GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model
GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model
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GPU 计算在实现随机波动模型的贝叶斯推理中的应用
DOI：
10.1088/1742-6596/574/1/012143
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发表时间：
2015
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通讯作者：
Ｔｅｔｓｕｙａ Ｔａｋａｉｓｈｉ
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作者：
Tetsuya Takaishi and Toshiaki Watanabe;Ｔｅｔｓｕｙａ Ｔａｋａｉｓｈｉ;Ｔｅｔｓｕｙａ Ｔａｋａｉｓｈｉ
利用已实现波动率作为附加信息的已实现随机波动率（RSV）模型被提出来推断金融时间序列的波动率。我们考虑的RSV模型的贝叶斯推断的混合蒙特卡罗（HMC）算法。HMC算法可以被并行化，从而在GPU上执行以加速。GPU代码是用CUDA Fortran开发的。我们比较了在GPU（GTX 760）和CPU（Intel i7-4770 3.4 GHz）上执行HMC算法的计算时间，发现GPU可以比CPU快17倍。我们还使用OpenACC对程序进行了编码，发现适当的编码可以达到与CUDA Fortran相似的加速比。
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 3.4 GHz) 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.