@tonyjones4451
this guy saved my degree... was averaging a 2:2 and now im getting first! (: so happy!!
@mustafizurrahman5699
One of the best video on Brownian motion. Such a lucid explanation
@chocolatemodelsofficial5859
OMG this is the information I wanted to know, you may have just changed my life. I was trying to understand why the normal standard deviation has the bell curve shape. And now thanks to you, I now know that the normal standard deviation shape is the projection of multiple scaled random walks.
@DanielloFratello
Hooooly shit, you are so good at explaining these topics, which apparently don't have to be so difficult compared to how my professor explains it.
@hyuming2577
nice geometric brownian motion lecture
@skeetski2307
College prolly masters level made simple, and available for us to try to learn. appreciate you
@infianant
You have explained it brilliantly…. Looking forward for other videos as well …keep uploading
@ibtissamaymen7321
Thank you for your vedios, i have question please, why you use .T and what is T??
@meisterthea
At 8:40 into the video you mention 10,000,000 simulations but it is not clear how you did this. Please explain.
@yassinejermouni3224
the best channel ever thxx man
@mobileentertainment212
Are there any pre-req video i can watch? really could not catch any of it. For example, what filtration means
@user-wr4yl7tx3w
Excellent video
@mundrakeshav22
I am currently pursuing the FRM (cleared FRM Level 1) and also learning Python! Which books would you suggest me to refer for building stronger basics??
@ghostwhowalks5623
fantastic video!! Quick question - what does it mean to say "variance accumulates at rate one per unit time"? Thanks!
@sergeychigrinov1360
Hi @Jonathon! Why we use square root of time when we switch to the scaled random walk? From what I see increments are additive, so to get to the same position at time 1 we need to do NxSize steps and not sqrt(N)xSize steps. I gues this is to keep properties of the process the same, but this is not too intuitive. If the answer is long - please steer me into the right direction. Thanks!
@parsecsprinter904
How can you use the normal distribution in your example for Brownian Motion with n = 100 steps and time t = 10 when "n" is not at all high enough to assume a normal distribution? It should have been a Binomial distribution instead. No? The binomial won't converge to normal at just small n. Law of Large Numbers.
@simonabarone8920
Can you simulate multidimensional Brownian motion with the correllation matrix?
@iv2689
Any book recommendations for a beginners in financial mathematics (Cfa candidate)?
@skalderman
How is it random if its dependent on anything?
@lucascooky8242