11

490-542

53

 

John Wiley & Sons, Inc. (Visit publisher website)

2005

English

 

 

 

Advanced

 

 

Quantitative analysis, estimation, time series analysis, state-space models, local trend model, statistical inference, Kalman filter, properties of forecast error, state error recursion, state smoothing, smoothed state variance, missing values, initialization, estimation, linear state-space models, model transformation, CAPM with time-varying coefficients, ARMA models, Akaike`s approach, Harvey`s approach, Aoki`s approach, linear regression model, linear regression models with ARMA errors, scalar unobserved component model, Kalman filter and smoothing, state estimation error, disturbance smoothing, forecasting

 

The concepts of filtering, smoothing and predictions, are introduced in a user-friendly way by using a special case of the state-space model: the local trend model. The chapter begins by developing the foundation needed for deriving the recursive Kalman filter algorithm. After the brief introduction in Section 11.1, the discussion focuses at the application of the methodology which includes how missing values are handled, the estimation and prediction of time varying CAPM and structural time series models. All these examples come with the S-Plus code which makes use of the freely downloadable package SsfPack. This reading assumes knowledge of linear time series models, multivariate normal distribution and linear algebra.

 

11.1 Local Trend Model
11.1.1 Statistical Inference
11.1.2 Kalman Filter
11.1.3 Properties of Forecast Error
11.1.4 State Smoothing
11.1.5 Missing Values
11.1.6 Effect of Initialization
11.1.7 Estimation
11.1.8 S-Plus Commands Used
11.2 Linear State-Space Models
11.3 Model Transformation
11.3.1 CAPM with Time-Varying Coefficients
11.3.2 ARMA Models
11.3.3 Linear Regression Model
11.3.4 Linear Regression Models with ARMA Errors
11.3.5 Scalar Unobserved Component Model
11.4 Kalman Filter and Smoothing
11.4.1 Kalman Filter
11.4.2 State Estimation Error and Forecast Error
11.4.3 State Smoothing
11.4.4 Disturbance Smoothing
11.5 Missing Values
11.6 Forecasting
11.7 Application
Exercises
References

 

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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 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-PlusŪ commands and illustrations. Among the new material, 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

This is an ideal textbook for MBA students as well as a reference for researchers and professionals in business and finance.