Estimation in Conditionally Heteroscedastic Time Series Models is popular PDF and ePub book, written by Daniel Straumann in 2006-01-27, it is a fantastic choice for those who relish reading online the Business & Economics genre. Let's immerse ourselves in this engaging Business & Economics book by exploring the summary and details provided below. Remember, Estimation in Conditionally Heteroscedastic Time Series Models can be Read Online from any device for your convenience.
Estimation in Conditionally Heteroscedastic Time Series Models Book PDF Summary
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 Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic).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.
Detail Book of Estimation in Conditionally Heteroscedastic Time Series Models PDF
- Author : Daniel Straumann
- Release : 27 January 2006
- Publisher : Springer Science & Business Media
- ISBN : 9783540269786
- Genre : Business & Economics
- Total Page : 239 pages
- Language : English
- PDF File Size : 21,9 Mb
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