Generalized dynamic linear models for financial time series

Campagnoli Patrizia (), Muliere Pietro () and Petrone Sonia
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Campagnoli Patrizia: University of Pavia, Italy
Muliere Pietro: University of Bocconi, Italy
Petrone Sonia: Department of Economics, University of Insubria, Italy

Economics and Quantitative Methods from Department of Economics, University of Insubria

Abstract: 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 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.

Keywords: dynamic linear models; conditionally gaussian models; Kalman filter; stochastic regressors; stochastic volatility; GARcH models. (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cfn, nep-ets and nep-fin
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Persistent link: https://EconPapers.repec.org/RePEc:ins:quaeco:qf0003

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