000			03298nam a22004935i 4500
001			978-3-540-78657-3
003			DE-He213
005			20161121231004.0
007			cr nn 008mamaa
008			100715s2008 gw | s |||| 0|eng d
020			_a9783540786573 _9978-3-540-78657-3
024	7		_a10.1007/978-3-540-78657-3 _2doi
050		4	_aHB139-141
072		7	_aKCH _2bicssc
072		7	_aBUS021000 _2bisacsh
082	0	4	_a330.015195 _223
100	1		_aArdia, David. _eauthor.
245	1	0	_aFinancial Risk Management with Bayesian Estimation of GARCH Models _h[electronic resource] : _bTheory and Applications / _cby David Ardia.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2008.
300			_aXIV, 206 p. 27 illus. _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 Economics and Mathematical System, _x0075-8442 ; _v612
505	0		_aBayesian Statistics and MCMC Methods -- Bayesian Estimation of the GARCH(1, 1) Model with Normal Innovations -- Bayesian Estimation of the Linear Regression Model with Normal-GJR(1, 1) Errors -- Bayesian Estimation of the Linear Regression Model with Student-t-GJR(1, 1) Errors -- Value at Risk and Decision Theory -- Bayesian Estimation of the Markov-Switching GJR(1, 1) Model with Student-t Innovations -- Conclusion.
520			_aFor his excellent monograph, David Ardia won the Chorafas prize 2008 at the University of Fribourg Switzerland. This book presents methodologies for the Bayesian estimation of GARCH models and their application to financial risk management. The study of these models from a Bayesian viewpoint is relatively recent and can be considered very promising due to the advantages of the Bayesian approach, in particular the possibility of obtaining small-sample results and integrating these results in a formal decision model. The first two chapters introduce the work and give an overview of the Bayesian paradigm for inference. The next three chapters describe the estimation of the GARCH model with Normal innovations and the linear regression models with conditionally Normal and Student-t-GJR errors. The sixth chapter shows how agents facing different risk perspectives can select their optimal Value at Risk Bayesian point estimate and documents that the differences between individuals can be substantial in terms of regulatory capital. The last chapter proposes the estimation of a Markov-switching GJR model.
650		0	_aEconomics, Mathematical.
650		0	_aStatistics.
650		0	_aEconometrics.
650		0	_aMacroeconomics.
650	1	4	_aEconomics.
650	2	4	_aEconometrics.
650	2	4	_aMacroeconomics/Monetary Economics//Financial Economics.
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: _z9783540786566
830		0	_aLecture Notes in Economics and Mathematical System, _x0075-8442 ; _v612
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-540-78657-3
912			_aZDB-2-SBE
950			_aBusiness and Economics (Springer-11643)
999			_c506901 _d506901