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arXiv 1502.05442q-fin.MF
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一般高斯随机波动率模型的极值-罢工渐近性

Extreme-Strike Asymptotics for General Gaussian Stochastic Volatility Models

Archil Gulisashvili, Frederi Viens, Xin Zhang

摘要:

考虑了一个随机波动率资产价格模型,其中波动率是具有任意给定均值和协方差的连续高斯过程的绝对值。通过对综合方差进行Karhunen-Loève展开,并利用一般二阶混沌变量密度的精确估计,我们得到了资产价格密度在大值和小值情况下的渐近性,并研究了这些模型中隐含波动率的翼行为.我们的主要结果为隐含波动率展开式中的前五项提供了明确的表达式。前三项的表达式很简单,并且基于高斯过程的三个基本谱型统计:协方差算子的顶部特征值,该特征值的重数,以及均值函数在顶部特征空间上的投影的$L^{2}$范数。第四项需要所有特征元素的知识。我们提出了详细的数字现实的流动性假设的基础上,经典和长记忆波动率模型校准的基础上,我们的扩展。

英文摘要:

We consider a stochastic volatility asset price model in which the volatility is the absolute value of a continuous Gaussian process with arbitrary prescribed mean and covariance. By exhibiting a Karhunen-Loève expansion for the integrated variance, and using sharp estimates of the density of a general second-chaos variable, we derive asymptotics for the asset price density for large or small values of the variable, and study the wing behavior of the implied volatility in these models. Our main result provides explicit expressions for the first five terms in the expansion of the implied volatility. The expressions for the leading three terms are simple, and based on three basic spectral-type statistics of the Gaussian process: the top eigenvalue of its covariance operator, the multiplicity of this eigenvalue, and the $L^{2}$ norm of the projection of the mean function on the top eigenspace. The fourth term requires knowledge of all eigen-elements. We present detailed numerics based on realistic liquidity assumptions in which classical and long-memory volatility models are calibrated based on our expansion.

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