it's taking into account current option prices.... what is there is no current options price, how do you get IV for Options 0 An .Exciting read is Option Vol and pricing.
WOW , I also just happen to learn this in my computer science class too! Thanks again QUANT GUILD for blessing our ears with this amazing content after a few month long break!!? This content is so iccccy MY MAN, keep pumping out this amazing content. Ur also look built like a Greek god!?!?
Hey, great video as always, been following you for a while now, Your videos on Black Scholes PDE derivation are very useful Your explanation style is simple to understand. Keep it up.👊
at around 20 mins I think you make a mistake. It is a technicality, but E [T_3 | X_2 =1 ] = pD + (1-p) 0 + E[T_2] , and so simply adding your 2 eqns together gives what you need. and then you dont have to add in the E[T_2] as that is baked into the conditional expectation where X_2 = 1. I think the mistake happens because you are treating T_n as two types of variable -- both cumulative payout, as well incremental pay out on toss number n. imagine you would have to introduce some third variable for incremental payouts to avoid this mistake
Hi! Great video, thanks very much for it! Just one correction. There seems to be an error: at 8' 30'', the sign in front of "sigma" should be positive (and so it should be in the following equations).
Man! I've been in the Python/Volatlity/HedgeFund space since '17, and from then to now, I think this has been my favorite explanation of implied volatility, Theo vs Market Price I've ever seen. So crystalized and simplistic, but without ignoring structural concepts. BRAVO!
Covers each key element in a clear, accessible conversation. As always. Especially grateful you brought the yield curve discussion in early, since it's clutch and not so easily understood. Thanks for this.
In all seriousness it depends on your position typically applied math (stochal, processes, etc. for pricing which means stats Calc I-IV linear algebra etc.)
You Rock Sir, period. I've gone through a markov chain lecture series previously, lengthy and broad, but your vids are quick and focused real-life end-2-end examples that truly put flesh on the bones. Keep it up, it's hugely helpful.
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Happy you enjoy the videos! I have quite enjoyed this one: dl.icdst.org/pdfs/files3/b9549cae1d6114c8de97f9f33f3adfe4.pdf (11th edition Intro to Probability Models)
Not a dumb question, this is hard stuff - Black-Scholes implies flat or constant volatility for all prices. What you see is a “market” implied volatility surface, that means each point represents the volatility required to achieve the given market price in the blackscholes equation.
Am I the only one who finds Brownian motion simulations as a bit finicky. I find regulators love this stuff for sensitivity analysis, but i find these sorts of simulations have a hole. My inner Nassim Taleb tells be its BS. Im more comfortable with something like Black-Litterman where you can impose some "expert opinion". What are yalls thoughts?
@QuantGuild wouldn't your parameterization (mean and var) be the bias? So by setting the mean to 5% and vol to 2%, we've already created the outcome that the simulation will confirm? Or am I missing something fundamental about how it's used in the real world?
@@Septumsempra8818 It depends on your parameterization and model. Typically parameters are fit to market surfaces to extrapolate prices - this is what I am referring to. If I were to say "mean is 5% and vol is 2%" arbitrarily then sure its biased.
