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Joint Bayesian analysis of parameters and states in nonlinear, non-Gaussian state space models
Barra, István; Hoogerheide, Lennart; Koopman, Siem Jan; … - 2014
We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear non-Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent...
Persistent link: https://www.econbiz.de/10010399681
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Bayesian Estimation of an Extended Local Scale Stochastic Volatility Model
Deschamps, Philippe J. - 2011
A new version of the local scale model of Shephard (1994) is presented. Its features are identically distributed evolution equation disturbances, the incorporation of in-the-mean effects, and the incorporation of variance regressors. A Bayesian posterior simulator and a new simulation smoother...
Persistent link: https://www.econbiz.de/10013120871
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Nonparametric Bayesian Volatility Estimation
Gugushvili, Shota - 2020
Given discrete time observations over a fixed time interval, we study a nonparametric Bayesian approach to estimation of the volatility coefficient of a stochastic differential equation. We postulate a histogram-type prior on the volatility with piecewise constant realisations on bins forming a...
Persistent link: https://www.econbiz.de/10012852986
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Bayesian analysis of output gap in Barbados
Agbeyegbe, Terence D. - In: Latin American journal of central banking : LAJCB 1 (2020) 1/4, pp. 1-14
This article contributes to understanding the performance of various unobserved components (UC) models in fitting Barbados’ real GDP. Relying on recent UC models techniques, it finds support for the UC model that captures correlated disturbances, but not for the model that does not. The best...
Persistent link: https://www.econbiz.de/10012545648
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Estimation of SVJD Models with Bayesian Methods and Power-Variation Estimators
Fičura, Milan - 2018
asset price jumps. The models are estimated by a combination of a MCMC algorithm and a SIR Particle Filter. The performance …
Persistent link: https://www.econbiz.de/10012914862
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Identifying Price Jumps from Daily Data with Bayesian vs. Non-Parametric Methods
Fičura, Milan - 2017
parametric approach utilizing a Stochastic-Volatility-Jump-Diffusion (SVJD) model, estimated with MCMC and extended with Particle …-sample estimation does the MCMC based parametric approach significantly outperform the L-Estimator. In the case of the out …-sample estimates, based on a combination of MCMC an Particle Filters, used to sequentially estimate the jump occurrences immediately at …
Persistent link: https://www.econbiz.de/10012964932
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Bayesian Backtesting for Counterparty Risk Models
Arnsdorf, Matthias; Zelvyte, Mante - 2022
In this article we introduce a new framework for counterparty risk model backtesting based on Bayesian methods. This provides a conceptually sound approach for analyzing model performance which is also straightforward to implement. We show that our methodology provides important advantages over...
Persistent link: https://www.econbiz.de/10013305804
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Estimating Stochastic Volatility and Jumps Using High-Frequency Data and Bayesian Methods
Fičura, Milan - 2015
-parametric power-variation approach using high-frequency returns, and the parametric Bayesian approach (MCMC estimation of SVJD models …
Persistent link: https://www.econbiz.de/10013030080
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Variable selection and functional form uncertainty in cross-country growth regressions
Salimans, Tim - 2011
Regression analyses of cross-country economic growth data are complicated by two main forms of model uncertainty: the uncertainty in selecting explanatory variables and the uncertainty in specifying the functional form of the regression function. Most discussions in the literature address these...
Persistent link: https://www.econbiz.de/10011382708
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The time-varying evolution of inflation risks
Korobilis, Dimitris; Landau, Bettina; Musso, Alberto; … - 2021
This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting in ation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting in ation with the ability of quantile regression to...
Persistent link: https://www.econbiz.de/10012643282