Financial Time Series Models

2010 ◽  
pp. 123-133
Author(s):  
Szymon Borak ◽  
Wolfgang Karl Härdle ◽  
Brenda López Cabrera
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Time Series Models ◽  
Financial Time

scholarly journals Asymptotic Optimality of Estimating Function Estimator for CHARN Model

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Tomoyuki Amano
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Financial Time Series ◽  
Asymptotic Optimality ◽  
Time Series Models ◽  
Estimating Function ◽  
Asymptotically Optimal ◽  
Financial Time ◽  
Conditional Least Squares ◽  
Better Than

CHARN model is a famous and important model in the finance, which includes many financial time series models and can be assumed as the return processes of assets. One of the most fundamental estimators for financial time series models is the conditional least squares (CL) estimator. However, recently, it was shown that the optimal estimating function estimator (G estimator) is better than CL estimator for some time series models in the sense of efficiency. In this paper, we examine efficiencies of CL and G estimators for CHARN model and derive the condition that G estimator is asymptotically optimal.

Financial Time Series Models

2012 ◽  
pp. 131-141
Author(s):  
Szymon Borak ◽  
Wolfgang Karl Härdle ◽  
Brenda López-Cabrera
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Time Series Models ◽  
Financial Time

On diagnostics in conditionally heteroskedastic time series models under elliptical distributions

2004 ◽  
Vol 41 (A) ◽  
pp. 393-405 ◽  
Author(s):  
Shuangzhe Liu
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Time Series Data ◽  
Financial Time Series ◽  
Local Influence ◽  
Series Data ◽  
Time Series Models ◽  
Elliptical Distributions ◽  
Financial Time ◽  
Likelihood Displacement

In statistical diagnostics and sensitivity analysis, the local influence method plays an important rôle. In the present paper, we use this method to study financial time series data and conditionally heteroskedastic models under elliptical distributions. We start with a likelihood displacement, and consider data- and model-perturbation schemes. We obtain corresponding matrices of derivatives, and measures of slope and normal curvature, and then discuss the assessment of local influence.

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