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【书名】Estimation in Conditionally Heteroscedastic Time Series Models  
【作者】Daniel Straumann
【出版社】Springer
【版本】
【出版日期】2005
【文件格式】PDF

【文件大小】4.65 MB
【页数】241 Pages
【ISBN出版号】ISBN: 978-3-540-21135-8 
【资料类别】计量经济学，统计学，Time Series Analysis
【市面定价】89.95 Dollar, Amazon Paperback
【扫描版还是影印版】影印版
【是否缺页】完整
【关键词】Financial Time SeriesGARCH
【内容简介】

In his seminal 1982 paper, Robert F. Engle described a time series model with a time-varying volatility. Engle showed that this model, which he called ARCH (autoregressive conditionally heteroscedastic), is well-suited for the description of economic and financial price. Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic). This monograph concentrates on mathematical statistical problems associated with fitting conditionally heteroscedastic time series models to data. This includes the classical statistical issues of consistency and limiting distribution of estimators. Particular attention is addressed to (quasi) maximum likelihood estimation and misspecified models, along to phenomena due to heavy-tailed innovations. The used methods are based on techniques applied to the analysis of stochastic recurrence equations. Proofs and arguments are given wherever possible in full mathematical rigour. Moreover, the theory is illustrated by examples and simulation studies.

【目录】Content

1 Introduction

2 Some Mathematical Tool

3 Financial Time Series: Facts and Model

4 Parameter Estimation: An Overview

5 Quansi Maximum Likelyhood Estimation in Heterscedestic Time Series Model: A Stochastic Recurrence Equation Approach

6 Quansi Maximum Likelyhood Estimation in Heterscedestic Time Series Model

7 Quansi Maximum Likelyhood Estimation in Generalized Conditionally Heterscedestic Time Series Model with heavy tailed innovation

8 Whittle Estimates in Heavy Tailed GARCH(1.1) Model

Reference

【书评】one specialized book on financial time series analysis

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