Parallelization and characterization of GARCH option pricing on GPUs

Abstract

Option pricing is an important problem in computational finance due to the fast-growing market and increasing complexity of options. For option pricing, a model is required to describe the price process of the underlying asset. The GARCH model is one of the prominent option pricing models since it can model stochastic volatility of the underlying asset. To derive expected profit based on the GARCH model, tree-based simulations are one of the commonly used approaches. Tree-based GARCH option pricing is computing intensive since the tree grows exponentially, and it requires enormous floating point arithmetic operations. In this paper, we present the first work on accelerating the tree-based GARCH option pricing on GPUs with CUDA. As the conventional tree data structure is not memory access friendly to GPUs, we propose a new family of tree data structures which position concurrently accessed nodes in contiguous and aligned memory locations. Moreover, to reduce memory bandwidth requirement, we apply fusion optimization, which combines two threads into one to keep data with temporal locality in register files. Our results show 50× speedup compared to a multi-threaded program on a 4-core CPU.