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arXiv 1005.0728math.PRq-fin.ST
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CEV模型的Euler-Maruyama近似

The Euler-Maruyama approximations for the CEV model

V. Abramov, F. Klebaner, R. Liptser

摘要:

CEV模型由随机微分方程$X_t=X_0+\int_0^tμX_sds+\int_0^tσ(X^+_s)^pdW_s$、$\frac{1}{2}\le p<1$给出。它具有非Lipschitz扩散系数,并以正概率在零处被吸收。证明了Euler-Maruyama逼近$X_t^n$在Skorokhod度量下弱收敛于过程$X_t$,$0\le t\le T$.我们给出了一个新的连续过程的近似,它允许放松一些技术条件证明弱收敛的\cite{HZa}离散时间鞅问题。我们计算破产概率作为这样的近似的一个例子。由于零点是极限分布的间断点,因此不能保证由模拟计算得到的破产概率收敛到理论破产概率。为了建立这样的收敛性,我们使用Levy度量,并在数值上确认收敛性。虽然结果是针对特定模型给出的,但我们的方法适用于更一般的非Lipschitz扩散吸收情况。

英文摘要:

The CEV model is given by the stochastic differential equation $X_t=X_0+\int_0^tμX_sds+\int_0^tσ(X^+_s)^pdW_s$, $\frac{1}{2}\le p<1$. It features a non-Lipschitz diffusion coefficient and gets absorbed at zero with a positive probability. We show the weak convergence of Euler-Maruyama approximations $X_t^n$ to the process $X_t$, $0\le t\le T$, in the Skorokhod metric. We give a new approximation by continuous processes which allows to relax some technical conditions in the proof of weak convergence in \cite{HZa} done in terms of discrete time martingale problem. We calculate ruin probabilities as an example of such approximation. We establish that the ruin probability evaluated by simulations is not guaranteed to converge to the theoretical one, because the point zero is a discontinuity point of the limiting distribution. To establish such convergence we use the Levy metric, and also confirm the convergence numerically. Although the result is given for the specific model, our method works in a more general case of non-Lipschitz diffusion with absorbtion.

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