The volatility of financial returns changes over time and‎,‎ for the last thirty years‎,‎ Generalized Autoregressive Conditional Heteroscedasticity ‎(‎GARCH‎)‎ models have provided the principal means of analyzing‎,‎ modeling and monitoring such changes‎.‎ Taking into account that financial returns typically exhibit heavy tails ‎- that is‎,‎ extreme values can occur from time to time ‎- Andrew Harvey‎'‎s new book shows how a small but radical change in the way GARCH models are formulated leads to a resolution of many of the theoretical problems inherent in the statistical theory‎.‎ The approach can also be applied to other aspects of volatility‎.‎ The more general class of Dynamic Conditional Score models extends to robust modeling of outliers in the levels of time series and to the treatment of time‎-varying relationships‎.‎ The statistical theory draws on basic principles of maximum likelihood estimation and‎,‎ by doing so‎,‎ leads to an elegant and unified treatment of nonlinear time‎-series modeling‎.‎