Time Series: Applications to Finance (Wiley Series in Probability and Statistics) - Hardcover

Über die Autorin bzw. den Autor

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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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:
* Nonstationarity
* Heteroskedasticity
* 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.

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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: * Nonstationarity * Heteroskedasticity * 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.