Journal article Open Access
This paper provides a New Log-Likelihood Estimator (NLLE) function as a tool for value approximation. We improved the accuracy of the log MLE in two steps (i) determine the log likelihood of a random variable X, and (ii) adjust the estimate by a factor of . In-Sample testing was accomplished by using daily SET100 indices over a period of 60 days. Out-of-sample data were used for confirmatory verification; out-of-sample data came from 5 major stock markets: NASDAQ, DOW, SP500, DAX, and CAC40. Relevant tests used to compare the results of the proposed NLLE include Cramer-Rao Lower Bound (CRLB), Likelihood Ratio Test, Wald statistic, and Lagrange Multiplier (Score Statistic). It was found that NLLE is more efficient than the conventional MLE. It gives practitioners a better tool for value estimation in many fields of natural and social sciences.
Name | Size | |
---|---|---|
ARTICLE 3, Vol 1, No 2, New Log Likelihood Estimation Function.pdf
md5:60023cfba70d43dd319df80cd89a6465 |
133.6 kB | Download |
All versions | This version | |
---|---|---|
Views | 12 | 10 |
Downloads | 15 | 10 |
Data volume | 1.7 MB | 1.3 MB |
Unique views | 10 | 10 |
Unique downloads | 10 | 9 |