Marginal Likelihood for Markov-Switching and Change-Point GARCH Models

Bauwens, Luc; Dufays, Arnaud; Rombouts, Jeroen V.K.

doi:10.2139/ssrn.1965660

Cited by 35 publications

(34 citation statements)

References 37 publications

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“…We are aware that univariate GARCH and Markov-Switching models have been estimated in a single step(Bauwens et al, 2014;Augustyniak, 2014;Marcucci, 2005) Pelletier (2006). has offered an example of a DCC-GARCH with Markov-Switching.5 The Euro was launched on 1 January 1999 and replaced by 2002 the national currencies.…”

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confidence: 99%

Will the crisis “tear us apart”? Evidence from the EU

Pappas

Ingham

Izzeldin

et al. 2016

International Review of Financial Analysis

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We examine the synchronisation of the European Union (EU) financial markets before and during the 2007 global financial crisis. We use an Asymmetric Dynamic Conditional Correlation (ADCC)-GARCH framework to control for the time-varying correlations and a Markov-Switching model to identify regime changes. Our sample considers 27 EU nations for the period 2000-2011. For each nation we formulate several characteristics of the crisis such as, synchronicity, duration and intensity measures. We find that the more recent EU members had a lagged entry to the crisis regime, were less adversely affected, show higher correlation between their stock markets and have their credit scores being revised more frequently relative to established EU members. We also find that higher levels of sovereign debt and lower levels of industrialisation positively impact crisis duration and intensity. Our results refute the notion of uniform integration of EU financial markets as evident from the highly non-synchronised observed crisis experience among the EU members. As such, one-size fits all policies are likely to be ineffective.

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confidence: 99%

Will the crisis “tear us apart”? Evidence from the EU

Pappas

Ingham

Izzeldin

et al. 2016

International Review of Financial Analysis

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“…The first Equation (20) refers to the DiffeRential Evolution Adaptive Metropolis (DREAM) proposal distribution of [18] and is the MCMC analog of the DE mutation (17). In their paper, it is shown that the proposal distribution is symmetric and so that the acceptance ratio is independent of the proposal density.…”

Section: Choice Of Mcmc Kernelsmentioning

confidence: 99%

Evolutionary Sequential Monte Carlo Samplers for Change-Point Models

Dufays

2016

Econometrics

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Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC) methods. Not only do SMC algorithms draw posterior distributions of static or dynamic parameters but additionally they provide an estimate of the marginal likelihood. The tempered and time (TNT) algorithm, developed in this paper, combines (off-line) tempered SMC inference with on-line SMC inference for drawing realizations from many sequential posterior distributions without experiencing a particle degeneracy problem. Furthermore, it introduces a new MCMC rejuvenation step that is generic, automated and well-suited for multi-modal distributions. As this update relies on the wide heuristic optimization literature, numerous extensions are readily available. The algorithm is notably appropriate for estimating change-point models. As an example, we compare several change-point GARCH models through their marginal log-likelihoods over time.

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“…Compared to other competitive volatility models such as Markov Switching GARCH models (Haas, Mittnik and Paolella, 2004;Francq and Zakoïan, 2008;Bauwens, Dufays and Rombouts, 2014), the AP-GARCH specification has a simpler probability structure and it is easier to estimate by maximum likelihood-type methods, a fact that makes it quite popular. However, a serious deficiency of the AP-GARCH model is that it assumes time-invariant parameters.…”

Section: Introductionmentioning

confidence: 99%

Bayesian analysis of periodic asymmetric power GARCH models

Aknouche

Demmouche

Dimitrakopoulos

et al. 2019

Studies in Nonlinear Dynamics &Amp; Econometrics

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In this paper, we set up a generalized periodic asymmetric power GARCH (PAP-GARCH) model whose coefficients, power, and innovation distribution are periodic over time. We first study its properties, such as periodic ergodicity, finiteness of moments and tail behavior of the marginal distributions. Then, we develop an MCMC algorithm, based on the Griddy-Gibbs sampler, under various distributions of the innovation term (Gaussian, Student-t, mixed Gaussian-Student-t). To assess our estimation method we conduct volatility and Value-at-Risk forecasting. Our model is compared against other competing models via the Deviance Information Criterion (DIC). The proposed methodology is applied to simulated and real data.

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Marginal Likelihood for Markov-Switching and Change-Point GARCH Models

Cited by 35 publications

References 37 publications

Will the crisis “tear us apart”? Evidence from the EU

Will the crisis “tear us apart”? Evidence from the EU

Evolutionary Sequential Monte Carlo Samplers for Change-Point Models

Bayesian analysis of periodic asymmetric power GARCH models

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