Simplified: Girsanov Theorem for Brownian Motion (Change of Probability Measure)
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- Опубликовано: 31 окт 2020
- 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.
The professor recommends the video to us! Thanks!
it is very kind of them! You're welcome!
The best video I have found on Girsanov Theorem. Thank you so much!
You're very welcome! thank you!
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.
So grateful for you and this channel. Thank you so much for your work
You’ re welcome! Thank you!
Thank you so much!! Really resourceful explanation, to get some insights into this abstract formula !
Wonderful explanation of Girsanov's theorem
Thank you so much for sharing, Sir.
Fantastic. Absolutely fantastic! You bring stochastic calculus to life and make it finally understandable for mortal people as well.
Many thanks for the kind words!!
Thanks, easy to understand
Clean explanation!
Glad it was helpful! many thanks!
That was awesome! Thank you for that patient, cogent explanation!
thank you!
Love the video! Thanks!
Glad you enjoyed it! You're welcome!
The very best intuitive explanation on the net. Thanks so much!
Thank you!!
Great ! thanks!
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 :)
Very intuitive explanation. Thank you!
Glad it was helpful!
Amazing explanation! Thank you so much for this.
thanks! You're very welcome!
You have great content, well done
Thank you so much 👍
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.
Amazing explanation !
Thank you!!
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
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)
The independence between the brownian increments (the change in the process between these times)
I am new to computational finance. With so many videos, suggest should be the first 5 topics to view ?
thanks! Just replied to your other comment, apologies for the slow response!
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) ?
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)
thanks!
Thanks bro, could you do video about le new FMM Foward Market Model and explain the changes VS LMM please,..? Many Thanks
thanks! it is on the to do list!
Yes a video on the FMM from the quantpie would be a huge hit.
Should it not be -2.5dt?
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?
@@quantpie I think they mean the dt coefficient for the dWt tilde. The gradient is downward sloping so should the vale not be negative?
value*