基于小波-NAR神经网络的气象要素时间序列预测与天气指数彩虹期权估值

被引：16

作者：

黄建风

陆文聪

机构：

[1] 浙江大学管理学院

来源：

系统工程理论与实践 | 2016年 / 05期

基金：

浙江省自然科学基金;

关键词：

天气指数彩虹期权; 天气期权估值; 气象预测; 小波-NAR神经网络;

D O I：

暂无

中图分类号：

P49 [应用气象学]; F831.5 [金融市场];

学科分类号：

摘要：

本文基于小波-NAR神经网络技术,提出气象要素时间序列预测与天气指数彩虹期权估值的原理与方法,同时采用2000-2014年悉尼日均气温和日降雨量数据,进行气象预测与天气期权估值.结果显示:小波-NAR神经网络因灵活的非线性动态结构较好地反映了气象变化特征,其预测与估值效果优于其他模型;该天气期权价值形成中的非线性特征取决于五种经济效应.科学预测天气和估计天气期权价值,开发天气衍生品,可挖掘天气不确定性的经济价值,弱化其对天气敏感产业的影响.

引用

页码：1146 / 1155

页数：10

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