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Sparse Change-point HAR Models for Realized Variance

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  • Arnaud Dufays
  • Jeroen V.K. Rombouts

Abstract

Change-point time series specifications constitute flexible models that capture unknown structural changes by allowing for switches in the model parameters. Nevertheless most models suffer from an over-parametrization issue since typically only one latent state variable drives the switches in all parameters. This implies that all parameters have to change when a break happens. To gauge whether and where there are structural breaks in realized variance, we introduce the sparse change-point HAR model. The approach controls for model parsimony by limiting the number of parameters which evolve from one regime to another. Sparsity is achieved thanks to employing a nonstandard shrinkage prior distribution. We derive a Gibbs sampler for inferring the parameters of this process. Simulation studies illustrate the excellent performance of the sampler. Relying on this new framework, we study the stability of the HAR model using realized variance series of several major international indices between January 2000 and August 2015.

Suggested Citation

  • Arnaud Dufays & Jeroen V.K. Rombouts, 2016. "Sparse Change-point HAR Models for Realized Variance," Cahiers de recherche 1607, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    • Handle: RePEc:lvl:crrecr:1607
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    Cited by:

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    More about this item

    Keywords

    Realized variance; Bayesian inference; Time series; Shrinkage prior; Change-point model; Online forecasting;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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