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

[@aidenshen8343]

The professor recommends the video to us! Thanks!

[@quantpie]

it is very kind of them! You're welcome!

[@abigail-sothoth362]

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

[@quantpie]

You're very welcome! thank you!

[@thiminhthinguyen6414]

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-bt5jb]

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

[@quantpie]

You’ re welcome! Thank you!

[@hbbexxter4666]

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

[@quantpie]

Many thanks for the kind words!!

[@milinds4161]

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

[@quantpie]

Thank you!!

[@StratosFair]

Wonderful explanation of Girsanov's theorem

[@bramgriffioen5436]

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

[@adokoka]

Clean explanation!

[@quantpie]

Glad it was helpful! many thanks!

[@uminhtetoo]

Thank you so much for sharing, Sir.

[@War4Skills]

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 :)

[@vitalimueller6209]

You have great content, well done

[@quantpie]

Thank you so much 👍

[@sheldonjallen]

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

[@quantpie]

thank you!

[@yanisbarillon3597]

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

[@devyash313]

Very intuitive explanation. Thank you!

[@quantpie]

Glad it was helpful!

[@jxliu6375]

Love the video! Thanks!

[@quantpie]

Glad you enjoyed it! You're welcome!

[@abhinavsaxena2388]

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

[@quantpie]

thanks! Just replied to your other comment, apologies for the slow response!

[@user-wc7em8kf9d]

Amazing explanation! Thank you so much for this.

[@quantpie]

thanks! You're very welcome!

[@mariuslotz8896]

Thanks, easy to understand

[@curisity]

Amazing explanation !

[@quantpie]

Thank you!!

[@williamqiu1917]

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.

[@yanisbarillon3597]

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)

[@peterpaton7817]

The independence between the brownian increments (the change in the process between these times)

[@qiguosun129]

Great ! thanks!

[@ajith009100]

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) ?

[@Beacher1085]

Check at 18:40, bottom right-hand side. You know μ from previous step. (He's applying Nadon-Rikodym derivative. en.m.wikipedia.org/wiki/Girsanov_theorem)

[@quantpie]

thanks!

[@ROni_ROmio]

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

[@quantpie]

thanks! it is on the to do list!

[@fabioniski3316]

Yes a video on the FMM from the quantpie would be a huge hit.

[@NA-rq5dw]

Should it not be -2.5dt?

[@quantpie]

Whereabout please? dW has drift zero under P, drift of -2.5 under Q. The tilde version has drift zero under Q, so under Q you will to add 2.5 to dW to get the tilde version. Does that answer your question?

[@danielmcdouall4056]

@@quantpie I think they mean the dt coefficient for the dWt tilde. The gradient is downward sloping so should the vale not be negative?

[@danielmcdouall4056]

value*