Analysis of Financial Time Series provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described.
The subject of ﬁnancial time series analysis has attracted substantial attention in recent years, especially with the 2003 Nobel awards to Professors Robert Engle and Clive Granger. At the same time, the ﬁeld of ﬁnancial econometrics has undergone various new developments, especially in high-frequency ﬁnance, stochastic volatil-ity, and software availability.
There is a need to make the material more complete and accessible for advanced undergraduate and graduate students, practitioners, and researchers. The main goals in preparing this second edition have been to bring the book up to date both in new developments and empirical analysis, and to enlarge the core material of the book by including consistent covariance estimation under heteroscedasticity and serial correlation, alternative approaches to volatility mod-eling, ﬁnancial factor models, state-space models, Kalman ﬁltering, and estimation of stochastic diffusion models.
The book therefore has been extended to 12 chapters and substantially revised to include S-Plus commands and illustrations. Many empirical demonstrations and exercises are updated so that they include the most recent data.
- Financial Time Series and Their Characteristics
- Linear Time Series Analysis and Its Applications
- Conditional Heteroscedastic Models
- Nonlinear Models and Their Applications
- High-Frequency Data Analysis and Market Microstructure
- Continuous-Time Models and Their Applications
- Extreme Values, Quantiles, and Value at Risk
- Multivariate Time Series Analysis and Its Applications
- Principal Component Analysis and Factor Models
- Multivariate Volatility Models and Their Applications
- State-Space Models and Kalman Filter
- Markov Chain Monte Carlo Methods with Applications