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Jump variation estimation with noisy high frequency financial data via wavelets
Zhang, Xin; Kim, Donggyu; Wang, Yazhen - In: Econometrics : open access journal 4 (2016) 3, pp. 1-26
-step procedure with detection and estimation. In Step 1, we detect the jump locations by performing wavelet transformation on the … observed noisy price processes. Since wavelet coefficients are significantly larger at the jump locations than the others, we … calibrate the wavelet coefficients through a threshold and declare jump points if the absolute wavelet coefficients exceed the …
Persistent link: https://www.econbiz.de/10011568279
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A smooth permanent surge process
González, Andrés (contributor) - 2004 - [Elektronische Ressource]
In this paper we introduce the Smooth Permanent Surge [SPS] model. The model is an integrated non lineal moving average process with possibly unit roots in the moving average coefficients. The process nests the Stochastic Permanent Break [STOPBREAK] process by Engle and Smith (1999) and in a...
Persistent link: https://www.econbiz.de/10002465176
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Markov Chain Monte Carlo Methods for Generalized Stochastic Volatility Models
Chib, Siddhartha; Shephard, Neil; Nardari, Federico - 2001
This paper is concerned with simulation based inference in generalized models of stochastic volatility defined by heavy-tailed student-t distributions (with unknown degrees of freedom) and covariate effects in the observation and volatility equations and a jump component in the observation...
Persistent link: https://www.econbiz.de/10014142429
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The Gibbs Sampler with Particle Efficient Importance Sampling for State-Space Models
Grothe, Oliver - 2017
We consider Particle Gibbs (PG) as a tool for Bayesian analysis of non-linear non-Gaussian state-space models. PG is a Monte Carlo (MC) approximation of the standard Gibbs procedure which uses sequential MC (SMC) importance sampling inside the Gibbs procedure to update the latent and potentially...
Persistent link: https://www.econbiz.de/10012970355
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Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models
Koopman, Siem Jan - 2012
We introduce a new efficient importance sampler for nonlinear non-Gaussian state space models. We propose a general and efficient likelihood evaluation method for this class of models via the combination of numerical and Monte Carlo integration methods. Our methodology explores the idea that...
Persistent link: https://www.econbiz.de/10013115029
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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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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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Discrete-time stochastic volatility models and MCMC-based statistical inference
Hautsch, Nikolaus (contributor); Ou, Yangguoyi (contributor) - 2008
In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap proach which is easily implemented in empirical...
Persistent link: https://www.econbiz.de/10003770817
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Numerically accelerated importance sampling for nonlinear non-Gaussian state space models
Koopman, Siem Jan; Lucas, André; Scharth, Marcel - 2012
We introduce a new efficient importance sampler for nonlinear non-Gaussian state space models. We propose a general and efficient likelihood evaluation method for this class of models via the combination of numerical and Monte Carlo integration methods. Our methodology explores the idea that...
Persistent link: https://www.econbiz.de/10011386179
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Improving Volatility Identification and Prediction of Realized Stochastic Volatility Model with Implied volatility
Zhang, Zehua - 2020
We examine a group of extended realized stochastic volatility (RSV) models with ex-ante volatility information added to the framework. The most advantageous specification is the one with implied volatility (IV) as an explanatory variable in the latent volatility process, which produces an...
Persistent link: https://www.econbiz.de/10012849247