Inference in Heteroscedastic Nonlinear Time Series Under Long Memory With Applications to Finance

项目摘要：在物理科学、经济学和金融学中，离散时间序列的许多实现都具有长记忆性，即，它们的自协方差作为滞后的函数随着滞后接近无穷大以双曲线速率减小到零。 这样的过程在原点处具有无限的谱密度。 该建议的一部分是关于发展渐近最佳和强大的估计异方差，非光滑，非线性时间序列模型中存在的回归或解释协变量，可能有很长的记忆，在半参数设置。 特别是，它计划获得的非光滑自回归模型产生的实验时，有长记忆解释变量存在于这些模型中，当误差分布是未知的限制。 在第二部分中，PI/Co-PI提出发展渐近分布自由检验，用于将参数自回归均值和/或分位数函数拟合到异方差平稳遍历时间序列。 这些检验被期望为不涉及非参数曲线估计的部分和过程的某些鞅变换的函数。 PI/Co-PI还计划与一些现有测试进行比较研究。 所获得的结果将用于估计感兴趣的参数和测试与金融经济学问题相关的理论。如果随着观测之间的距离增加，远距离观测之间的关联缓慢衰减但持续存在，则称数据集具有长记忆。 在一段时间内观察到的数据集称为时间序列。 异方差时间序列是指给定过去，当前时间的观测值的条件变异性取决于过去。 这些数据通常出现在经济学、金融学和物理学中。 特别是，长记忆异方差时间序列的一个重要例子是现货收益率的波动过程。人们还知道，这种波动性随着银行干预货币市场而增加。 这种干预过程是高度非平滑的时间序列，因为它在大多数情况下为零，在某些时间内具有某些突发。 该建议的部分重点是在一类非光滑非线性异方差时间序列模型中开发最佳推理程序。 另一部分强调了所获得的结果的应用，以开发新的市场效率测试和金融经济学中的时间依赖性风险溢价估计以及提案中提到的与德国马克和瑞士法郎对美元汇率和商品价格有关的高频数据。

PROJECT ABSTRACT:In physical sciences, economics, and finance many realizations of discrete time series exhibit long memory, i.e., their autocovariances as a function of lag decrease to zero at a hyperbolic rate as the lag approaches to infinity. Such processes have unbounded spectral densities at the origin. A part of this proposal is concerned with developing asymptotically optimal and robust estimators for heteroscedastic, non-smooth, non-linear time series models in the presence of regression or explanatory covariates that may have long memory, in a semi-parametric setting. In particular, it is planned to obtain the limits of the experiments generated by the non-smooth autoregressive models when there are long memory explanatory variables present in these models and when the error distributions are unknown. In the second part, the PI/Co-PI propose to develop asymptotically distribution free tests for fitting a parametric autoregressive mean and/or quantile function to a heteroscedastic stationary ergodic time series. These tests are expected to be functions of certain martingale transforms of a partial sum processes that do notinvolve nonparametric curve estimation. PI/Co-PI also plan to carry out a comparative study with some of the existing tests. The results obtained will be used to estimate parameters of interest and test theories relevant to problems in financial economics.A data set is said to have long memory if an association between distant observations is slowly decaying but persistent, as the distance between observations increases. A data set observed over a period of time is called a time series. A heteroscedastic time series is one where the conditional variability of an observation at the current time, given the past, depends on the past. Such data often arises in economics, finance, and physical sciences. In particular, an important example of long memory heteroscedastic time series is the volatility process in spot returns. It is also known that this volatility increases with bank interventions in currency markets. This intervention process is highly non-smooth time series since it is zero most of the times with certain bursts over some times. Part of the emphasis of the proposal is on developing optimal inferential procedures in a class of non-smooth non-linear heteroscedastic time series models. Another part emphasizes application of the results obtained to develop new tests of market efficiency and estimates of time dependent risk premium in financial economics and high frequency data mentioned in the proposal pertaining to German Mark and Swiss Frank vs. US Dollar exchange rates and commodity prices.