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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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Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models
Barra, Istvan - 2016
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/10013005987
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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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Joint independent metropolis-hastings methods for nonlinear non-Gaussian state space models
Barra, Istvan; Hoogerheide, Lennart; Koopman, Siem Jan; … - 2013
Persistent link: https://www.econbiz.de/10010191411
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Alternative Formulations of the Leverage Effect in a Stochastic Volatility Model with Asymmetric Heavy-Tailed Errors
Deschamps, Philippe J. - 2016
compared with each other and with a GARCH formulation, using Bayes factors. MCMC estimation relies on a parametric proposal …, suggest that the last two leverage formulations strongly dominate the conventional one. The performance of the MCMC method is …
Persistent link: https://www.econbiz.de/10012998056
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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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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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Monetary policy regimes and the volatility of long-term interest rates
Queijo von Heideken, Virginia (contributor) - 2008
This paper addresses two important questions that have, so far, been studied separately in the literature. First, the paper aims at explaining the high volatility of long-term interest rates observed in the data, which is hard to replicate using standard macro models. Building a small-scale...
Persistent link: https://www.econbiz.de/10003651439
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Time-varying combinations of Bayesian dynamic models and equity momentum strategies
Basturk, Nalan; Grassi, Stefano; Hoogerheide, Lennart; … - 2016
A novel dynamic asset-allocation approach is proposed where portfolios as well as portfolio strategies are updated at every decision period based on their past performance. For modeling, a general class of models is specified that combines a dynamic factor and a vector autoregressive model and...
Persistent link: https://www.econbiz.de/10011563065
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Bayesian Analysis of a Threshold Stochastic Volatility Model
Wirjanto, Tony S. - 2013
This paper proposes a parsimonious threshold stochastic volatility (SV) model for financial asset returns. Instead of imposing a threshold value on the dynamics of the latent volatility process of the SV model, we assume that the innovation of the mean equation follows a threshold distribution...
Persistent link: https://www.econbiz.de/10013084224