Neural networks in financial engineering: a study in methodology
Neural networks in financial engineering: a study in methodology
复制标题
金融工程中的神经网络：方法论研究
DOI：
10.1109/72.641449
复制
发表时间：
1997
影响因子：
--
通讯作者：
Y. Bentz
中科院分区：
文献类型：
--
作者：
A. Refenes;A. Burgess;Y. Bentz
神经网络在对金融数据序列建模方面取得了相当大的成功。然而，神经建模的一个主要弱点是缺乏对错误指定的模型执行测试的既定程序，以及对已估计的各种参数进行具有统计意义的测试。在这样的应用中，这是一个严重的劣势，在这种应用中，不仅要测试模型的预测能力或因变量对输入变化的敏感度，而且还要测试在特定置信度水平下发现的统计意义。这一点很少比金融工程更重要，在金融工程中，数据生成过程主要是随机的，只有部分确定性。本文部分是教程，部分是综述，描述了期权定价、协整、利率期限结构和投资者行为模型等方面的典型应用，突出了这些弱点，并提出和评估了一些解决方案。我们描述了几种处理变量选择问题的替代方法，展示了如何使用模型误指定检验，我们采用了一种新的基于协整的方法来处理非平稳性问题，并概括地描述了更符合金融数据序列建模要求的预测神经建模方法。
Neural networks have shown considerable successes in modeling financial data series. However, a major weakness of neural modeling is the lack of established procedures for performing tests for misspecified models, and tests of statistical significance for the various parameters that have been estimated. This is a serious disadvantage in applications where there is a strong culture for testing not only the predictive power of a model or the sensitivity of the dependent variable to changes in the inputs but also the statistical significance of the finding at a specified level of confidence. Rarely is this more important than in the case of financial engineering, where the data generating processes are dominantly stochastic and only partially deterministic. Partly a tutorial, partly a review, this paper describes a collection of typical applications in options pricing, cointegration, the term structure of interest rates and models of investor behavior which highlight these weaknesses and propose and evaluate a number of solutions. We describe a number of alternative ways to deal with the problem of variable selection, show how to use model misspecification tests, we deploy a novel way based on cointegration to deal with the problem of nonstationarity, and generally describe approaches to predictive neural modeling which are more in tune with the requirements for modeling financial data series.
韩国机械工程师学会期刊讲座（1988）。
DOI：
--
发表时间：
--
期刊：
影响因子：
--
作者：
通讯作者：
--