11–20 of 23 posts
I'm curious about the applications of these systems. Why is it that finance would be the third largest (known) group for supercomputer use? What kind of number crunching would they be doing?
Some derivative pricing models require a lot of cycles.
I'm curious about the applications of these systems. Why is it that finance would be the third largest (known) group for supercomputer use? What kind of number crunching would they be doing?
Possibly used for risk management, generating lots of different scenarios and calculating their P&L impact. This would include VaR calculation. Some derivative pricing models require a lot of cycles.
I'm curious about the applications of these systems. Why is it that finance would be the third largest (known) group for supercomputer use? What kind of number crunching would they be doing?
That is: you look at the historical prices of all commodities over time and try to figure out which ones tend to vary together (eg copper-mining companies go up when copper prices go up... but you're looking for less obvious examples than that). Then you look at whether current prices diverge from these trends at all, and if they do you buy/sell accordingly. At least, that's the handwavey version I know -- I'm sure whatever they're doing at Rennaisance and DE Shaw is something I don't even know about.
I just watched this the other day - http://www.youtube.com/watch?v=J9kobkqAicU Cray-1 Supercomputer 30th Anniversary. I would give up my career and turn it around if given a chance to work in HPC.
Earlier quoted context omitted.
http://en.wikipedia.org/wiki/De_novo_protein_structure_predi...
How does that relate to finance? I know that protein folding involves massive computation, as it was what our university cluster was being used for 40% of the time.
I'm curious about the applications of these systems. Why is it that finance would be the third largest (known) group for supercomputer use? What kind of number crunching would they be doing?
A lot of it is statistical arbitrage. That is: you look at the historical prices of all commodities over time and try to figure out which ones tend to vary together (eg copper-mining companies go up when copper prices go up... but you're looking for less obvious examples than that). Then you look at whether current prices diverge from these trends at all, and if they do you buy/sell accordingly. At least, that's the…
As stated, risk management is a big application: VaR and market stress scenarios are computationally intensive, particularly for portfolios with path-dependent derivatives. Pricing is the other big application: it is similarly computationally intensive to value derivatives against the market-implied term structure of volatility.
Earlier quoted context omitted.
How does that relate to finance? I know that protein folding involves massive computation, as it was what our university cluster was being used for 40% of the time.
I don't know why the grandparent mentioned it, but I do know that D.E. Shaw works on both.