Estimating market risk with neural networks
Estimating market risk with neural networks
复制标题
使用神经网络估计市场风险
DOI：
--
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
Diagne Mabouba
中科院分区：
文献类型：
--
作者：
Franke Jürgen;Diagne Mabouba
本文研究了具有外生成分的非线性自回归-自回归型离散金融时间序列的随机波动率模型，讨论了如何通过神经网络的最小二乘拟合，或者更一般地，通过具有普适逼近性质的其他参数类的函数，非参数地估计决定该过程的趋势函数和波动率函数。我们证明了一致性的条件下的函数复杂性的增加率的估计。该程序适用于量化市场风险的问题，即计算波动率或风险价值的数据，不仅考虑到时间序列的兴趣，但额外的市场信息。作为应用，我们研究了一些股票价格序列，并将我们的方法与基于GARCH（1，1）模型的常用方法进行了比较
We consider stochastic volatility models for discrete financial time series of the nonlinear autoregressive-ARCH type with exogenous components.We discuss how the trend and volatility functions determining the process may be estimated nonparametrically by least-squares fitting of neural networks or, more generally, of functions from other parametric classes having a universal approximation property. We prove consistency of the estimates under conditions on the rate of increase of function complexity. The procedure is applied to the problem of quantifying market risk, i.e. of calculating volatility or value-at-risk from the data taking not only the time series of interest but additional market information into account. As an application, we study some stock prices series and compare our approach with the common method based on fitting a GARCH(1,1)-model to the data