Utilizing Intel Advanced Vector Extensions for Monte Carlo Simulation based Value at Risk Computation

Value at Risk (VaR) is a statistical method of predicting market risk associated with financial portfolios. There are numerous statistical models which forecast VaR and out of those, Monte Carlo Simulation is a commonly used technique with a high accuracy though it is computationally intensive. Calculating VaR in real time is becoming a need of short term traders in current day markets and adapting Monte Carlo method of VaR computation for real time calculation poses a challenge due to the computational complexity involved with the simulation step of the Monte Carlo Simulation. The simulation process has an independent set of tasks. Hence a performance bottleneck occurs during the sequential execution of these independent tasks. By parallelizing these tasks, the time taken to calculate the VaR for a portfolio can be reduced significantly. In order to address this issue, we looked at utilizing the Advanced Vector Extensions (AVX) technology to parallelize the simulation process. We compared the performance of the AVX based solution against the sequential approach as well as against a multithreaded solution and a GPU based solution. The results showed that the AVX approach outperformed the GPU approach for up to an iteration count of 2. Since such a number of iterations is generally not required to gain a sufficiently accurate VaR measure, it makes sense both computationally and economically to utilize AVX for Monte Carlo method of VaR computation.