Maximum Likelihood Estimation - the Cauchy distribution (Excel)

How to estimate the parameters when the statistical moments of the empirical distribution are unreliable or meaningless? There is a powerful method - maximum likelihood estimation - that is frequently used in many areas of mathematics and statistics. Today we are investigating the maximum likelihood estimation and apply it to the case of the Cauchy stock return distribution.
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[@NEDLeducation]

You can find the spreadsheets for this video and some additional materials here: drive.google.com/drive/folders/1sP40IW0p0w5IETCgo464uhDFfdyR6rh7
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[@michaelzumpano7318]

That was very well done! Very easy to follow.

[@RustuYucel]

Very good tut! Keep going on like this! Thnx. Can we have it same for Laplace too?

[@NEDLeducation]

Hi Rustu and many thanks for your feedback! As for the Laplace distribution, the original video (ruclips.net/video/zR_liKniGOc/видео.html) already has the maximum likelihood parameter estimation. The catch is the following: if you solve the log-likelihood maximisation problem analytically, you will arrive at the solution that the location parameter m is equal to the sample median and the scale parameter b is equal to absolute average deviation.

[@Algoreborn]

Hi Saava,
I am new to your channel been learning really allot, Great work.
firstly wanted to get in touch with you if that's possible. secondly wanted to ask about the value which came up as the likely hood which was predicted 4263.23 was it next day or yearly ?