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A0489
Title: Sparse change-point and Markov-switching HAR models for realized volatility Authors:  Arnaud Dufays - Namur University (Belgium) [presenting]
Jeroen Rombouts - ESSEC Business School (France)
Abstract: Change-point and Markov-switching 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. We introduce sparse change-point and Markov-switching processes, a new approach for detecting which parameters change over time. We propose shrinkage prior distributions allowing to control model parsimony by limiting the number of parameters which evolve from one regime to another. Additionally, we derive a Gibbs sampler for inferring the parameters of these processes. Relying on this new framework, we study the stability of the HAR model using realized volatilities series of eleven international indices between January 2000 and August 2015.