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Another major application is in finance! Computing risk metrics for hedging purposes used to to be based on a "bump and reprice" method, i.e. you bump the input
by curiousgal 3y ago
Another major application is in finance! Computing risk metrics for hedging purposes used to to be based on a "bump and reprice" method, i.e. you bump the input by a small amount and see how that affects the price (the definition of a derivative). But now in modern quant libraries, implementing the pricing as a graph allows you to get all of the sensitivities (derivatives) for free!
- blackbear_ 3y agoThat's cool! But it relies on the model being an accurate representation of reality, right? What kind of models are we talking about anyways, and what inputs do they use? (Asking out of curiosity, not skepticism)
- mhh__ 3y agoA model is a computation based on its initial assumptions. Sometimes that's fine. The key thing is that the (good) practitioners know the finance and know the models. If it's obviously wrong that's a sign in itself - a simple model doesn't fit the market: You might be about to lose some money, or you could take on some risk from other people and get rewarded for it (people are panicking). Weirder derivatives and so on can get more dangerous, of course. However even the really famous example from the 07 crash (pricing CDOs and CDS against them) was arguably due to a deliberate ignorance of widespread fraud and fictitious numbers than the models as per se. Garbage in garbage out (the model was also not great but still)
- mhh__ 3y agoNot quite. Some people want bump Greeks. For example a company may have a standard bump. Make of that what you will.
- KMag 3y agoNot quite free. Last I checked, adding automatic differentiation to GPU computation for pricing exotic derivatives roughly doubled the computation cost. (Though, traditional bump-and-reprice has exponential cost in the number of dimensions.)