Sparse Change-point HAR Models for Realized Variance

Arnaud Dufays and Jeroen V.K. Rombouts

Cahiers de recherche from Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques

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.

Keywords: Realized variance; Bayesian inference; Time series; Shrinkage prior; Change-point model; Online forecasting (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 C51 (search for similar items in EconPapers)
Date: 2016
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Related works:
Journal Article: Sparse Change-point HAR Models for Realized Variance (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:lvl:crrecr:1607

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