Maximum Likelihood Estimation - the Ornstein-Uhlenbeck Process(part 2)
In part one, we looked at the method of maximum likelihood estimation in the context of the normal distribution. Keeping with the theme of this channel, we also showed how to calculate these parameters numerically if one cannot derive an analytical solution. In the video, we look at using maximum likelihood estimation for extracting the parameters where our random variable follows an Ornstein-Uhlenbeck process. We will look at some simulated data and extract the parameters numerically. Since these videos will eventually lead to parameter analysis for mean-reverting pairs trades, we will also compare our answers with the results given analytically in the book, Optimal Mean Reverting Trading mentioned in the description of part one.
Tip Jar: https://paypal.me/kpmooney
-
1:03
Oldfarm
2 years agoBaling With The Massey Ferguson Model #12
216 -
15:37
kpmooney
2 years agoPairs Trading: The Ornstein-Uhlenbeck Process and Pairs Ration Determination
25 -
1:21
Tuxedo Den
2 years agoOur Fitting Process
21 -
12:54
kpmooney
3 years agoKernel Density Estimation with Python: Estimate a Density Function from Data
14 -
9:22
kpmooney
3 years agoIntro to Monte Carlo Techniques: Using Python and Random Numbers to Estimate the Value of Pi
79 -
20:40
AndreJMcClendon
11 months agoZigZag, Stochastic and Bollinger Bands Expert Advisor for MQL4 / MT4
67 -
1:37
BarrysAutoBody
3 years ago2 Hour Course on automotive estimate process
38 -
10:28
Anarchics
1 year agoAn Approximation of Perpetual Motion PART 3 MIT​ DanFrey​ OXFORDUNIVERSITY​ ChiaraMarletto
13 -
1:13:04
The Great American Reset
1 year agoAnd Ode to the Prussian Pickle, Part V
274 -
23:14
kpmooney
3 years agoMore on Regressions and Parameter Estimation in Python
13