【摘 要】
:
Using nonparametric estimation method, this paper studies the dynamic evolution features of jumps in volatility of domestic stock market.We build the heterogeneous autoregressive model of realized vol
【机 构】
:
Applied Economics Postdoctoral Research Station, School of Economics and Finance, Xi 'an Jiaotong U
【出 处】
:
The Third IMS-China International Conference on Statistics a
论文部分内容阅读
Using nonparametric estimation method, this paper studies the dynamic evolution features of jumps in volatility of domestic stock market.We build the heterogeneous autoregressive model of realized volatility for stock index returns, and use the conditional extreme value approach to predict the volatility risk of stock market dynamically.
其他文献
Bayes factor usually models the distribution of the observed data under the alternative hypothesis H1 over that under the null hypothesis Ho, It has also been used to model the distribution of a test
The growing availability of population based disease registry data has brought precious opportunities for epidemiologists to understand the natural history of chronic diseases.It also presents challen
The receiver operating characteristic (ROC) curve and its summary measure,the area under the ROC curve (AUC), are useful statistical tool to evaluate the performance of a biomarker predicting disease
We will discuss a general semi-parametric estimation strategy for analyses of longitudinal binary response data ascertained from outcome dependent sampling designs.The Sequential Offsetted Logistic Re
Gene-gene interactions have long been recognized to be fundamentally inportant to understand genetic causes of complex disease traits.At present, identifying gene-gene interactions from genome-wide ca
Genomic imprinting and maternal effects are two epigenetic factors that have been increasingly explored for their roles in the etiology of complex diseases.This is part of a concerted effort to find t
Feature extraction corresponds to reduced rank modeling which has been widespread real-world applications.This talk investigates non-asymptotic performance of rank penalized estimators.The constructio
Much of the recent statistics, biostatistics and econometric literature investigate conditional moment restrictions (or estimating equations) models in the presence of censored observations.Parametric
In many conventional scientific investigations with high or ultra-high dimensional feature spaces, the relevant features, though sparse, are large in number compared with classical statistical problem
We propose a novel sparse matrix graphical model for matrix-variate data.By penalizing the row and column precision matrices, our method gives sparse estimates of these matrices.The resulting estimate