Hope you enjoyed learning about the implied volatility surface! Feel free to check out Quant Guild for more open-source projects! Website: quantguild.com Blog: / quant
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!
Hello , thanks a lot for these series of videos. Can you please do one to explain the Feynman-Kac theorem and how does it provide an expansion for the conditional expectations and how it is linked to the Black and Scholes PDE with delta hedging it is quite confusing sometimes . Actually for more context since the feynman-kac concept is used to identify the relationship between Implied and local volatility in some text books and i want to understand does the feynman-kac theorem is useful in this case thanks a bunch
Yet another great video, i had a request for maybe another video! could we maybe look into optimizing a "pre existing" portfolio and talk about ways in order to re optimize or hedge our bets using correlation coeffiants since were likley heading into a recession it would be cool to look into stuff like that and see ways to hedge educational purposes only !! others video ideas options trading or talk about what it is cointegration algorithms optimiztion hedging
Very good video. I especially like the use of the scipy optimize method. On another matter, when calculating T (time), why did you use the number of calendar days to expiration as the numerator and the number of trading days in a year for the denominator? It seems to me that T should've been 5 trading days to expiration / 252 trading days in one calendar year or 6 calendar days to expiration / 365 calendar days per year. Also, the risk free rate is a mystery to me. I've used the discounted 1 year treasury rate. How do you determine the best risk free rate?
Glad you enjoyed the video! Good questions. The ratio 5/252 is approximately 7/365 so in this case we are being more accurate than precise. The notion of a risk free rate is used to proxy alternative investments - what you would demand the return to be investing an alternate security with a guaranteed return to aid in pricing a risky asset. Several proxies for a risk free rate may be used, treasuries can be an appropriate choice!
Hello! This video was awesome. Nothing like it out there. I am new to your channel. I was wondering, the Black-Scholes model is for European Options. I have read that binomial models can be used for American options. Do you have anything that talks about the binomial model?
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.
Hi I’m undergraduate In accounting but I’m considering enrollment for quantitative finance. I was trying to understand Signal processing but my statistics tutor who’s a phd student in financial engineering told me that it’s a phd level. Are you doing some signal processing In the future? Thanks
Thank you for your comment Konstantin! We would be happy to offer any guidance to aid in your decision to enroll in a quantitative finance program. As for your statement regarding signal processing - we disagree with your tutor and don't believe education should be restricted to certain degrees, programs, or "levels" of difficulty. In fact, we know of many undergraduates ( in many programs including financial engineering, electrical engineering, etc...) that are quite experienced in that field of study. In the future, we are looking to provide accessible videos, notebooks, and other resources on this topic and others!
@@QuantGuild thank you very much.I was advised just to enroll quantitative finance and eventually pursuing a phd level for machine learning, signal processing, HFT etc... Despite the fact that I was offered a job for private banking in wealth management as an independent consultant but quantitative skills are still valuable especially for modeling and risk management. Best regards
Hey, you have got interesting content. I am a Chartered Financial Analyst but have no background in STEM . Is it possible for me to work as an Algo trader? We learn some primary time series analysis, statistics, regression & theory for Factor models and efficient frontier. Is calculus absolutely essential to succeed in algo trading or one can apply ML algos irrespective. Thanks.
Hey Atul, thank you for your comment! It seems you are in a similar situation as those with a STEM education but no finance experience (in this case finance experience with no formal STEM experience). The answer will depend on the role you’re seeking. A more quant oriented role will require advanced maths but an Algo trader/developer role that is oriented purely toward strategy development and implementation may require only the fundamentals of basic statistics (you will however need advanced programming skills but these you can build on your own!). Many times this misconception is that hedge funds and quant funds use ridiculous mathematics to devise their strategies when in reality it’s a careful combination of the appropriate maths and creative ideas! Hope this helps!
