The most underrated channel, There are very few channels who are making videos on these topics. But this channel is the best, after watching one video I hit subscribe button without any second thought. Hope you reach a million subscribers soon.
Im sorry I have a maybe dumb question. I thought the moment generating function is t or θ dependent. So in 3:18 what you calculate is just a constant (or θ=1). Why do you use that one to substitute the variance of the brownian which is not constant ( var = t or θ). ? It confuses me a little bit and I would love a clarification! thanks
Many thanks! And glad you liked the playlist! We have tried to order the material in conceptual terms- e.g., to understand GBM, which is a transformation of the brownian, one needs to understand stochastic integration and for that reason Ito’s lemma and quadratic variation. But ordering is never easy considering diversity of backgrounds. E.g., if one is happy to make assumptions such as dW^2=dt, then the GBM and ito’s product rules videos should be before the stochastic integration. The introduction to stochastic process video was supposed to be the first video in the playlist, but then we had to move it to the end because the feedback suggested the video is quite dense!
Excuse me but in 09:28, when we derivative both of the equation, why it doesn't use quotient rule? I mean dy = ( 2t*dB - B^2*2 ) / (2t)^(2). But it just get the derivative only the variable 'B'
These videos are brilliant, a very clear step by step break down.
thank you!!
Excellent videos, especially for newer ones with your natural voices.
Thank you!
I think there's a slight error in 2:02. The last line is lacking the factorial term for the constant in front of E[z^4]. It should be θ^2/2! not θ^2.
The most underrated channel, There are very few channels who are making videos on these topics. But this channel is the best, after watching one video I hit subscribe button without any second thought. Hope you reach a million subscribers soon.
thank you!!
Im sorry I have a maybe dumb question. I thought the moment generating function is t or θ dependent. So in 3:18 what you calculate is just a constant (or θ=1). Why do you use that one to substitute the variance of the brownian which is not constant ( var = t or θ). ? It confuses me a little bit and I would love a clarification! thanks
Amazing work with these videos!
感谢，对于非数学背景的学生是很好的教学。
Hello guys. Are the videos in the list ordered? Could you order them? Legedary courses! Love from Barcelona
Many thanks! And glad you liked the playlist! We have tried to order the material in conceptual terms- e.g., to understand GBM, which is a transformation of the brownian, one needs to understand stochastic integration and for that reason Ito’s lemma and quadratic variation. But ordering is never easy considering diversity of backgrounds. E.g., if one is happy to make assumptions such as dW^2=dt, then the GBM and ito’s product rules videos should be before the stochastic integration. The introduction to stochastic process video was supposed to be the first video in the playlist, but then we had to move it to the end because the feedback suggested the video is quite dense!
@@quantpie It says some videos are missing, can you check pls
7
Excuse me but in 09:28, when we derivative both of the equation, why it doesn't use quotient rule? I mean dy = ( 2t*dB - B^2*2 ) / (2t)^(2). But it just get the derivative only the variable 'B'
Many thanks and sorry for the slow response! We are considering distribution at a given horizon- t is treated as a constant.
Excellent 😍 I just loved it
Thank you! Cheers!
really helpful Thanks
You're welcome! thank you!!
Wiener did some hard work there :)
haha a lot of mo(ve)ments!!