针对现有 Va R计算中主流方法的缺陷 ,创新性地提出了一种基于马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)模拟的 Va R计算方法 ,以克服传统 Monte Carlo模拟的高维、静态性缺陷 ,提高估算精度 .通过对美元国债的实证分析和计算 ,验证了 MCMC方法的优越性 .
Abstract:
In order to overcome the limitations of Monte Carlo simulation method in computing VaR, i.e. high-dimensionality and static characteristics, this paper put forward a new method of Markov chain Monte Carlo(MCMC)simulation to improve the computing precision. And a computing example of US treasury bonds proved the advantage of MCMC.