Name
Abstract | ||
|---|---|---|
The proliferation of algorithmic trading, derivative usage and highly leveraged hedge funds necessitates the acceleration of market Value-at-Risk (VaR) estimation to measure the severity of portfolios losses. This paper demonstrates how solely relying on advances in computer hardware to accelerate market VaR estimation overlooks significant opportunities for acceleration. We use a simulation based delta-gamma Value-at-Risk (VaR) estimate and compute the loss function using basic linear algebra subroutines (BLAS). Our NVIDIA GeForce GTX280 graphics processing unit (GPU) based baseline implementation is a straight-forward port from the CPU implementation and only had a 8.21x speed advantage over a quadcore Intel Core2 Q9300 central processing unit (CPU) based implementation. We demonstrate three approaches to gain additional speedup over the baseline GPU implemention. Firstly, we reformulate the loss function to reduce the amount of necessary computation and achieved a 60.3x speedup. Secondly, we selected functionally equivalent distribution conversion modules to give the best convergence rate - providing an additional 2x speedup. Thirdly, we merged data-parallel computational kernels to remove redundant load store operations leading to an additional 1.85x speedup. Overall, we have achieved a speedup of 148x against the baseline GPU implementation, reducing the time of a VaR estimation with a standard error of 0.1% from minutes to less than one second. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1645413.1645418 | SC-WHPCF |
Keywords | Field | DocType |
baseline gpu implemention,delta-gamma value-at-risk,market value-at-risk estimation,market var estimation,cpu implementation,loss function,core2 q9300 central processing,additional speedup,baseline implementation,var estimation,baseline gpu implementation,central processing unit,parallel computing,database,fixed income securities,monte carlo simulation,pricing,market value,convergence rate,valuation,value at risk,hedge funds | Central processing unit,Computer science,Parallel computing,Real-time computing,Rate of convergence,Acceleration,Graphics processing unit,Value at risk,Algorithmic trading,Speedup,Computation | Conference |
Citations | PageRank | References |
5 | 0.77 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew Dixon | 1 | 5 | 0.77 |
| Jike Chong | 2 | 136 | 11.62 |
| Kurt Keutzer | 3 | 5040 | 801.67 |