In recent years, financial simulations have gotten computationally intensive due to larger portfolio sizes, and an increased demand to perform real-time risk analysis. In this paper, we propose a hardware implementation that uses a recursive analytical method to price the Collateralized Debt Obligations. A novel convolution approach based on FIFOs for storage is implemented for the recursive convolution. It is also used to address one of the main drawbacks of the analytical approach. The FIFO-based convolution approach is compared against two different convolution approaches outperforming them with a much smaller memory usage. The CDO core designed with the FIFO-based convolution method is implemented and tested on a Virtex-5 FPGA and compared against a C implementation, running on a 2.8GHz Intel Processor, resulting in a 41-fold speed up. A brief comparison against a Monte Carlo based hardware implementation for structured instruments yields mixed results.

In recent years, financial simulations have gotten computationally intensive due to larger portfolio sizes, and an increased demand to perform real-time risk analysis. In this paper, we propose a hardware implementation that uses a recursive analytical method to price the Collateralized Debt Obligations. A novel convolution approach based on FIFOs for storage is implemented for the recursive convolution. It is also used to address one of the main drawbacks of the analytical approach. The FIFO-based convolution approach is compared against two different convolution approaches outperforming them with a much smaller memory usage. The CDO core designed with the FIFO-based convolution method is implemented and tested on a Virtex-5 FPGA and compared against a C implementation, running on a 2.8GHz Intel Processor, resulting in a 41-fold speed up. A brief comparison against a Monte Carlo based hardware implementation for structured instruments yields mixed results.