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GARCH Models
By: Christian Francq , Jean-Michel ZakoianeBook Publisher: John Wiley & Sons
Imprint: Wiley
Format: ePub Encrypted (DRM)
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This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation and tests. The book also provides coverage of several extensions such as asymmetric and multivariate models and looks at financial applications.
Key features: Provides up-to-date coverage of the current research in the probability, statistics and econometric theory of GARCH models. Numerous illustrations and applications to real financial series are provided. Supporting website featuring R codes, Fortran programs and data sets. Presents a large collection of problems and exercises.
This authoritative, state-of-the-art reference is ideal for graduate students, researchers and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.
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| Title of eBook: GARCH Models | |
| Release Date: 06-24-2011 | |
| Publisher: Wiley |
This eBook download is available in the following formats:
| Parent title | GARCH Models |
|---|---|
| Encrypted (DRM) | Yes |
| SKU | 9780470670040 |
| File size | 6769 |
| Security | n/a |
| Printing | Not allowed |
| Copying | Not allowed |
| Read aloud | No Sys requirements Download reader |
| Devices | Samsung Tablet, Apple Ipad & Iphone, Barnes & Noble Nook, Kobo eReader, Aluratek Libre, Iliad, Nokia, Blackberry, Hanlin |
| Note | Excellent navigation features are available via Adobe such as bookmarks and a quick access table of contents. Text search is easily accessible. An Adobe DRM-protected file is different than a pdf file in that it uses Adobe DRM (Digital Rights Management) technology, which authors and publishers use to protect their content from illegal online distribution and to set certain privileges such as restrictions on copying and printing. |
GARCH Models
Chapter One
Classical Time Series Models and Financial Series
The standard time series analysis rests on important concepts such as stationarity, autocorrelation, white noise, innovation, and on a central family of models, the autoregressive moving average (ARMA) models. We start by recalling their main properties and how they can be used. As we shall see, these concepts are insufficient for the analysis of financial time series. In particular, we shall introduce the concept of volatility, which is of crucial importance in finance.
In this chapter, we also present the main stylized facts (unpredictability of returns, volatility clustering and hence predictability of squared returns, leptokurticity of the marginal distributions, asymmetries, etc.) concerning financial series.
1.1 Stationary Processes
Stationarity plays a central part in time series analysis, because it replaces in a natural way the hypothesis of independent and identically distributed (iid) observations in standard statistics.
Consider a sequence of real random variables (Xt)t]member of]Z, defined on the same probability space. Such a sequence is called a time series, and is an example of a discrete-time stochastic process.
We begin by introducing two standard notions of stationarity.
Definition 1.1 (Strict stationarity) The process (Xt) is said to be strictly stationary if the vectors (X1, ..., Xk)' and (X1+h, ..., Xk+h)' have the same joint distribution, for any k N and any h [member of] Z.
The following notion may seem less demanding, because it only constrains the first two moments of the variables Xt, bu
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