black-scholes

I mean, good grief, a lot of the interest is in the Brownian motion approach to the Dirichelet problem and Black-Scholes, and that was Fisher Black.

Not a facetious but serious question, but what theoretical part of Black-Scholes is really important for a options trader's everyday trading?

I'm assuming your calling black-scholes the lamp post and the more generic technique the alley.

Considering the pretty volatile nature of startups, a rough Black-Scholes calculation will show you're effectively discounting the shared by about 40% by tranching it in that way.

I’ve seen many CS graduates who tried to self-study their Black-Scholes, but rarely do they achieve anything more than a superficial understanding of the underlying theory.

I see: - Black-Scholes Valuation Computing - Weather simulation fairly specialist number crunching I guess [update] I see the 'worlds fastest computer', Tianhe-2 uses 48,000 Xeon Phis.

Of course, there are other times where using Black-Scholes to model a high-volatility market outside of the 'smoothly differentiable' market assumptions it was built on can crash a large part of the economy.

Programmers are not better than programs if the program it is competing against is a black-scholes estmator, compiler, interpreter, or assembler, and time is an input to the cost function.

This is interesting because the options market is almost by definition slower than stock markets (not just because of the presence of HF firms in the underlying markets, but think of Black-Scholes - one of the inputs to the model is the stock price).

But not sure how the theory can really help me hedge better, come up with better implied volatility as compared to the current open source plug n' chug frameworks that computes Black-Scholes pricing (e.g., QuantLib).

The reason that I was told is that people are afraid of using some generic black box and instead prefer something like black-scholes (or whatever) that has an explanation they can understand.

To establish in a semi-rigorous way the (btw obvious) relationship between coarsening in physics and in stacked neural networks, as was done long after the latter became a thing [1][2], is at the same level as observing the analogy between Black-Scholes and diffusion.

Thinking of startup investments as options seems to make a lot of sense but as one of the commenters there noted: "Black-Scholes is probably a poor model choice to price start-up options as the return distribution is clearly not normal."

To do the analogous thing with startup options would (a) result in employees getting taxed even earlier and (b) require using Black-Scholes or something to estimate the value of the option, resulting in "income" that is even more divorced from reality than the current status quo.

And further, you are not in the least addressing the points in this and other articles, the problems with Black-scholes, the Gaussian Copula and so-forth.