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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

Nonparametric Estimation of the Volatility Under Microstructure Noise : Wavelet Adaptation

Hoffmann, Marc - 2010

wavelet thresholding, we construct adaptive estimators that achieve a nearly optimal rate within a large scale of smoothness …

Persistent link: https://www.econbiz.de/10013139169

Asymptotic properties of the estimator of the long-run coefficient in a dynamic model with integrated regressors and serially correlated errors

Maekawa, Koichi (contributor); McAleer, Michael (contributor) - 2001 - [Elektronische Ressource]

In this paper we examine the asymptotic properties of the estimator of the long-run coefficient (LRC) in a dynamic regression model with integrated regressors and serially correlated errors. We show that the OLS estimators of the regression coefficients are inconsistent but the OLS-based...

Persistent link: https://www.econbiz.de/10001644304

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

Finite sample comparison of parametric, semiparametric, and wavelet estimators of fractional integration

Nielsen, Morten Ørregaard (contributor); … - 2005

differencing parameter, d. This involves frequency domain, time domain, and wavelet based approaches and we consider both …-periodogram regression estimators are shown to be more robust to short-run dynamics than other semiparametric (frequency domain and wavelet … wavelet based estimators are heavily biased. -- Bias ; finite sample distribution ; fractional integration ; maximum …

Persistent link: https://www.econbiz.de/10003780898

Changes in persistence in outlier contaminated time series

Hirsch, Tristan; Rinke, Saskia - 2017

Outlying observations in time series influence parameter estimation and testing procedures, leading to biased estimates and spurious test decisions. Further inference based on these results will be misleading. In this paper the effects of outliers on the performance of ratio-based tests for a...

Persistent link: https://www.econbiz.de/10011581507

On the practice of Bayesian inference in basic economic time series models using Gibbs sampling

Pooter, Michiel de; Segers, Rene; Dijk, Herman K. van - 2006

Several lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling algorithm are presented. Models include the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model, the Instrumental Variables model...

Persistent link: https://www.econbiz.de/10011349180

Bayesian estimation of the GARCH(1,1) model with student-t innovations

Ardia, David; Hoogerheide, Lennart F. - 2010

This note presents the R package bayesGARCH (Ardia, 2007) which provides functions for the Bayesian estimation of the parsimonious and effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the tedious task of tuning a MCMC sampling...

Persistent link: https://www.econbiz.de/10011380176

Sequential Monte Carlo Sampling for State Space Models

Wuthrich, Mario V. - 2016

The aim of these notes is to revisit sequential Monte Carlo (SMC) sampling. SMC sampling is a powerful simulation tool for solving non-linear and/or non-Gaussian state space models. We illustrate this with several examples

Persistent link: https://www.econbiz.de/10012993836

Outlier Robust Cointegration Analysis

Franses, Philip Hans; Lucas, André - 1998

Standard unit root tests and cointegration tests are sensitive to atypical events such as outliers and structural breaks. This paper uses outlier robust estimation techniques to reduce the impact of these events on cointegration analysis. As a byproduct of computing the robust estimator, we...

Persistent link: https://www.econbiz.de/10014073583