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“Value at Risk” (VaR) as a book category covers the theory and practice of measuring the potential maximum loss of a portfolio over a specified time horizon at a chosen confidence level. Titles in this genre span foundational concepts and practical implementation, including parametric/variance-covariance, historical simulation, and Monte Carlo methods; data and modeling challenges such as fat tails, non-normality, and volatility clustering; and backtesting, validation, and model risk. They address applications to market, credit, and liquidity risk; derivatives and structured products; stress testing and scenario analysis; and the use of VaR in risk limits, capital allocation, and regulatory frameworks like Basel. Many works also present case studies and critiques that highlight VaR’s limitations and the motivation for alternatives such as Expected Shortfall. The audience includes risk managers, quantitative analysts, traders, auditors, regulators, and students seeking rigorous, practice-oriented guidance on portfolio risk measurement.

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