...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied Economics Letters >Linear and nonlinear financial time series: evidence in a sample of pension funds in Spain and the United Kingdom
【24h】

Linear and nonlinear financial time series: evidence in a sample of pension funds in Spain and the United Kingdom

机译：线性和非线性金融时间序列：西班牙和英国的养老基金样本中的证据

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this article, we examine whether traditional linear models are suitable to assess financial samples, because financial data usually present nonnormality or nonlinear patterns, therefore linear models do not always adequately capture them. For this reason, as returns series usually follow autoregressive patterns, nonlinear models such as Self-Exciting Threshold Autoregressive (SETAR), Logistic STAR (LSTAR), Additive Autoregressive (AAR) or Neural Network (NNET) could provide a good fit. We study whether two samples of pension funds' returns in Spain and the United Kingdom present these features, and we find that the most appropriate model for the Spanish sample is an autoregressive model, but in the United Kingdom sample, we fit a neural network.View full textDownload full textKeywordspension fund, linear model, nonlinear models, autoregressive patternsJEL ClassificationC52, C58, G19, G23Related var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/13504851.2012.669454
机译：在本文中，我们检查了传统的线性模型是否适合评估金融样本，因为金融数据通常呈现非正态或非线性模式，因此线性模型并不总是能够充分捕获它们。因此，由于收益系列通常遵循自回归模式，因此非线性模型（例如自激阈值自回归（SETAR），逻辑STAR（LSTAR），加性自回归（AAR）或神经网络（NNET））可以很好地拟合。我们研究了西班牙和英国的两个养老基金收益样本是否都具有这些特征，我们发现最适合西班牙样本的模型是自回归模型，但是在英国样本中，我们拟合了神经网络。查看全文下载全文关键词养老金，线性模型，非线性模型，自回归模式JEL分类C52，C58，G19，G23相关var addthis_config = {ui_cobrand：“ Taylor＆Francis Online”，services_compact：“ citeulike，netvibes，twitter，technorati，delicious，linkedin， facebook，stumbleupon，digg，google，more“，发布号：” ra-4dff56cd6bb1830b“};添加到候选列表链接永久链接http://dx.doi.org/10.1080/13504851.2012.669454

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号