The memory of stochastic volatility models
The memory of stochastic volatility models
复制标题
随机波动率模型的记忆
DOI：
10.1016/s0304-4076(00)00079-8
复制
发表时间：
2001
影响因子：
6.3
通讯作者：
P. Robinson
中科院分区：
文献类型：
--
作者：
P. Robinson
多元正态向量函数的协方差的一个有效的渐近展开适用于近似的自协方差的高斯潜变量的非线性变换所产生的时间序列，和这些非线性函数，特别是参考长记忆随机波动模型，服务于识别潜在的高斯过程和非线性变换所扮演的角色。简单的随机波动率模型的影响进行了详细研究，数值和蒙特卡罗计算，并讨论了循环行为，横截面和时间聚集，和多变量模型的应用。
A valid asymptotic expansion for the covariance of functions of multivariate normal vectors is applied to approximate autocovariances of time series generated by nonlinear transformation of Gaussian latent variates, and nonlinear functions of these, with special reference to long memory stochastic volatility models, serving to identify the roles played by the underlying Gaussian processes and the nonlinear transformation. Implications for simple stochastic volatility models are examined in detail, with numerical and Monte Carlo calculations, and applications to cyclic behaviour, cross-sectional and temporal aggregation, and multivariate models are discussed.