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 quad-core Intel Core2 Q9300 central processing unit (CPU) based implementation.We demonstrate three approaches to gain additional speedup over the baseline GPU implementation. 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.