scholarly journals Local asymptotic mixed normality of approximate maximum likelihood estimator of drift parameters in diffusion model

2017 ◽  
Vol 52 (2) ◽  
pp. 377-410
Author(s):  
Snježana Lubura Strunjak ◽  
◽  
Miljenko Huzak ◽  
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Diffusion Model ◽  
Likelihood Estimator ◽  
Approximate Maximum Likelihood ◽  
Asymptotic Mixed Normality ◽  
Approximate Maximum Likelihood Estimator

scholarly journals Credit Risk: Simple Closed-Form Approximate Maximum Likelihood Estimator

2020 ◽  
Author(s):  
Anand Deo ◽  
Sandeep Juneja
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Credit Risk ◽  
Closed Form ◽  
Maximum Likelihood Estimator ◽  
Likelihood Estimator ◽  
Approximate Maximum Likelihood ◽  
Approximate Maximum Likelihood Estimator

Interpretable, Computationally Tractable Approximate Parameter Estimation for Corporate Defaults

INTRODUCING AN INTERPOLATION METHOD TO EFFICIENTLY IMPLEMENT AN APPROXIMATE MAXIMUM LIKELIHOOD ESTIMATOR FOR THE HURST EXPONENT

Fractals ◽  
2015 ◽  
Vol 23 (04) ◽  
pp. 1550045 ◽  
Author(s):  
YEN-CHING CHANG
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Hurst Exponent ◽  
Interpolation Method ◽  
Computational Cost ◽  
Likelihood Estimator ◽  
Unknown Variance ◽  
Power Spectral ◽  
Levinson Algorithm ◽  
Approximate Maximum Likelihood Estimator

The efficiency and accuracy of estimating the Hurst exponent have been two inevitable considerations. Recently, an efficient implementation of the maximum likelihood estimator (MLE) (simply called the fast MLE) for the Hurst exponent was proposed based on a combination of the Levinson algorithm and Cholesky decomposition, and furthermore the fast MLE has also considered all four possible cases, including known mean, unknown mean, known variance, and unknown variance. In this paper, four cases of an approximate MLE (AMLE) were obtained based on two approximations of the logarithmic determinant and the inverse of a covariance matrix. The computational cost of the AMLE is much lower than that of the MLE, but a little higher than that of the fast MLE. To raise the computational efficiency of the proposed AMLE, a required power spectral density (PSD) was indirectly calculated by interpolating two suitable PSDs chosen from a set of established PSDs. Experimental results show that the AMLE through interpolation (simply called the interpolating AMLE) can speed up computation. The computational speed of the interpolating AMLE is on average over 24 times quicker than that of the fast MLE while remaining the accuracy very close to that of the MLE or the fast MLE.

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