2

24-96

73

 

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

2005

English

 

 

 

Advanced

 

 

Quantitative analysis, probability, estimation, linear time series analysis, stationarity, correlation, autocorrelation function (ACF), testing individual ACF, Portmanteau test, white noise, linear time series, autoregressive (AR) model, AR(1) model, AR(2) model, AR(p) model, identifying AR models, partial autocorrelation function, information criteria, parameter estimation, model checking, goodness of fit, forecasting, n-step ahead forecast, moving average (MA) models, identifying MA order, forecasting using MA models, autoregressive moving average (ARMA) models, ARMA(1,1) model, general ARMA models, identifying ARMA models, forecasting using an ARMA model, unit-root nonstationarity, random walk, random walk with drift, trend-stationary time series, seasonal models, seasonal differencing, regression models with time series errors, consistent covariance matrix estimation, long-memory models

 

This chapter is intended for applied researchers wishing to develop time series models. Model properties for autoregressive, moving average ARMA, seasonal, unit root, regression models with autocorrelated errors and fractionally differenced series are developed. The properties of forecasts are clearly illustrated. The presentation focuses on the practical aspects of model applications and includes useful S-plus code for the many illustrations. The chapter presents an excellent illustration of commonly used model selection criteria. The chapter ending exercises are supported with data sets so that the reader can implement each of the models discussed.

 

2.1 Stationarity
2.2 Correlation and Autocorrelation Function
2.3 White Noise and Linear Time Series
2.4 Simple Autoregressive Models
2.4.1 Properties of AR Models
2.4.2 Identifying AR Models in Practice
2.4.3 Goodness of Fit
2.4.4 Forecasting
2.5 Simple Moving-Average Models
2.5.1 Properties of MA Models
2.5.2 Identifying MA Order
2.5.3 Estimation
2.5.4 Forecasting Using MA Models
2.6 Simple ARMA Models
2.6.1 Properties of ARMA(1,1) Models
2.6.2 General ARMA Models
2.6.3 Identifying ARMA Models
2.6.4 Forecasting Using an ARMA Model
2.6.5 Three Model Representations for an ARMA Model
2.7 Unit-Root Nonstationarity
2.7.1 Random Walk
2.7.2 Random Walk with Drift
2.7.3 Trend-Stationary Time Series
2.7.4 General Unit-Root Nonstationary Models
2.7.5 Unit-Root Test
2.8 Seasonal Models
2.8.1 Seasonal Differencing
2.8.2 Multiplicative Seasonal Models
2.9 Regression Models with Time Series Errors
2.10 Consistent Covariance Matrix Estimation
2.11 Long-Memory Models
Appendix: Some SCA Commands
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.