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>It's not better because of huge computation resources, it's actually more computationally efficient than some classic alternatives It's similar with options p
by logicchains 1y ago
>It's not better because of huge computation resources, it's actually more computationally efficient than some classic alternatives
It's similar with options pricing. The most sophisticated models like multivariate stochastic volatility are computationally expensive to approximate with classical approaches (and have no closed form solution), so just training a small NN on the output of a vast number of simulations of the underlying processes ends up producing a more efficient model than traditional approaches. Same with stuff like trinomial trees.
- William_BB 1y agoInteresting. Are these models the SOTA in the options trading industry (e.g. MM) nowadays?
- cactusfrog 1y agoThis is really interesting. I think force fields in molecular dynamics have underwent a similar NN revolution. You train your NN on the output of expensive calculations to replace the expensive function with a cheap one. Could you train a small language model with a big one?
- lossolo 1y ago> Could you train a small language model with a big one? Yes, it's called distillation.
- jwjohnson314 1y agoCan you elaborate on this? I am curious what processes are being simulated to feed the network.