000 03119nam a22004575i 4500
001 978-3-540-26978-6
003 DE-He213
005 20161121230932.0
007 cr nn 008mamaa
008 100301s2005 gw | s |||| 0|eng d
020 _a9783540269786
_9978-3-540-26978-6
024 7 _a10.1007/b138400
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aK
_2bicssc
072 7 _aBUS061000
_2bisacsh
082 0 4 _a330.015195
_223
100 1 _aStraumann, Daniel.
_eauthor.
245 1 0 _aEstimation in Conditionally Heteroscedastic Time Series Models
_h[electronic resource] /
_cby Daniel Straumann.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2005.
300 _aXVI, 228 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Statistics,
_x0930-0325 ;
_v181
505 0 _aSome 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.
520 _aIn 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 Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic).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.
650 0 _aStatistics.
650 0 _aEconomics, Mathematical.
650 1 4 _aStatistics.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
650 2 4 _aQuantitative Finance.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540211358
830 0 _aLecture Notes in Statistics,
_x0930-0325 ;
_v181
856 4 0 _uhttp://dx.doi.org/10.1007/b138400
912 _aZDB-2-SMA
950 _aMathematics and Statistics (Springer-11649)
999 _c506127
_d506127