scholarly journals Gaussian Estimation of One-Factor Mean Reversion Processes

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Freddy H. Marín Sánchez ◽  
J. Sebastian Palacio
Keyword(s):  
Maximum Likelihood ◽  
Continuous Time ◽  
Simulated Data ◽  
Mean Reversion ◽  
Data Series ◽  
Continuous Time Model ◽  
Time Model ◽  
New Process ◽  
Gaussian Estimation ◽  
Maximum Likelihood Technique

We propose a new alternative method to estimate the parameters in one-factor mean reversion processes based on the maximum likelihood technique. This approach makes use of Euler-Maruyama scheme to approximate the continuous-time model and build a new process discretized. The closed formulas for the estimators are obtained. Using simulated data series, we compare the results obtained with the results published by other authors.

Open Higher Order Continuous-Time Dynamic Model with Mixed Stock and Flow Data and Derivatives of Exogenous Variables

1991 ◽  
Vol 7 (3) ◽  
pp. 404-408 ◽  
Author(s):  
K. Ben Nowman
Keyword(s):  
Continuous Time ◽  
Higher Order ◽  
Flow Data ◽  
Continuous Time Model ◽  
Exogenous Variables ◽  
Time Model ◽  
Time Dynamic ◽  
Mixed Stock ◽  
Gaussian Estimation ◽  
Derivatives Of

This paper is concerned with deriving formulae for higher order derivatives of exogenous variables for use in estimating the parameters of an open secondorder continuous time model with mixed stock and flow data and first and second order derivatives of exogenous variables which are not observable. This should provide the basis for the future estimation of continuous time models in a range of applied areas using the new Gaussian estimation computer program developed by Nowman [4].

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