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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

This paper develops a method to improve the estimation of jump variation using high frequency data with the existence of market microstructure noises. Accurate estimation of jump variation is in high demand, as it is an important component of volatility in finance for portfolio allocation,...

Persistent link: https://www.econbiz.de/10011568279

Residual log-periodogram inference for long-run relationships

Hassler, Uwe; Mármol, Francesc; Velasco, Carlos - 2002

Persistent link: https://www.econbiz.de/10001751669

Structural Nonparametric Cointegrating Regression

Wang, Qiying; Phillips, Peter C. B. - 2008

Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is...

Persistent link: https://www.econbiz.de/10014217972

Model Specification between Parametric and Nonparametric Cointegration

Gao, Jiti - 2012

This paper considers a general model specification between a parametric co-integrating model and a nonparametric co-integrating model in a multivariate regression model, which involves a univariate integrated time series regressor and a vector of stationary time series regressors. A new and...

Persistent link: https://www.econbiz.de/10013101176

Residual log-periodogram inference for long-run relationships

Hassler, Uwe; Mármol, Francesc; Velasco, Carlos - 2002

analyze the benefits of a pooled version of the estimate. The empirical applicability of our general cointegration test is …

Persistent link: https://www.econbiz.de/10011524765

Nonparametric regression estimation for multivariate null recurrent processes

Cai, Biqing; Tjostheim, Dag - In: Econometrics : open access journal 3 (2015) 2, pp. 265-288

This paper discusses nonparametric kernel regression with the regressor being a d-dimensional ß-null recurrent process in presence of conditional heteroscedasticity. We show that the mean function estimator is consistent with convergence rate p n(T)hd, where n(T) is the number of regenerations...

Persistent link: https://www.econbiz.de/10011297654

Non-parametric transformation regression with non-stationary data

Linton, Oliver; Wang, Qiying - 2013

We examine a kernel regression smoother for time series that takes account of the error correlation structure as proposed by Xiao et al. (2008). We show that this method continues to improve estimation in the case where the regressor is a unit root or near unit root process.

Persistent link: https://www.econbiz.de/10009734305

Asymptotic Normality of Narrow-Band Least Squares in the Stationary Fractional Cointegration Model and Volatility Forecasting

Christensen, Bent Jesper; Nielsen, Morten Ørregaard - 2005

We consider semiparametric frequency domain analysis of cointegration between long memory processes, i.e. fractional … cointegration, allowing derivation of useful long-run relations even among stationary processes. The approach is due to Robinson …-run dynamics. We derive the asymptotic distribution theory for the FDLS estimator of the cointegration vector in the stationary …

Persistent link: https://www.econbiz.de/10014067273

A new approach to identifying generalized competing risks models with application to second-price auctions

Komarova, Tatiana - In: Quantitative economics : QE ; journal of the … 4 (2013) 2, pp. 269-328

This paper proposes an approach to proving nonparametric identification for distributions of bidders’ values in asymmetric second-price auctions. I consider the case when bidders have independent private values and the only available data pertain to the winner’s identity and the transaction...

Persistent link: https://www.econbiz.de/10011757066

A general semiparametric approach to inference with marker-dependent hazard rate models

Berg, Gerard J. van den; Janys, Lena; Mammen, Enno; … - 2014

We examine a new general class of hazard rate models for survival data, containing a parametric and a nonparametric component. Both can be a mix of a time effect and (possibly time-dependent) marker or covariate effects. A number of well-known models are special cases. In a counting process...

Persistent link: https://www.econbiz.de/10010386392