CAJ | 학술논문

为了克服极小概率事件发生概率估计的困难,提出了把重要抽样技术发展到外汇期权组合非线性VaR模型中,通过改变市场变量回报分布的期望向量和协方差矩阵,在相应区域产生更多的样本,使得该情形下不再是稀有事件Monte Carlo模拟,从而减少Monte Carlo模拟计算工作量,更精确地估计出组合的损失概率,而组合的损失概率是计算组合损失分布的分位点(VaR值)的必备条件.模拟结果表明,该算法比常用Monte Carlo模拟法的计算效率更有效,且能很大程度上减少所要估计的损失概率的方差.
위료극복겁소개솔사건발생개솔고계적곤난,제출료파중요추양기술발전도외회기권조합비선성VaR모형중,통과개변시장변량회보분포적기망향량화협방차구진,재상응구역산생경다적양본,사득해정형하불재시희유사건Monte Carlo모의,종이감소Monte Carlo모의계산공작량,경정학지고계출조합적손실개솔,이조합적손실개솔시계산조합손실분포적분위점(VaR치)적필비조건.모의결과표명,해산법비상용Monte Carlo모의법적계산효솔경유효,차능흔대정도상감소소요고계적손실개솔적방차.
To overcome the difficulty in estimating low probability, the paper proposes that importance sampling technique is developed upto non-linear VaR model of FX option portfolio. Producing more samples in corresponding region by changing expectation vector and covariance matrix of distribution of market factors returns, this makes the state not be rare event simulation. Accordingly, this decreases calculating effort in Monte Carlo simulation. Moreover, the loss probability of portfolio is estimated precisely. Precise estimation of loss probability of portfolio is a prerequisite to calculating VaR, which is a percentile of the loss distribution. The simulation result shows the algorithm has more much effectiveness of computational efficiency than the standard Monte Carlo simulation, and can lead to large variance reductions when estimating the loss probability of portfolio.