@kevon217
Great dive into and demonstration of random numbers and coding with them. Appreciate it!
@АнатолийБугаков-е9г
excellent video
@i_bench_225
I'll try implementing this in my equity curve simulation
@zebmason6530
Very interesting. Got me worried for a minute until I remembered that at the start of the year I was using uniformly distributed random numbers in a Fisher-Yates shuffle (for an epidemic simulation).
@arcade-fighter
Congrats for the video!! This topic is really unknown inthe quant community. Myself I used to apply QMC on my Master Thesis, via Halton, Sobol sequences and others as you mentioned here. With QMC we can push forward the "curse of dimensionality" to converge faster than raw MC. In my case I did a lot of experiments to support the QMC goodness thesis, working out valuations of exotic options (with no easy analytical solution) such as Spread and Lookback Stock Options.
@kevinshen3221
never really know the random generator can fall short! thanks for this vid
@Alexander-pk1tu
thank you for your very informative video
@TimoFriedl
Thanks a lot for this high quality video
@kilocesar
Very good
@var7397
Thanks from Ukraine! You inspired me 🙂
@davidetrevi3918
Around the end of the video you defined the relative error as the difference between the approximation and the exact BS formula. Shouldn't you divide the exact value to get relative errors?
@maxhohenstein4554
How would you recommend a newby to learn python ?
@ghostwhowalks5623
this is great! How would you sample from Halton repeatedly and get different numbers? For eg in Matlab, I can change randn(i, 1:5) and loop through i. Not sure how to do it for the Halton sequence....
@marcoesteves4367
I tried this approach in R with quasi random numbers but got put and calls values very far from market values.
@Drewww71
In my humble opinion Quasi numbers are not truly random if they are remembering the previous sequences therefore lead to bias
@QuantPy