billziss

Has anyone else experienced a degradation of the OpenAI UX in recent weeks (mostly after introduction of GPT-4o).

- Links are not clickable and I have to ask GPT to redisplay the full URL so I can copy and paste it.

- The builtin programming sandbox is unable to access web resources. For example, it cannot be used to fetch a Wikipedia page for further processing. (It was able to so before.)

- A few days ago the UI was blank for a full day and it showed me as signed off. Attempts to sign in would not succeed. This was fixed on its own a day later.

- Math formulas would not correctly render for a few days. This appears to be fixed now, but it is very slow compared to before (waiting seconds for pages with a lot of math to render).

- Many other small quirks and frustrations.

For the record I am using macOS/Safari.

Is there a modern theory for the probability distribution of stock returns?

It is relatively easy to deduce that under idealized conditions stock returns follow a log normal distribution. One arrives at this by considering the product of ratios of prices ("stock returns"), applying a natural logarithm to convert the product into a sum and then applying the Central Limit Theorem under the condition that the ratios are iid (independent and identically distributed) and have finite mean and variance.

The problem is of course that we cannot just assume that returns are iid or that they have finite variance. So I am seeking alternative theories that try to address these shortcomings.

I am aware of Mandelbrot's Multifractal Model of Asset Returns. Is this considered SOA in the field? Is there something else that is considered a better model or easier to work with?