Analysis of Financial Time Series

This title provides statistical tools and techniques needed to understand today's financial markets. The second edition of this critically acclaimed text provides a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This latest edition continues to emphasize empirical financial data and focuses on real-world examples. Following this approach, readers will master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure and high-frequency financial data, continuous-time models and Ito's Lemma, Value at Risk, multiple returns analysis, financial factor models, and econometric modeling via computation-intensive methods. The author begins with the basic characteristics of financial time series data, setting the foundation for the three main topics: analysis and application of univariate financial time series; return series of multiple assets; and, Bayesian inference in finance methods. This new edition is a thoroughly revised and updated text, including the addition of S-Plus(r) commands and illustrations. Exercises have been thoroughly updated and expanded and include the most current data, providing readers with more opportunities to put the models and methods into practice. Among the new material added to the text, readers will find: consistent covariance estimation under heteroscedasticity and serial correlation; alternative approaches to volatility modeling; financial factor models; state-space models; Kalman filtering; and, estimation of stochastic diffusion models. The tools provided in this text aid readers in developing a deeper understanding of financial markets through firsthand experience in working with financial data. This is an ideal textbook for MBA students as well as a reference for researchers and professionals in business and finance. 

 

Preface. Preface to First Edition. 1. Financial Time Series and Their Characteristics. 2. Linear Time Series Analysis and Its Applications. 3. Conditional Heteroscedastic Models. 4. Nonlinear Models and Their Applications. 5. High-Frequency Data Analysis and Market Microstructure. 6. Continuous-Time Models and Their Applications. 7. Extreme Values, Quantile Estimation, and Value at Risk. 8. Multivariate Time Series Analysis and Its Applications. 9. Principal Component Analysis and Factor Models. 10. Multivariate Volatility Models and Their Applications. 11. State-Space Models and Kalman Filter. 12. Markov Chain Monte Carlo Methods with Applications. Index.