rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Estimation in Conditionally Heteroscedastic Time Series Models
  name:
    name part: Straumann, Daniel.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2005
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 228 p. online resource.
  abstract: 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 Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic).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.
  table of contents: Some Mathematical Tools -- Financial Time Series: Facts and Models -- Parameter Estimation: An Overview -- Quasi Maximum Likelihood Estimation in Conditionally Heteroscedastic Time Series Models: A Stochastic Recurrence Equations Approach -- Maximum Likelihood Estimation in Conditionally Heteroscedastic Time Series Models -- Quasi Maximum Likelihood Estimation in a Generalized Conditionally Heteroscedastic Time Series Model with Heavy—tailed Innovations -- Whittle Estimation in a Heavy—tailed GARCH(1,1) Model.
  note: by Daniel Straumann.
  subject:
    topic: Statistics
  subject:
    topic: Economics, Mathematical
  subject:
    topic: Statistics
  subject:
    topic: Statistics for Business/Economics/Mathematical Finance/Insurance
  subject:
    topic: Quantitative Finance
  classification: QA276-280
  classification: 330.015195
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Statistics, 181
  identifier: 9783540269786
  identifier: http://dx.doi.org/10.1007/b138400
  record info:
    record creation date: 100301
    record change date: 20151204175703.0
    record identifier: 978-3-540-26978-6