GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model

TL;DR

This paper demonstrates that GPU acceleration significantly speeds up Bayesian inference for the realized stochastic volatility model using the Hybrid Monte Carlo algorithm, with up to 17 times faster performance compared to CPU implementations.

Contribution

The paper develops GPU-accelerated implementations of the HMC algorithm for RSV model Bayesian inference, showing substantial computational speedups.

Findings

GPU implementation is up to 17 times faster than CPU.

CUDA Fortran and OpenACC achieve similar speedups.

GPU acceleration enables more efficient Bayesian inference for financial models.

Abstract

The realized stochastic volatility (RSV) model that utilizes the realized volatility as additional information has been proposed to infer volatility of financial time series. We consider the Bayesian inference of the RSV model by the Hybrid Monte Carlo (HMC) algorithm. The HMC algorithm can be parallelized and thus performed on the GPU for speedup. The GPU code is developed with CUDA Fortran. We compare the computational time in performing the HMC algorithm on GPU (GTX 760) and CPU (Intel i7-4770 3.4GHz) and find that the GPU can be up to 17 times faster than the CPU. We also code the program with OpenACC and find that appropriate coding can achieve the similar speedup with CUDA Fortran.