In contrast to the usual procedure of estimating the distribution of a time series and then obtaining the quantile from the distribution, we develop a compensatory model to improve the quantile estimation under a given distribution estimation. A novel penalty term is introduced in the compensatory model. We prove that the penalty term can control the convergence error of the quantile estimation of a given time series, and obtain an adaptive adjusted quantile estimation. Simulation and empirical analysis indicate that the compensatory model can significantly improve the performance of the value at risk (VaR) under a given distribution estimation.
翻译:与通常估计时间序列分布并随后从分布中获得量化的程序不同,我们开发了一个补偿模型,以改进在特定分配估计下的量化估计。在补偿模型中引入了新的惩罚术语。我们证明,惩罚术语可以控制特定时间序列量化估计的趋同错误,并获得适应性调整的量化估计。模拟和实证分析表明,补偿模型可以大大改善特定分配估计下风险值(VaR)的性能。