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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

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

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

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

Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models

Koopman, Siem Jan - 2012

Persistent link: https://www.econbiz.de/10013115029

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

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

Sequential Monte Carlo Pricing of American-Style Options under Stochastic Volatility Models

Rambharat, Bhojnarine - 2013

We introduce a new method to price American-style options on underlying investments governed by stochastic volatility (SV) models. The method does not require the volatility process to be observed. Instead, it exploits the fact that the optimal decision functions in the corresponding dynamic...

Persistent link: https://www.econbiz.de/10013078765

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

Sample path generation of the stochastic volatility CGMY process and its application to path-dependent option pricing

Kim, Young Shin - In: Journal of risk and financial management : JRFM 14 (2021) 2/77, pp. 1-18

This paper proposes the sample path generation method for the stochastic volatility version of the CGMY process. We present the Monte-Carlo method for European and American option pricing with the sample path generation and calibrate model parameters to the American style S&P 100 index options...

Persistent link: https://www.econbiz.de/10012484130

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