论文标题
数据驱动的P型载序和P-Spline-Garch模型
A data-driven P-spline smoother and the P-Spline-GARCH-models
论文作者
论文摘要
研究了时间序列及其渐近特性的惩罚样条平滑。开发了用于选择平滑参数的数据驱动算法。该提案用于根据平方返回的对数数据转换来定义众所周知的garch的半参数延伸，称为p-Spline-garch。结果表明，现在错误过程与所有订单的有限矩成倍强烈混合。在这种情况下，P-Sphine的渐近正态性被证明了。该提案的实际相关性通过数据示例和仿真说明。该提案进一步应用于风险和预期不足的价值。
Penalized spline smoothing of time series and its asymptotic properties are studied. A data-driven algorithm for selecting the smoothing parameter is developed. The proposal is applied to define a semiparametric extension of the well-known Spline-GARCH, called a P-Spline-GARCH, based on the log-data transformation of the squared returns. It is shown that now the errors process is exponentially strong mixing with finite moments of all orders. Asymptotic normality of the P-spline smoother in this context is proved. Practical relevance of the proposal is illustrated by data examples and simulation. The proposal is further applied to value at risk and expected shortfall.
