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Bayesian Markov Switching Stochastic Correlation Models
Casarin, Roberto - 2015
This paper builds on Asai and McAleer (2009) and develops a new multivariate Dynamic Conditional Correlation (DCC …) model where the parameters of the correlation dynamics and those of the log-volatility process are driven by two latent … European sovereign debt crisis. Moreover, we find strong evidence supporting the existence of a time-varying correlation …
Persistent link: https://www.econbiz.de/10013035516
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A Bayesian Approach to Inference on Probabilistic Surveys
Del Negro, Marco; Casarin, Roberto; Bassetti, Federico - 2022
We propose a nonparametric Bayesian approach for conducting inference on probabilistic surveys. We use this approach to study whether U.S. Survey of Professional Forecasters density projections for output growth and inflation are consistent with the noisy rational expectations hypothesis. We...
Persistent link: https://www.econbiz.de/10014080529
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Sparse Graphical Vector Autoregression : A Bayesian Approach
Ahelegbey, Daniel Felix - 2019
In high-dimensional vector autoregressive (VAR) models, it is natural to have large number of predictors relative to the number of observations, and a lack of efficiency in estimation and forecasting. In this context, model selection is a difficult issue and standard procedures may often be...
Persistent link: https://www.econbiz.de/10012904383
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Efficient Gibbs Sampling for Markov Switching GARCH Models
Billio, Monica - 2013
We develop efficient simulation techniques for Bayesian inference on switching GARCH models. Our contribution to existing literature is manifold. First, we discuss different multi-move sampling techniques for Markov Switching (MS) state space models with particular attention to MS-GARCH models....
Persistent link: https://www.econbiz.de/10013088788
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Sparse Graphical Vector Autoregression : A Bayesian Approach
Ahelegbey, Daniel Felix - 2016
This paper considers a sparsity approach for inference in large vector autoregressive (VAR) models. The approach is based on a Bayesian procedure and a graphical representation of VAR models. We discuss a Markov chain Monte Carlo algorithm for sparse graph selection, parameter estimation, and...
Persistent link: https://www.econbiz.de/10013005518
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Bayesian Inference for Mixtures of Stable Distributions
Casarin, Roberto - 2006
In many different fields such as hydrology, telecommunications, physics of condensed matter and finance, the gaussian model results unsatisfactory and reveals difficulties in fitting data with skewness, heavy tails and multimodality. The use of stable distributions allows for modelling skewness...
Persistent link: https://www.econbiz.de/10012735217
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Adaptive Sticky Generalized Metropolis
Leisen, Fabrizio; Casarin, Roberto; Luengo, David; … - Dipartimento di Economia, Università Ca' Foscari Venezia - 2013
We introduce a new class of adaptive Metropolis algorithms called adaptive sticky algorithms for efficient general-purpose simulation from a target probability distribution. The transition of the Metropolis chain is based on a multiple-try scheme and the different proposals are generated by...
Persistent link: https://www.econbiz.de/10010735577
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Business Cycle and Stock Market Volatility: A Particle Filter Approach
Casarin, Roberto; Trecroci, Carmine - Université Paris-Dauphine (Paris IX) - 2006
The recent observed decline of business cycle variability suggests that broad macroeconomic risk may have fallen as well. This may in turn have some impact on equity risk premia. We investigate the latent structures in the volatilities of the business cycle and stock market valuations by...
Persistent link: https://www.econbiz.de/10011072864
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Bayesian Inference on Dynamic Models with Latent Factors
Billio, Monica; Casarin, Roberto; Sartore, Domenico - Dipartimento di Economia, Università Ca' Foscari Venezia - 2007
In time series analysis, latent factors are often introduced to model the heterogeneous time evolution of the observed processes. The presence of unobserved components makes the maximum likelihood estimation method more difficult to apply. A Bayesian approach can sometimes be preferable since it...
Persistent link: https://www.econbiz.de/10005113373
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Bayesian Inference in Dynamic Models with Latent Factors
Billio, Monica; Casarin, Roberto; Sartore, Domenico - 2012
In time series analysis, latent factors are often introduced to model the heterogeneous time evolution of the observed processes. The presence of unobserved components makes the maximum likelihood estimation method more difficult to apply. A Bayesian approach can sometimes be preferable since it...
Persistent link: https://www.econbiz.de/10014067403