A multivariate preconditioned conjugate gradient approach for maximum likelihood estimation in vector long memory processes

2009 ◽  
Vol 79 (9) ◽  
pp. 1282-1289 ◽  
Author(s):  
Jeffrey Pai ◽  
Nalini Ravishanker
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Conjugate Gradient ◽  
Long Memory ◽  
Likelihood Estimation ◽  
Preconditioned Conjugate Gradient ◽  
Memory Processes ◽  
Long Memory Processes ◽  
Gradient Approach

scholarly journals Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data—Under Conditional Heteroskedasticity Framework

2019 ◽  
Vol 12 (2) ◽  
pp. 74
Author(s):  
A. M. M. Shahiduzzaman Quoreshi ◽  
Reaz Uddin ◽  
Naushad Mamode Khan
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Long Memory ◽  
Likelihood Estimation ◽  
Monte Carlo Experiment ◽  
Empirical Estimation ◽  
Transaction Data ◽  
Quasi Maximum Likelihood ◽  
Stock Transaction ◽  
The Impact

This paper introduces Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data of unknown underlying distribution. The moments with conditional heteroscedasticity have been discussed. In a Monte Carlo experiment, it was found that the QML estimator performs as well as CLS and FGLS in terms of eliminating serial correlations, but the estimator can be sensitive to start value. Hence, two-stage QML has been suggested. In empirical estimation on two stock transaction data for Ericsson and AstraZeneca, the 2SQML turns out relatively more efficient than CLS and FGLS. The empirical results suggest that both of the series have long memory properties that imply that the impact of macroeconomic news or rumors in one point of time has a persistence impact on future transactions.

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