Elements of Financial Time Series fills a gap in the market in the area of financial time series analysis by giving both conceptual and practical illustrations. Examples and discussions in the later chapters of the book make recent developments in time series more accessible. Examples from finance are maximized as much as possible throughout the book.
* Full set of exercises is displayed at the end of each chapter.
* First seven chapters cover standard topics in time series at a high-intensity level.
* Recent and timely developments in nonstandard time series techniques are illustrated with real finance examples in detail.
* Examples are systemically illustrated with S-plus with codes and data available on an associated Web site.
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Expert coverage of time series techniques for financial applications
Time Series is designed to help readers grasp the conceptual underpinnings of time series modeling in order to gain a deeper understanding of the ever-changing dynamics of the financial world. It covers theory and application equally for readers from both financial and mathematical backgrounds.
The book offers succinct coverage of standard topics in statistical time series-such as forecasting and spectral analysis-in a manner that is both technical and conceptual. Recent developments in nonstandard time series techniques are discussed and illustrated in detail with real financial examples. These techniques include:
* Multivariate time series
* State space modeling and stochastic volatility
* Multivariate GARCH
* Cointegrations and common trends
All examples are systematically illustrated with S-Plus(r) and highlight the relevance of time series in financial applications. Detailed analyses and explanations for the S-Plus commands, as well as challenging end-of-chapter exercises, are also provided.
NGAI HANG CHAN, PhD, is Professor of Statistics and Director of the Risk Management Science Program at the Chinese University of Hong Kong and Professor of Statistics at Carnegie Mellon University.
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