Options Analysis Suite Blog
Long-form research on options pricing theory, model selection, volatility modeling, and market-regime case studies. Each post is an applied piece, the kind of analysis that shapes how an analytics platform actually treats a problem, not introductory primer copy. Posts pair with the canonical documentation (definitions, methodology) and the live platform surfaces that the research informed.
What You Will Find Here
Three threads run through the blog. The first is pricing-model selection: when does a Black-Scholes price tell you something useful, when does it lie, and which alternative model (Heston, SABR, local volatility, jump-diffusion, variance gamma) repairs which specific failure mode. The second is volatility-surface diagnostics: skew, term structure, vol-of-vol, and what each tells you about dealer hedging flow, crash insurance demand, and event positioning. The third is market-regime case studies: deep dives on specific episodes (gamma squeezes, vol crushes, rate-shock weeks) where the framework's outputs lined up, or didn't, with what subsequently happened.
How These Posts Connect to the Platform
Most posts cite specific surfaces in the analytics platform: the GEX docs, the term-structure docs, the model-divergence docs, or one of the per-Greek pages under /documentation/greeks. The intent is that a post explains a concept once, in context, and the canonical docs explain the same concept reusably. If you are coming in cold, the options market-structure ontology hub is a good map of how the canonical concepts connect.
Suggested Reading Order
If you are new to the platform, a useful order is: start with Black-Scholes as the universal frame, then pick whichever piece of the surface is bothering you and read the relevant model post (Heston for stochastic volatility, SABR for skew dynamics, local volatility for term-structure-aware deltas, jump-diffusion for tail risk, variance gamma for return non-normality). Once the model layer is grounded, move to the volatility-surface diagnostics: skew, term structure, implied vs realized. Read the case-study posts after that foundation, since they reference the diagnostic surfaces continuously.
Most posts assume working familiarity with the Greeks. If a post references vanna, charm, vomma, or veta and the meaning is not immediate, the per-Greek reference pages are short canonical explainers; the glossary covers the broader vocabulary. The Options Volatility Tutor GPT is a guided walkthrough for readers who prefer to learn by asking questions.
Why Long-Form, Not Short-Form
The blog is deliberately not a daily news feed. Options analytics writing has a long-form bias because the questions worth answering rarely fit in a paragraph. A post about why a particular model breaks during a vol regime change has to explain the regime, the model, the failure mode, and the diagnostic that catches it. That is fifteen hundred to three thousand words, not three. We publish when the analysis is finished, not on a calendar. If you want catalog-style summaries for AI retrieval, see llms-full.txt, which concatenates the canonical reference content into a single AI-consumable document.