@dafuqyobrotwelve
I do this for work and I wish things were explained this concisely and clearly at universities 👏 kudos to you, great job
@surajrlucas
i love how you explained the expectation of the payoff of the option
@adityasethia61
A couple things: 1) commissions, fees, and slippage will eat up almost all of your profits because of how the position is created on such small ticks. 2) still won't be able to beat the index but have a much better Sharpe ratio although trading is absolutely about the positive expectancy and that's how you remain in the game for a long which you covered well
@polyvon
That was awesome! YouTube algorithm popping off today :D
@manimaranpramoth1782
One of the best videos i have seen that explains GBM and Black-Scholes so simply in the real world context
@kacper3958p6p
There is one issue with this strategy though. BS model assumes some input IV but the IV is calculated by applying some secret model that is used by institutions to find the best estimate possible - and it's based on what the market agrees on: the price of an option. You can, theoretically, use such strategy but it requires a fine-crafted ML model for IV estimation. Without this best you can do is have a random guess. The fair option price is implicit and parametrized by IV = f(price), you cannot possibly expect to use a naive models and reconstruct f'(IV) = price just like this.
@Caller8194
these sorts of models/strats do not work for retail investors. the mispricing of options is arbed out nearly instantly by HFT firms. do you seriously think you can beat a team of 10+ mathematics PhDs with massive HF infrastructure to price out these trades. no way
@gustinapert
Great explanation of what a trading edge is with the Black-Scholes Model. One GIANT issue you didn't cover is making sure the edge is big enough that it would be worth using. With this edge it wouldn't even come close to beating the S&P. Granted it could possibly be with some tweaks, but for active trading or investing your money you really need it to be worth your time and energy. Otherwise just park it in an index.
@xijinpingthepresidentofchi1431
I have seen many vids on BSM, and believe me when I say this is the most detailed, practical, precise and among the best explanations.😇😀
@blueboyd5297
Good video. Understand that we are dealing with uncertainty modeling an approach that can yield a positive skew edge over a large sample size. This us a great channel. Ultimately you are looking for management techniques over a higher win rate. We can never hope to fully predict chaos.
@saadallah8987
Imagine that i got a project of option pricing by the model of black-sholes, and this video got updated 2 years ago. You may call it the ytb algo, i call it destiny hahahah thank you hero for this great content
@brockobama257
this guy didn't stutter, every word was meaningful, subbed
@astroganov
One of these 5 parameters of Black-Scholes model is sigma (i.e. volatility). So basically you should "know" the value of sigma to use Black-Scholes model. And you have to be sure that your sigma value is more accurate than volatility implied from market price of an option. But you can't be sure. You don't really know of market is overpriced or not.
@tanvirsethi6477
Great video. Amazing explanation
@Cosmicvendor
This is an interesting idea, and I'm glad I stumbled on this video as it's highly informative. For anyone who does watch this video however, you must understand that while this is an excellent example of what implementation COULD look like and does cover the premise of model trading in general for positive expectancy, YOU SHOULD NOT USE THIS AS A DIRECT TRADING STRATEGY! The Black Scholes model is a well understood and ubiquitous idea that is used by both quantitative focused actors and even retail traders. If it was as simple as looking at the difference between the expected output of the Black Scholes model and what the current market price is, this would immediately be arbitraged away if true alpha existed because of the ubiquity of this model. Likely this separation in price is due less in market forces (potential arbitrage opportunity) and more with market makers and other sophisticated actors using more comprehensive models to determine the appropriate price. The Black Scholes model is based on EUROPEAN options, this means the "price" is determined at the final end date. American options (what you're likely trading) are able to be exercised at any point in it's lifetime as long as it's above the strike price (call example). The Black Scholes model does not reflect the possibility of intra time frame upside or downside (probability of the option being profitable at ANY point in it's life time) only the end date. To do so would require something like a adjusted Black Scholes model with a pricing derived from every day until expiration and an integration taken over the entirety of these outputs to examine the potential continuous probability and determine a fair price. Perhaps even using path dependent modifications, a Monte Carlo simulation for St to allow for non constant volatility modeling (Black Scholes assumes to be constant (Volatility Clustering research has proven constant volatility to not be true)); or even better Instead of integrating probability distributions (as I originally used as an example) integrate expected option values at each time t, weighted by the probability distribution over St. All this to say, please get involved in quantitative endeavors, do not think it's as simple as it sounds.
@vaguthun
Wonderfully explained, straight to the point, thanks for the info.
@_R.F_
Honestly, great video! Great exemplification and transmission of applicational understanding.
@matt_MY
If you’re a retail trader, you will understand why it’s hard to make money. But to make things simpler, we can always set a principal for ourselves to take trades. Not everyone is a mathematician so just by solely relying on price action could make you money if you follow your own strict principals.
@markkevinaguilar2396
Great Quant video! The explanations are clear and very informative. I do have some noob question as I am just starting my journey in Quant field—how can we determine whether a specific asset or option adheres to the assumptions of the model used? Are there particular tests or criteria that should be checked? Looking forward to your insights!
@MrMaguuuuuuuuu