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Generalized dynamic linear models for financial time series

Author

Listed:
  • Campagnoli Patrizia

    (University of Pavia, Italy)

  • Muliere Pietro

    (University of Bocconi, Italy)

  • Petrone Sonia

    (Department of Economics, University of Insubria, Italy)

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.

Suggested Citation

  • Campagnoli Patrizia & Muliere Pietro & Petrone Sonia, "undated". "Generalized dynamic linear models for financial time series," Economics and Quantitative Methods qf0003, Department of Economics, University of Insubria.
    • Handle: RePEc:ins:quaeco:qf0003
    as

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    Keywords

    dynamic linear models; conditionally gaussian models; Kalman filter; stochastic regressors; stochastic volatility; GARcH models.;
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