stochastic volatility;
linear and nonlinear state space representation;
simulation techniques;
generalized bilinear stochastic volatility processes;
estimation algorithms;
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'.
机构:
Univ Calif Los Angeles, Dept Elect Engn, Adapt Syst Lab, Los Angeles, CA 90095 USAUniv Calif Los Angeles, Dept Elect Engn, Adapt Syst Lab, Los Angeles, CA 90095 USA
Sayed, AH
Subramanian, A
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机构:
Univ Calif Los Angeles, Dept Elect Engn, Adapt Syst Lab, Los Angeles, CA 90095 USAUniv Calif Los Angeles, Dept Elect Engn, Adapt Syst Lab, Los Angeles, CA 90095 USA
Subramanian, A
[J].
TOTAL LEAST SQUARES AND ERRORS-IN-VARIABLES MODELING: ANALYSIS, ALGORITHMS AND APPLICATIONS,
2002,
: 191
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202
机构:
Jiangnan Univ, Sch Internet Things Engn, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi, Jiangsu, Peoples R ChinaJiangnan Univ, Sch Internet Things Engn, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi, Jiangsu, Peoples R China
Ding, Feng
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机构:
Zhang, Xiao
Xu, Ling
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机构:
Jiangnan Univ, Sch Internet Things Engn, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi, Jiangsu, Peoples R ChinaJiangnan Univ, Sch Internet Things Engn, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi, Jiangsu, Peoples R China