State-space stochastic volatility models: A review of estimation algorithms

被引:0
|
作者
Capobianco, E
机构
[1] Department of Statistical Sciences, University of Padua
来源
关键词
stochastic volatility; linear and nonlinear state space representation; simulation techniques; generalized bilinear stochastic volatility processes; estimation algorithms;
D O I
10.1002/(SICI)1099-0747(199612)12:4<265::AID-ASM288>3.0.CO;2-N
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
Stochastic volatility models (SVMs) represent an important framework for the analysis of financial time series data, together with ARCH-type models; but unlike the latter, the former, at least from the statistical point of view, cannot rely on the possibility of obtaining exact inference, in particular with regard to maximum likelihood estimates for the parameters of interest. For SVMs, usually only approximate results can be obtained, unless particularly sophisticated estimation strategies like exact non-gaussian filtering methods or simulation techniques are employed. In this paper we review SVM and present a new characterization for them, called 'generalized bilinear stochastic volatility'.
引用
收藏
页码:265 / 279
页数:15
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