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“Stochastic Volatility” as a book category encompasses quantitative finance works that model an asset’s volatility as a random, time-evolving process rather than a constant. Books in this genre cover theory and models (e.g., Heston, SABR, jumps, and rough volatility), econometric estimation and filtering (MLE/Bayesian methods, particle filters, comparisons with GARCH), calibration to option smiles and volatility surfaces, and numerical techniques (Monte Carlo, finite differences, Fourier methods). They connect these methods to practical pricing and hedging of options and volatility products (e.g., VIX, variance swaps) across equities, FX, rates, and commodities, often blending empirical evidence with implementation details and code. Aimed at graduate students, researchers, and practitioners, the category provides rigorous foundations and applied tools for forecasting, pricing, and risk management in markets where volatility itself is stochastic.

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