The Slow Convergence of Ordinary Least Squares Estimators of α , β and Portfolio Weights under Long‐Memory Stochastic Volatility

2019 ◽  
Vol 40 (4) ◽  
pp. 590-608 ◽  
Author(s):  
Jun Liu ◽  
Rohit Deo ◽  
Clifford Hurvich
Keyword(s):  
Least Squares ◽  
Stochastic Volatility ◽  
Long Memory ◽  
Ordinary Least Squares ◽  
Slow Convergence ◽  
Least Squares Estimators

scholarly journals Parameter Estimation for Long-Memory Stochastic Volatility at Discrete Observation

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Xiaohui Wang ◽  
Weiguo Zhang
Keyword(s):  
Least Squares ◽  
Stochastic Volatility ◽  
Long Memory ◽  
Least Squares Method ◽  
Ordinary Least Squares ◽  
Discrete Observations ◽  
Least Squares Estimators ◽  
And Diffusion ◽  
Ordinary Least Squares Method ◽  
Explicit Estimators

Ordinary least squares estimators of variogram parameters in long-memory stochastic volatility are studied in this paper. We use the discrete observations for practical purposes under the assumption that the Hurst parameterH∈(1/2,1)is known. Based on the ordinary least squares method, we obtain both the explicit estimators for drift and diffusion by minimizing the distance function between the variogram and the data periodogram. Furthermore, the resulting estimators are shown to be consistent and to have the asymptotic normality. Numerical examples are also presented to illustrate the performance of our method.

WAVELET ESTIMATORS FOR LONG MEMORY IN STOCK MARKETS

2009 ◽  
Vol 12 (03) ◽  
pp. 297-317 ◽  
Author(s):  
ANOUAR BEN MABROUK ◽  
HEDI KORTAS ◽  
SAMIR BEN AMMOU
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Stock Returns ◽  
Long Memory ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Estimation Accuracy ◽  
Approximate Maximum Likelihood ◽  
Approximate Maximum Likelihood Estimator ◽  
Wavelet Estimators

In this paper, fractional integrating dynamics in the return and the volatility series of stock market indices are investigated. The investigation is conducted using wavelet ordinary least squares, wavelet weighted least squares and the approximate Maximum Likelihood estimator. It is shown that the long memory property in stock returns is approximately associated with emerging markets rather than developed ones while strong evidence of long range dependence is found for all volatility series. The relevance of the wavelet-based estimators, especially, the approximate Maximum Likelihood and the weighted least squares techniques is proved in terms of stability and estimation accuracy.

Sign in / Sign up

Export Citation Format

Share Document