An authoritative handbook on risk management techniques and simulations as applied to financial engineering topics, theories, and statistical methodologies

The Handbook of Financial Risk Management: Simulations and Case Studies illustrates the prac-tical implementation of simulation techniques in the banking and financial industries through the use of real-world applications.

Striking a balance between theory and practice, the "Handbook of Financial Risk Management: Simulations and Case Studies" demonstrates how simulation algorithms can be used to solve practical problems and showcases how accuracy and efficiency in implementing various simulation methods are indispensable tools in risk management. The book provides the reader with an intuitive understanding of financial risk management and deepens insight into those financial products that cannot be priced traditionally. The "Handbook of Financial Risk Management" also features: Examples in each chapter derived from consulting projects, current research, and course instructionTopics such as volatility, fixed-income derivatives, LIBOR Market Models, and risk measuresOver twenty-four recognized simulation modelsCommentary, data sets, and computer subroutines available on a chapter-by-chapter basis

As a complete reference for practitioners, the book is useful in the fields of finance, business, applied statistics, econometrics, and engineering. The "Handbook of Financial Risk Management" is also an excellent text or supplement for graduate and MBA-level students in courses on financial risk management and simulation.

A new edition of the comprehensive, hands-on guide to financial time series, now featuring S-Plus(R) and R software

Time Series: Applications to Finance with R and S-Plus(R), Second Edition is designed to present an in-depth introduction to the conceptual underpinnings and modern ideas of time series analysis. Utilizing interesting, real-world applications and the latest software packages, this book successfully helps readers grasp the technical and conceptual manner of the topic in order to gain a deeper understanding of the ever-changing dynamics of the financial world.

With balanced coverage of both theory and applications, this Second Edition includes new content to accurately reflect the current state-of-the-art nature of financial time series analysis. A new chapter on Markov Chain Monte Carlo presents Bayesian methods for time series with coverage of Metropolis-Hastings algorithm, Gibbs sampling, and a case study that explores the relevance of these techniques for understanding activity in the Dow Jones Industrial Average. The author also supplies a new presentation of statistical arbitrage that includes discussion of pairs trading and cointegration. In addition to standard topics such as forecasting and spectral analysis, real-world financial examples are used to illustrate recent developments in nonstandard techniques, including: NonstationarityHeteroscedasticityMultivariate time seriesState space modeling and stochastic volatilityMultivariate GARCHCointegration and common trends

The book's succinct and focused organization allows readers to grasp the important ideas of time series. All examples are systematically illustrated with S-Plus(R) and R software, highlighting the relevance of time series in financial applications. End-of-chapter exercises and selected solutions allow readers to test their comprehension of the presented material, and a related Web site features additional data sets.

Time Series: Applications to Finance with R and S-Plus(R) is an excellent book for courses on financial time series at the upper-undergraduate and beginning graduate levels. It also serves as an indispensible resource for practitioners working with financial data in the fields of statistics, economics, business, and risk management.