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See detailSpecific Markov-switching behaviour for ARMA parameters
Carpantier, Jean-Francois UL; Dufays, Arnaud

E-print/Working paper (2014)

Change-point (CP) and Markov-switching (MS) Auto-regressive models have been intensively discussed over the last two decades due to their flexibility and their relatively simple estimations. Although CP ... [more ▼]

Change-point (CP) and Markov-switching (MS) Auto-regressive models have been intensively discussed over the last two decades due to their flexibility and their relatively simple estimations. Although CP- and MS-ARMA models constitute a natural extension, they have been barely studied. The main reason comes from that classical inference fails when estimating these models. In particular the CP- and MS-ARMA models exhibit path dependence problem that renders the likelihood out of reach. We propose an estimation method that circumvents the issue. Our MCMC algorithm resting on the sticky in.finite hidden Markov-switching model (sticky IHMM) self-determines the number of regimes as well as the specification : CP or MS. Furthermore, the CP and MS frameworks usually assume that all the model parameters vary from one regime to another. We relax this restrictive assumption. As illustrated by simulations realized on moderate samples (300 observations), the sticky IHMM-ARMA algorithm detects which parameters of the model change over regimes. Applications to the U.S. GDP growth and the DJIA realized volatility highlight this flexibility by estimating different structural breaks for the mean and the variance parameters. [less ▲]

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See detailCommodities Inventory Effect
Carpantier, Jean-Francois UL; Dufays, Arnaud

E-print/Working paper (2013)

Does commodity price volatility increase when inventories are low? We are the first ones to document this relationship. To that aim, we estimate asymmetric volatility models for a large set of commodities ... [more ▼]

Does commodity price volatility increase when inventories are low? We are the first ones to document this relationship. To that aim, we estimate asymmetric volatility models for a large set of commodities over 1994-2011. Since inventories are hard to measure, especially for high frequency data, we use positive return shocks as a new original proxy for inventories and find that asymmetric GARCH models reveal a significant inventory effect for many commodities. The results look robust. They hold if we allow the unconditional variance to vary over time and if we relax the parametric form. [less ▲]

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