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
* Bayesian inference in finance methods
This new edition is a thoroughly revised and updated text,
including the addition of S-Plus® 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
* 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.

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