Author: Jun Yu
Publisher:
ISBN:
Category :
Languages : en
Pages : 39
Book Description
This paper reviews the method of model-fitting via the empirical characteristic function. The advantage of using this procedure is that one can avoid difficulties inherent in calculating or maximizing the likelihood function. Thus it is a desirable estimation method when the maximum likelihood approach encounters difficulties but the characteristic function has a tractable expression. The basic idea of the empirical characteristic function method is to match the characteristic function derived from the model and the empirical characteristic function obtained from data. Ideas are illustrated by using the methodology to estimate a diffusion model that includes a self-exciting jump component. A Monte Carlo study shows that the finite sample performance of the proposed procedure offers an improvement over a GMM procedure. An application using over 72 years of DJIA daily returns reveals evidence of jump clustering.
Empirical Characteristic Function Estimation and its Applications
Author: Jun Yu
Publisher:
ISBN:
Category :
Languages : en
Pages : 39
Book Description
This paper reviews the method of model-fitting via the empirical characteristic function. The advantage of using this procedure is that one can avoid difficulties inherent in calculating or maximizing the likelihood function. Thus it is a desirable estimation method when the maximum likelihood approach encounters difficulties but the characteristic function has a tractable expression. The basic idea of the empirical characteristic function method is to match the characteristic function derived from the model and the empirical characteristic function obtained from data. Ideas are illustrated by using the methodology to estimate a diffusion model that includes a self-exciting jump component. A Monte Carlo study shows that the finite sample performance of the proposed procedure offers an improvement over a GMM procedure. An application using over 72 years of DJIA daily returns reveals evidence of jump clustering.
Publisher:
ISBN:
Category :
Languages : en
Pages : 39
Book Description
This paper reviews the method of model-fitting via the empirical characteristic function. The advantage of using this procedure is that one can avoid difficulties inherent in calculating or maximizing the likelihood function. Thus it is a desirable estimation method when the maximum likelihood approach encounters difficulties but the characteristic function has a tractable expression. The basic idea of the empirical characteristic function method is to match the characteristic function derived from the model and the empirical characteristic function obtained from data. Ideas are illustrated by using the methodology to estimate a diffusion model that includes a self-exciting jump component. A Monte Carlo study shows that the finite sample performance of the proposed procedure offers an improvement over a GMM procedure. An application using over 72 years of DJIA daily returns reveals evidence of jump clustering.
Simulation of Estimates Using the Empirical Characteristic Function
Author: Dilip Madan
Publisher:
ISBN: 9780868370507
Category : Parameter estimation
Languages : en
Pages : 18
Book Description
Publisher:
ISBN: 9780868370507
Category : Parameter estimation
Languages : en
Pages : 18
Book Description
Parametric Estimation Through the Use of the Empirical Characteristic Function
Author: Ronald Glenn Thomas
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 248
Book Description
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 248
Book Description
Selected Topics in Characteristic Functions
Author: Nikolaĭ Georgievich Ushakov
Publisher: Walter de Gruyter
ISBN: 9789067643078
Category : Mathematics
Languages : en
Pages : 376
Book Description
The series is devoted to the publication of high-level monographs and surveys which cover the whole spectrum of probability and statistics. The books of the series are addressed to both experts and advanced students.
Publisher: Walter de Gruyter
ISBN: 9789067643078
Category : Mathematics
Languages : en
Pages : 376
Book Description
The series is devoted to the publication of high-level monographs and surveys which cover the whole spectrum of probability and statistics. The books of the series are addressed to both experts and advanced students.
Empirical Characteristic Function in Time Series Estimation
Author: John Knight
Publisher:
ISBN:
Category :
Languages : en
Pages : 41
Book Description
Since the empirical characteristic function (ECF) is the Fourier transform of the empirical distribution function, it retains all the information in the sample but can overcome difficulties arising from the likelihood. This paper discusses an estimation method via the ECF for strictly stationary processes. Under some regularity conditions, the resulting estimators are shown to be consistent and asymptotically normal. The method is applied to estimate the stable ARMA models. For the general stable ARMA model for which the maximum likelihood approach is not feasible, Monte Carlo evidence shows that the ECF method is a viable estimation method for all the parameters of interest. For the Gaussian ARMA model, a particular stable ARMA model, the optimal weight functions and estimating equations are given. Monte Carlo studies highlight the finite sample performances of the ECF method relative to the exact and conditional maximum likelihood methods.
Publisher:
ISBN:
Category :
Languages : en
Pages : 41
Book Description
Since the empirical characteristic function (ECF) is the Fourier transform of the empirical distribution function, it retains all the information in the sample but can overcome difficulties arising from the likelihood. This paper discusses an estimation method via the ECF for strictly stationary processes. Under some regularity conditions, the resulting estimators are shown to be consistent and asymptotically normal. The method is applied to estimate the stable ARMA models. For the general stable ARMA model for which the maximum likelihood approach is not feasible, Monte Carlo evidence shows that the ECF method is a viable estimation method for all the parameters of interest. For the Gaussian ARMA model, a particular stable ARMA model, the optimal weight functions and estimating equations are given. Monte Carlo studies highlight the finite sample performances of the ECF method relative to the exact and conditional maximum likelihood methods.
Continuous Empirical Characteristic Function Estimation of Mixtures of Normal Parameters
Efficient Estimation Using the Characteristic Function
Empirical characteristic functions-based estimation and distance correlation for locally stationary processes
Author: Carsten Jentsch
Publisher:
ISBN:
Category :
Languages : de
Pages :
Book Description
In this paper, we propose a kernel-type estimator for the local characteristic function of locally stationary processes. Under weak moment conditions, we prove joint asymptotic normality for local empirical characteristic functions. For time-varying linear processes, we establish a central limit theorem under the assumption of finite absolute first moments of the process. Additionally, we prove weak convergence of the local empirical characteristic process. We apply our asymptotic results to parameter estimation. Furthermore, by extending the notion of distance correlation of Szekely, Rizzo and Bakirov (2007) to locally stationary processes, we are able to provide asymptotic theory for local empirical distance correlations. Finally, we provide a simulation study on minimum distance estimation for a-stable distributions and illustrate the pairwise dependence structure over time of log returns of German stock prices via local empirical distance correlations.
Publisher:
ISBN:
Category :
Languages : de
Pages :
Book Description
In this paper, we propose a kernel-type estimator for the local characteristic function of locally stationary processes. Under weak moment conditions, we prove joint asymptotic normality for local empirical characteristic functions. For time-varying linear processes, we establish a central limit theorem under the assumption of finite absolute first moments of the process. Additionally, we prove weak convergence of the local empirical characteristic process. We apply our asymptotic results to parameter estimation. Furthermore, by extending the notion of distance correlation of Szekely, Rizzo and Bakirov (2007) to locally stationary processes, we are able to provide asymptotic theory for local empirical distance correlations. Finally, we provide a simulation study on minimum distance estimation for a-stable distributions and illustrate the pairwise dependence structure over time of log returns of German stock prices via local empirical distance correlations.
Density Estimation Through Kernal Estimation-based Empirical Characteristic Function
Author: Mawia Bakri Kaddoura
Publisher:
ISBN:
Category : Characteristic functions
Languages : en
Pages : 162
Book Description
Publisher:
ISBN:
Category : Characteristic functions
Languages : en
Pages : 162
Book Description