基于非参数方法的银行操作风险度量

Operational risk measures for banks based on nonparametric methods

金融业全球化竞争和金融管制放松,导致商业银行面临的操作风险不断增加,操作风险已成为金融监管的焦点.因此,对商业银行和其它金融机构来说,可靠的操作风险度量正变得越来越重要.文章采用基于厚尾分布的非参数方法度量我国商业银行操作风险,给出了VaR的点估计方法和3种区间估计方法(正态近似法NA,经验似然法EL,数据倾斜法DT).此方法的优点在于不用假设操作风险损失分布,这样可以消除参数化模型设定差异而带来的估计偏差.同时,根据厚尾分布的特征,提出了新的厚尾分布样本均值求法,调整后均值更注重对尾部的描述和刻画.实证结果表明:调整后的厚尾分布样本均值大于简单算术平均值,更符合右偏厚尾的分布特征;非参数方法得到的VaR点估计和区间估计考虑了厚尾的因素,解决了传统VaR低估风险的问题,更接近真实情况;VaR 3种区间估计的方法能够提升对风险衡量的准确性,其中DT方法所得到的区间估计最为准确.

With global competition of the financial sector and financial deregulation , commercial banks are fa-cing increasing operational risk which has become the focus of attention .Therefore, reliable operational risk measurement is becoming increasingly important for commercial banks and other financial institutions .In this paper , nonparametric methods based on heavy-tailed distributions are applied to operational risk measurement . The main advantage of these nonparametric methods is that there are no assumptions made about the shape of loss distributions .It avoids estimate deviation caused by unwittingly mis-specified models .Meanwhile , accord-ing to the characteristics of heavy-tailed distributions , a new method to estimate the mean of loss distributions is put forward , and the adjusted mean focuses more on the tail part of loss distributions .The empirical results demonstrate that the adjusted mean exceeds the sample mean , which is in more conformity with the right heav-y-tailed distributions’ characteristics.This paper employs non-parametric approaches and constructs a consist-ent and unbiased point and interval estimates for VaR .It has overcome the weakness of underestimating of tra-ditional VaR .We discuss three methods of estimating confidence intervals to improve the accuracy of risk measurement .As a consequence , DT （ Data Tilting） interval estimates turned out to be the best .