Overview
Fitting local volatility is a book by Andrey Itkin, first published in 2020. In this catalogue it is classified mainly under Options and Volatility and Mathematical Finance, with secondary coverage of Quantitative Finance, Derivatives Pricing and Deep Learning for Finance.
Audience and depth. The rule-based model places its mathematical difficulty at advanced level (7/10); readers are typically expected to know calculus, linear algebra, machine learning, probability theory, statistics and stochastic processes.
Orientation. The theory-versus-practice balance could not be determined from metadata. The archive holds 6 papers whose topics and vocabulary overlap with this book; those links are inferred (see the research connections below).
How to read this page. Topics, difficulty, prerequisites and orientation are model-derived (rule-based, rule_based_v1) from bibliographic metadata such as title, subject headings and edition data. They were not produced by reading the book and are not a review. Relevance confidence is 98% and difficulty confidence is 85%; the catalogue lists no ratings or awards.