Evaluation of Various Numerical Methods for Option Pricing Model

Looking at the values for resonance frequency we got from both methods, at first glance it appears that the second method is more accurate. However, in order to better compare the two methods, we can calculate the percent error from the theoretical value for the resonant frequency. The formula for percent error is:

The different measurement had siginificant differences between them. I think the the changing frequency method gave better results because the graph was more consistent.

The advantages of this method are that it is accurate, specific, and fairly easy to perform (Randolph, 1975). One

Suggestion of a possible improvement and explanation of how this will improve the accuracy or precision of the

Businesses rely on numerical models while choosing a project. Most businesses see numerical models more useful than non-numerical models which are highly biased and unempirical.

• Sometimes the best thing to do for your method is to draw a diagram of the experimental set up and refer to it. That’s

The series form in which the methodologies were implemented and result achieved using them are as follows.

The method validation results for the 3-MCPD analytical method were evaluated as follow. The LODs of 3-MCPD in various matrices ranged from 4.18 to 10.56 ng g-1. Moreover, the accuracy and precision were found to be ~90.38–122.46%, ~1.89–25.22% relative standard deviation (RSD), respectively. In contrast, the LODs of 1,3-DCP following matrices were found to be the ~1.06–3.15 ng g-1. The accuracy and precision were determined as ~91.24–113.40 and ~1.42–10.58% RSD. Meanwhile, all the3-MCPD and 1,3-DCP calibration curves in diverse matrices have higher linearity than 0.99.

ways, does the model agree with existing data, can the model be used to make physical predictions, what

Monte Carlo simulation approach is the most powerful and flexible approach which involves assuming a particular distribution specified by the user, using computer software to draw random samples from the distribution and generating an enormous amount of outcomes. The selected outcomes will naturally form a distribution, which will approximate the normal distribution. VaR is calculated in the same way as with the delta-normal approach by using the expected mean, and volatility generated by Monte Carlo approach (Kaplan, Inc., 2014).

INTRODUCTION ...............................................................................1 1.1 Background of study .................................................................................2 1.2 Problem Identification ..............................................................................3 1.3 Scope of Study ...........................................................................................4 1.4 Objective ....................................................................................................4 1.5 Benefits .......................................................................................................5 1.6 Theoretical Framework and Hypothesis .................................................6 1.7 Systems of Writing ....................................................................................7

approximations used, inversion method explanation and other parameters explained using example of aki richards approximation

In this chapter, we have discussed about the analysis and methodologies of the existing problem.

In this chapter, we have discussed about the analysis and methodologies of the existing problem.

Finally, Rosenstein’s method has been adopted in considerably greater cases (82 cases, 79%) compared to Wolf’s methods (16 cases, 15%). 6 studies (6%) also did not specify the algorithm used.