@@raulbartolome8389 Not all heroes wear capes, thanks man! :)

YES KNOWLEDGE FOR ALL

Agreed

Especially considering a conceited self-appraisal half the lecture. The fuck I care about who the teacher is. The best teachers talk about the subject non-stop, not their credentials.

So what? He seems an intelligent engaging and sympathetic teacher.

🥲🤣🤣

44:40 cookie

Thank you. I'd be a lesser teacher if I had a PhD. My value-add proposition is to explain how things are actually done.

It seems to me that he can empower people and has a lot of knowledge. So the PhD is just a title not a necessarily proof of knowledge.

I'm glad you liked it.

hey oscar ,how ıs ıt goıng your beıng quant adventure?

I am well, thank you very much. I'm glad you enjoyed it.

Why do you ask?

Also FORTRAN & COBOL coder using cards. USAF required that I be able to read blank Hollerith Cards at 1 card per minute, Baudot paper table at 25 characters per minute. Am another of the ancient ones.

I'm happy to be of service.

Happy to be of use. Let me know if you have any questions.

Thanks. My wife thinks I'm just ok, though.

XD

Thanks

@@kennethabbott8248 No, thank you. Amazing lecture!!!

Thanks.

Same career

I'm happy to provide any help I can.

@@kennethabbott8248 hi Ken ı realy need help about my life ı have to ask questions How can ı connect to you

Thanks.

Krunal Patel Sorry, the course materials do not include any Excel spreadsheets... but the course lectures notes do have a number of data tables on the slides that might be of some help? See the course on MIT OpenCourseWare for the course materials at: ocw.mit.edu/18-S096F13

I'm glad you found it to be of use.

@Ken Abbott Haha, totally understand. Would you mind sending me your email? Would really appreciate having a contact like yourself for topics like this. Best!

abbottk (at) post (dot) harvard (dot) edu.

I'm glad it was useful for you.

Thanks. Quiero visitar El Salvador algún día.

My wife says I'm only "better than average". That's all I got after 25 years I guess. KA

@@kennethabbott8248 Hahaha after 25 years that's pretty damn good!!! I've only been able to make it to 5, and my fiancee dropped my course...(then me...)

The answer to that is usually cat videos. Where are your interests?

Thanks

This might be useful: kzfaq.info/get/bejne/h6mhpq2HuJ7UZXU.html

Thanks. I'm glad you liked it.

Let me know when you're in NYC. You can sit in.

​@@kennethabbott8248, Great lecture! definitely useful even not for quants (CS grad from Israel here)

@@kennethabbott8248 Sorry but at 52:15 that bit about Abe Lincoln and the Internet just fell completely flat 😂

@@HelloThisIsAnon Yeah. That one didn't go over.

I have spent so many years at several universities, I have never seen something like thatbefore!! I laughed out loud! That was genuinely funny!!!

Yes. That is correct.

@@kennethabbott8248 Thanks Ken. I wish there would be your 8-hour course! I learned a lot from your condensed lecture!

I do a whole lecture series every year at Baruch in the MFE program. I teach undergrads, as well. Come sit in!

@@kennethabbott8248 Thanks! And thanks for the great work you put out!

You are correct That is a known weakness and the reason most forms don't use MC for VaR. You can use a t-copula or make ex-post adjustments to calibrate the distribution. A gaussian copula makes more sense in doing Vasicek-type default models and counterparty risk.

Apparently, I cannot post links. I tried twice but they never showed up. I don't recommend any books, since they are usually out-of-date as soon as published. I do recommend the following papers/publications, though all availabel on the web: UK Actuaries Society, Illuminating the Black Box Canabarro and Duffie, Measuring and Marking Counterparty Risk Basel Committee on Banking Supervision, The Basel Framework The latter is 1800+ pages long. It's a difficult slog to read, but it's a useful reference.

The VaR will change every time you change the portfolio unless you manage to add to in in a manner 100% consistent with its current composition. Even then, it will change as market conditions change. Email me at abbottk (at) post (dot) harvard (dot) edu if you want to discuss further.

I'm glad you liked it.

@@kennethabbott8248 Mr. Abbott, where can I find related papers/journals from which I can read more about VAR, Portfolio, Options, Derivatives, etc related to finance? I believe that getting a good in-depth know-how of these things would be beneficial in the long run, atleast for me.

No. Leptokurtosis implies a higher probability of larger losses.

​@@kennethabbott8248 Doesn't that depend on how the fat tailed distribution look? The probability for a extremely large loss are indeed higher in a fat tailed distribution. But it can still be more likely for a 2,33 sigma loss in a normal. For example, 2 distributions, one normal and one fat tailed with the same mean and variance. Both have 1000 observations. In the normal it will be approx 10 events outside 2,33 sigma, but in the fat tailed it can in the extreme case be 1. If that observation is far enough out in the tail, like 15-20 sigma, then the distributions can still have the same variance. Thus the probability of a 2,33 sigma loss is smaller in the fat tailed. Or am I missing something?

@@moosecapital8886 Technically, of course, you are correct. The fact that the two cumulative distribution functions cross each other demands that they cross a second time since both of the associated density functions must integrate out to 1.0. In practice, in a diversified portfolio, the VaR at 99% and 95% are always higher for HS than for VCV or MC. (At least I have never seen any instances of an empirical VaR at these levels be less that that implied by the normal.) Ive seen the crossover between 80% and 95%, typically around 90%. My $0.02.

@@kennethabbott8248 That makes sense, thanks a lot for the reply Kenneth. Really enjoyed the lecture

Thanks. I clearly can't please everyone, but if I can make the material clearer for a few, I'm satisfied.

@Ken Abbott lol yeah totally understood! The proof is easy and will be leave as an exercise, said by professor

Send me your email. abbottk(at)post(dot)harvard(dot)edu