That point about the weather and the pipeline functioning is honestly fascinating
My life is nothing but stochastic drift
Quants are basically a wall of people who are smart that hedge funds use to make it look like their insider trades are carefully calculated.
We have now fully investigated quants. What should we cover next?
i may not be a quant or a journalist of the reputed dan toomey's caliber, but i, too, am passionate about edge
they do statistics all day. the math used in quant finance isn't earth-shattering, it's essentially just graduate level statistics applied in clever ways on massive datasets and really fast computers
That clip from The Big Short lives in my brain rent free
either your tie is very long or your torso is very short
Can you investigate my dad he went missing when I was a kid
I have never in my life seen a 9 minute 59 video on YouTube. What is this deviousness Dan?
I'm a risk quant in a top tier IB. Another point to mention is that there are a lot of different types of quants. Buy side, sell side, high frequency, low frequency, pricing, risk, assets allocation, hedging strategy, and model validation etc. The nature (and pay) are very different.
The trenchcoat is gone 😮
I clicked fully expecting The Big Short scene with "That's my quant... My quantitative" was not disappointed 😂
Dan. I don't believe you without the trench coat
Honesty, if more financial information was presented in this way, I would probably know A LOT more about the stock market and finance in general.
This is honestly the realest sounding job so far. Also something I might have actually enjoyed if I had reallised it was an option and was actually motivated when I was younger
The flash of Sam Bankman-Fried's face on the Jane Street logo was masterful.
There’s a deviation between the polynomial fit model and PLTR's market performance, likely due to missing stochastic drift or an incomplete volatility model. Using a skewed Black-Scholes approach with decay could account for market skewness and time decay, but adjustments are needed. 1. Add stochastic volatility using Heston or SABR models, so volatility evolves over time and captures market drift better. 2. Factor in time decay, especially if theta is skewed. 3. Account for implied volatility skew and construct a volatility surface from PLTR’s options market. 4. Use local volatility to allow volatility to change with price and time. 5. Add a stochastic drift adjustment, like a mean-reverting component. 6. Try implementing a time-dependent skew in the Black-Scholes formula. 7. Consider adding jumps with a model like Merton’s Jump Diffusion. In short, leveraging skew, refitting your polynomial model, and refining your GBDT model could help with the divergence you're seeing. I'd also suggest running a sensitivity analysis on the volatility skew to see if it captures the drift better.
In my opinion it's worth the 10 microcents just to not have to think about Nebraska. If i wanted to know the weather there i just go there
@-chisai-