Currently checking out "FLEXIBLE INDETERMINATE FACTOR-BASED ASSET ALLOCATION" dash.harvard.edu/bitstream/ha... I'm interesting in bettering my understand of pension funds / endowments asset allocation strategies.
a handful of billionaires want lower taxes and they're able to fund an entire ecosystem of thousands of pundits, influencers, newscasters, writers, professors, and podcasters with their table scraps
one of my fav games to watch on twitch. Any souls game really.
It seems however that using characteristic functions (and also moment generating functions) makes things less involved. So I am looking into that. As a learning exercize though I'll look into rederiving the gaussian model first, if it makes sense will look into other distributions.
My master's dissertation involved deriving an involved and tedious log likelihood function. For my PhD I want to explore variations of that model with different distributional assumptions. I quickly realized that everything that's non Gaussian is even more tedious lol fml.
Okay so I am learning about characteristic functions again. is it me or it seems like it's significantly easier to solve very involved integrals that way?
I'm thinking about dialing down my time on my main account over there. I'm not getting what I want from it anymore.
whoop the other place is a little broken right now