Видео

In the last chapter when we formed the model, shouldn’t the right side be y_i instead of y_j and i=1,2,3,...,n ( not m 0) ?

Respected Sir, Can you please suggest a good book for this topic

Girsanov instead of Girsanav

You're the best professor ever. I am super satisfied.

30:30 There are n*n elements in the variance-covariance matrix, where n of them are variances and the remainders are covariances.

JOYDEEP!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

At 09:24, how is E[YZ] = 0 ?

Thank you so much sir for making this hard subject very easy for us students. ❤

@14:23, where did the term 2 * sigma1 * sigma 2 come from ??

@ 25:03...Goodness me, where did that matrix vector form pop out from ??

How did you came up with that Utility Function. This ruins the whole hard work of patiently watching and making notes from the first lecture.

One part of the proof of Girsanov that still bothers me a bit is why the integral of theta(t) is continuous. How to give a strict proof of this? Sure, if the integrand is continuous, it follows, but how do we know for sure it does not have something like a dirac point mass at some time? I guess this follows from the adaptivity to Brownian motion somehow? Or maybe I am just stupid and don't see something trivial here..

Excellent clarity and presentation . on KKT

Excellent explanation sir…

dw(t)*dw(t)=dt ??

thanks for the great video! 8:00 shouldn't rho_12 = 1?🙂

Thank you for lecture...but could implement in real cases or make it Ms. Excell

Correction: Total Return = (X1/X0 ) - 1

Great lecture

Slide half dukh rha hai

1st one to know how i am really thankful to you that someone speaking on this Great day

Thank you 😊 prof

Than you very helpful.

Sir kindly remove the black stripe as we are unable to see what is written specifically in the lower part of the board Or introduce smthing to remove it manually

Thanks for your efforts sir

The phenomenon of Income Security is quite a complex thing for me to understand. This course has provided me with a great step by step guide to understand the concepts. skillscourses.org/Fixed-Income-Securities.html

Took to the web to find the constraint qualifications of the KKT method and how to use them, to watch a 30 minute video to have another professor encourage me to take to the web lol Great overview though!

link to the 1st part : ruclips.net/video/732bzOEhQpM/видео.html

The black scholes equation derivation through stochastic calculus starts by assuming a hedged portfolio where in a call option with strike price K is sold and equivalent delta shares are bought. Since the portfolio is hedged, it provides risk free return of R. Thereafter, partial differentiation is applied w.r.t S. Here you are following a completely different approach.

R used as discount factor considers R to be a risk free rate. Being risk free, R has zero volatility and hence no Brownian motion component. Therefore, it is better to use the constant R as such on exponential function as discount factor, rather than using stochastic calculus of R w.r.t T.

The origin of the utility function is not clear. The assumption of std deviation of return from shares to be 1 means the returns follow standard normal distribution. In such case, the expected returns need to be zero and not 1,2 ,3. Most of the time , the presenter's body blocks the board , so nothing is visible.

Very good and clear mathematical presentation. In real life the presence of duration risk makes long period spot rates higher thus making the yield curve upward sloping most of the time. So the forward rates are balancing figs to make that equation equal or it is other way round ?

3:30 , how did you come up with that utility function?

Thank you professor!

Well presented. Crystal clear

Heartly thanks☺️☺️

this is helped thank you

Can someone take a better care of those subtitles, or rather erase them ..l was "fainted " seeing "affine function " has become "a faint function " in the subtitle..

I don't need pen and paper to understand the concept taught by u . Could u plese help me in exploring the subsequent lectures?

Really incredible sir !

Totally confusing

Great talk!

give him a big board to write.

writting is so elegant!!!!

Thank you, professor!!! I have been studying Shrieve's books, and your lectures help me a lot!!! I will not take your efforts in vain. I will be a great quant in near future!

that handwriting tho...

They are all Indians man

it was a bouncer

Can you explain me why lambda_i g_i(x_0) = 0 means either lambda_i = 0 or g_i(x_0) = 0 , the fact that it's not possible to have lambda_i = 0 and g_i(x_0) = 0 ? Thanks

He is one of the best professor in the world