In this paper, we study the memory properties of transformations of linear processes. Dittmann and Granger (2002) studied the polynomial transformations of Gaussian FARIMA(0,d,0) processes by applying the orthonormality of the Hermite polynomials under the measure for the standard normal distribution. Nevertheless, the orthogonality does not hold for transformations of non-Gaussian linear processes. Instead, we use the decomposition developed by Ho and Hsing (1996, 1997) to study the memory properties of nonlinear transformations of linear processes, which include the FARIMA(p,d,q) processes, and obtain consistent results as in the Gaussian case. In particular, for stationary processes, the transformations of short-memory time series still have short-memory and the transformation of long-memory time series may have different weaker memory parameters which depend on the power rank of the transformation. On the other hand, the memory properties of transformations of non-stationary time series may not depend on the power ranks of the transformations. This study has application in econometrics and financial data analysis when the time series observations have non-Gaussian heavy tails. As an example, the memory properties of call option processes at different strike prices are discussed in details.
Adaptive semiparametric wavelet estimator and goodness-of-fit test for long memory linear processes
本文首先研究了一般半参数框架下线性过程长记忆参数的自适应小波估计。这是Bardet { \it et al.}（2008），只涉及高斯过程。此外，修改了长记忆参数估计的定义，改进了高斯情形下的渐近结果。最后还建立了一种易于使用的自适应拟合优度检验：卡方检验。仿真证实了自适应估计和测试的一致性和鲁棒性的有趣的属性。
