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Generalized dynamic linear models for financial time series
Author(s) -
Campagnoli Patrizia,
Muliere Pietro,
Petrone Sonia
Publication year - 2001
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.428
Subject(s) - series (stratigraphy) , state space , stochastic volatility , state space representation , computer science , volatility (finance) , simple (philosophy) , gaussian , mathematics , class (philosophy) , finance , mathematical optimization , econometrics , algorithm , economics , artificial intelligence , statistics , paleontology , philosophy , physics , epistemology , quantum mechanics , biology
In this paper we consider a class of conditionally Gaussian state‐space models and discuss how they can provide a flexible and fairly simple tool for modelling financial time series, even in the presence of different components in the series, or of stochastic volatility. Estimation can be computed by recursive equations, which provide the optimal solution under rather mild assumptions. In more general models, the filter equations can still provide approximate solutions. We also discuss how some models traditionally employed for analysing financial time series can be regarded in the state‐space framework. Finally, we illustrate the models in two examples to real data sets. Copyright © 2001 John Wiley & Sons, Ltd.