Options-Framework for Capacity Market Pricing

Battle Count: 63

While primarily focused on electricity capacity market design and policy, the paper has moderate relevance to quantitative trading in energy markets. The options pricing framework, regime-switching models, CVaR-based risk metrics, and Monte Carlo simulation approaches are directly applicable to energy derivatives trading (power forwards, futures on EEX/CME). The Markov Regime Switching OU model and GARCH with jumps are standard tools in energy price modeling. However, the paper does not address trading strategies, execution, or portfolio construction directly. The reliability option pricing methodology could inform hedging strategies for LSEs and generators participating in capacity markets.

Implementation requires: (1) fitting multiple stochastic models (OU, GARCH, jump-diffusion, Markov regime-switching AR) to historical price data; (2) K-Means clustering for regime identification; (3) Hamilton iterative filtering for latent regime inference; (4) Monte Carlo simulation with regime-switching paths; (5) CVaR computation and quantile-based strike price selection; (6) break-even analysis for contract duration determination; (7) sensitivity analysis across multiple parameters. The lack of closed-form solutions for regime-switching models necessitates extensive simulation. Cross-market calibration adds complexity. However, the modular architecture and clear algorithmic descriptions aid implementation.

The paper uses publicly available data from NYISO, CAISO, ERCOT, SMARD (Germany), and GMI (Italy). Detailed algorithms (Algorithm 1 for Monte Carlo) and parameter tables are provided. However, no GitHub repository or code is mentioned. The methodology involves multiple stochastic models with specific calibration procedures (Hamilton filter, K-Means, BIC selection), which are described but would require significant implementation effort. Assumptions are clearly stated (price-taking behavior, constant O&M, scarcity-pricing map).