January 17th, 2012, 2:38 pm
QuoteOriginally posted by: outrunHee T4A, I differ on both points. I suspect that we do agree, in general, and are really discussing crucial nuances in which a general rule breaks down.QuoteOriginally posted by: outrunMy view for multiple payoffs per scenario's is that it's a must for valuations of portfolios. You need to price all elements in the portfolio (all payoffs) against various scenario's in order to build a set of scenario's of the total portfolio value.Yes, I do see your point. In some applications, reusing the paths for different instruments will reduce some types of estimation error (e.g., the estimated price difference between two call-like instruments). And, in other applications, reusing the paths for different instruments will increase some types of estimation error (e.g., the price difference between a call-like and put-like instrument or the correlation between prices for two call-like instruments). It's a matter of thinking through how errors in reused samples (versus independent samples) propagate to the final aggregate statistical estimates to see if reused samples (versus independent samples) produces a more accurate estimate.QuoteOriginally posted by: outrunFor the rnd: I think it's bad to create asymmetry in parallel algorithms (have a central CPU doing something different, having a scaling bottleneck), it will also create a lot of data copying and moving and I expect that to be much than to generate it at various locations locally.In general, I agree entirely that asymmetry is bad in parallel algorithms. But it's not always avoidable. If one must adapt a serial or single-threaded algorithm (e.g., an RNG) to parallel processing, then some kind of asymmetry seems inevitable. It's then a matter of what kind of asymmetry does one use: pre-calculation of seed intervals or a running buffered production of RNs for dispatch.