Bayesian analysis of switching ARCH models

Kaufmann, Sylvia; Frühwirth‐Schnatter, Sylvia

doi:10.1111/1467-9892.00271

Cited by 49 publications

(37 citation statements)

References 37 publications

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“…However, at least Geweke (1989), Kleibergen and van Dijk (1993), Lubrano (1998, 2002), Nakatsuma (2000), Kaufmann and Fruhwirth-Schnatter (2002), Vrontos et al (2000Vrontos et al ( , 2003, and Bauwens et al (2006) have employed the Bayesian framework.…”

Section: Econometric Methodologymentioning

confidence: 99%

Robustness of the risk–return relationship in the U.S. stock market

Lanne

Luoto

2008

Finance Research Letters

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In this paper, we study the risk-return relationship in monthly U.S. stock returns (1928:1-2004:12) using GARCH-in-Mean models. In particular, we consider the robustness of the relationship with respect to the omission of the intercept term in the equation for the expected excess return recently recommended by Lanne and Saikkonen (2006). The existence of the relationship is quite robust, but its estimated strength is dependent on the prior belief concerning the intercept. This is the case in particular in the first half of the sample period, where also the coefficient of the relative risk aversion is found to be smaller and the equity premium greater than in the latter half.JEL Classification: C11, C22, G12.

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Section: Econometric Methodologymentioning

confidence: 99%

Robustness of the risk–return relationship in the U.S. stock market

Lanne

Luoto

2008

Finance Research Letters

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“…When identification is imposed, misspecification diagnostics that are not available at the random permutation stage can be used. One such method is based on an analysis of predictive probabilities, called "p-scores" by Kaufmann and Frühwirth-Schnatter (2002). A simple variant of this method conditions on point estimates (posterior averages)θ i of the regression parameters and transition probabilities.…”

Section: Implementing the Constrained Permutation Samplermentioning

confidence: 99%

“…The conditional probability that Y t ≤ y t is easily computed from the Student-t integral, and the probabilities of the regimes can be computed by one pass of the discrete filter described in the Appendix of Kaufmann and Frühwirth-Schnatter (2002). If the model is correct, the p-scores should have independent uniform distributions asymptotically.…”

Section: Implementing the Constrained Permutation Samplermentioning

confidence: 99%

A flexible prior distribution for Markov switching autoregressions with Student-t errors

Deschamps

2006

Journal of Econometrics

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Abstract. This paper proposes an empirical Bayes approach for Markov switching autoregressions that can constrain some of the state-dependent parameters (regression coefficients and error variances) to be approximately equal across regimes. By flexibly reducing the dimension of the parameter space, this can help to ensure regime separation and to detect the Markov switching nature of the data. The permutation sampler with a hierarchical prior is used for choosing the prior moments, the identification constraint, and the parameters governing prior state dependence. The empirical relevance of the methodology is illustrated with an application to quarterly and monthly real interest rate data.

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“…Despite its attractiveness, the Bayesian analysis of GARCH model is relatively less explored. See for instance, the papers by Bauwens and Lubrano (1998), Pole (1998), Nakatsuma (2000), Vrontos, Dellaportas and Politis (2000), Kaufmann and Frühwirth-Schnatter (2002) and Ausin and Galeano (2007), among others.…”

Section: Introductionmentioning

confidence: 99%

A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation

Ausín

Galeano

Ghosh

2014

European Journal of Operational Research

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Financial time series analysis deals with the understanding of data collected on financial markets. Several parametric distribution models have been entertained for describing, estimating and predicting the dynamics of financial time series. Alternatively, this article considers a Bayesian semiparametric approach. In particular, the usual parametric distributional assumptions of the GARCH-type models are relaxed by entertaining the class of location-scale mixtures of Gaussian distributions with a Dirichlet process prior on the mixing distribution, leading to a Dirichlet process mixture model. The proposed specification allows for a greater exibility in capturing both the skewness and kurtosis frequently observed in financial returns. The Bayesian model provides statistical inference with finite sample validity. Furthermore, it is also possible to obtain predictive distributions for the Value at Risk (VaR), which has become the most widely used measure of market risk for practitioners. Through a simulation study, we demonstrate the performance of the proposed semiparametric method and compare results with the ones from a normal distribution assumption. We also demonstrate the superiority of our proposed semiparametric method using real data from the Bombay Stock Exchange Index (BSE-30) and the Hang Seng Index (HSI). In particular, the usual parametric distributional assumptions of the GARCH-type models are relaxed by entertaining the class of location-scale mixtures of Gaussian distributions with a Dirichlet process prior on the mixing distribution, leading to a Dirichlet process mixture model. The proposed specification allows for a greater flexibility in capturing both the skewness and kurtosis frequently observed in financial returns. The Bayesian model provide statistical inference with finite sample validity. Furthermore, it is also possible to obtain predictive distributions for the Value at Risk (VaR), which has become the most widely used measure of market risk for practitioners. Through a simulation study, we demonstrate the performance of the proposed semiparametric method and compare results with the ones from a normal distribution assumption. We also demonstrate the superiority of our proposed semiparametric method using real data from the Bombay Stock Exchange Index (BSE-30) and the Hang Seng Index (HSI).

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Bayesian analysis of switching ARCH models

Cited by 49 publications

References 37 publications

Robustness of the risk–return relationship in the U.S. stock market

Robustness of the risk–return relationship in the U.S. stock market

A flexible prior distribution for Markov switching autoregressions with Student-t errors

A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation

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