Simplified: Girsanov Theorem for Brownian Motion (Change of Probability Measure)

Explains the Girsanov’s Theorem for Brownian Motion using simple visuals. Starts with explaining the probability space of brownian motion paths, and once the probability measure is introduced, then shows how the change of probability measure looks like visually. The video ends with outlining the relationship between conditional expectation under the two measures.

[abigail-sothoth]

The best video I have found on Girsanov Theorem. Thank you so much!

[aidenshen]

The professor recommends the video to us! Thanks!

[thiminhthinguyen]

Your visualization is truly amazing. I have had a hard time constructing the probability measure of Brownian motion in my head and thanks to your explanation, it is clear to me now.

[AlexRodriguez-btjb]

So grateful for you and this channel. Thank you so much for your work

[hbbexxter]

Fantastic. Absolutely fantastic! You bring stochastic calculus to life and make it finally understandable for mortal people as well.

[milinds]

The very best intuitive explanation on the net. Thanks so much!

[bramgriffioen]

Thank you so much!! Really resourceful explanation, to get some insights into this abstract formula !

[StratosFair]

Wonderful explanation of Girsanov's theorem

[WarSkills]

Hi, thank you for the great video, it truly made me understand the concept of changing probability measures way easier. I never knew it was actually that straightforward! Is it possible to share your slides? I would like to take notes on them if you don't mind :)

[sheldonjallen]

That was awesome! Thank you for that patient, cogent explanation!

[williamqiu]

You mentioned in the video that we don't need to worry about the sigma algebra too much. But the problem always hugged me a little, can't the sigma algebra be too small for something we are describing or is there a theorem stating for any problem we can find a suitable sigma algebra.

[yanisbarillon]

At 18:46 you made a mistake on the sign of the term in t in the radon nikodyn dQ/dP density but really thank you good explanation

[devyash]

Very intuitive explanation. Thank you!

[abhinavsaxena]

I am new to computational finance. With so many videos, suggest should be the first 5 topics to view ?

[wcemkfd]

Amazing explanation! Thank you so much for this.

[ajith]

Hi, great content ! I lost a bit at 17:34, how did you get at the dQ/dP = exp(-2.5W -0.5*2.5*t^2) ?

[yanisbarillon]

First, thanks for your video it's quite clear to relate with practical and visual example. But around 7 min you said that the proba that the brownian pass through the 3 gates is the product because of independance but there is only independance of Wt2-Wt1 with Wt1 not of Wt2 with Wt1 isn't it ? (So my question is : Is there an error or am I missing something)

[ROni_ROmio]

Thanks bro, could you do video about le new FMM Foward Market Model and explain the changes VS LMM please, ..? Many Thanks