登录
首页 » matlab » code1

code1

于 2021-04-03 发布
0 244
下载积分: 1 下载次数: 0

代码说明:

说明:  能够使用HAR族模型对金融市场已实现波动率进行建模和预测,并包含相应的MCS检验和DM检验代码。(Can use the har family model to model and forecast the realized volatility of financial market, and contains the corresponding MCS test and DM test code.)

文件列表:

code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\data.xlsx, 1005419 , 2018-03-12
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\DM test\Codes.txt, 5186 , 2018-03-12
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\DM test\DM test - 1M.dta, 275729 , 2017-12-02
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\DM test\DM test - 1W.dta, 279367 , 2017-12-02
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\DM test\DM test -1D.dta, 280223 , 2017-12-02
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\MCS test\R-HMAE.ox, 1966 , 2016-02-23
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\MCS test\R-MAE.ox, 1143 , 2016-02-23
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\MZ regression\mz regression -1d.wf1, 421107 , 2017-11-22
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\MZ regression\mz regression -1m.wf1, 207935 , 2017-11-23
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\MZ regression\mz regression -1w.wf1, 209993 , 2017-11-22
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Linear HAR models\confirm.m, 248 , 2015-12-15
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Linear HAR models\Copy of HARRVTYPE.m, 19127 , 2017-11-19
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Linear HAR models\HARRVTYPE.asv, 18563 , 2017-11-19
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Linear HAR models\HARRVTYPE.m, 18578 , 2017-11-19
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Linear HAR models\icss.m, 740 , 2015-12-15
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Linear HAR models\icss_step2.m, 348 , 2015-12-15
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Linear HAR models\rvdata.mat, 326911 , 2017-11-19
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Linear HAR models\sxdicss.m, 1129 , 2015-12-15
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Linear HAR models\xlbl.m, 124 , 2015-12-15
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Logarithmic HAR models\confirm.m, 248 , 2015-12-15
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Logarithmic HAR models\Copy of HARRVTYPE.m, 19127 , 2017-11-19
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Logarithmic HAR models\HARRVTYPE.asv, 18563 , 2017-11-19
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Logarithmic HAR models\HARRVTYPE.m, 18578 , 2017-11-21
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Logarithmic HAR models\icss.m, 740 , 2015-12-15
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Logarithmic HAR models\icss_step2.m, 348 , 2015-12-15
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Logarithmic HAR models\rvdata.mat, 347859 , 2017-11-21
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Logarithmic HAR models\sxdicss.m, 1129 , 2015-12-15
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Logarithmic HAR models\xlbl.m, 124 , 2015-12-15
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Other benchmark models\confirm.m, 248 , 2015-12-15
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Other benchmark models\HARRVTYPE.m, 27389 , 2017-11-20
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Other benchmark models\icss.m, 740 , 2015-12-15
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Other benchmark models\icss_step2.m, 348 , 2015-12-15
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Other benchmark models\rvdata.mat, 473418 , 2017-11-20
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Other benchmark models\sxdicss.m, 1129 , 2015-12-15
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Other benchmark models\xlbl.m, 124 , 2015-12-15
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Parameter estimation\Codes.txt, 3072 , 2018-03-12
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Parameter estimation\Parameter estimation.dta, 450013 , 2018-03-12
code1\1-s2.0-S0140988318302238-mmc1.zip, 3641124 , 2019-05-28
code1\The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market.pdf, 637323 , 2019-05-28
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Linear HAR models, 0 , 2019-05-28
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Logarithmic HAR models, 0 , 2019-05-28
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction\Other benchmark models, 0 , 2020-01-09
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\DM test, 0 , 2019-05-28
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\MCS test, 0 , 2019-05-28
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\MZ regression, 0 , 2019-05-28
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting\Rolling window prediction, 0 , 2019-05-28
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Out-of-sample forecasting, 0 , 2019-05-28
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief\Parameter estimation, 0 , 2019-05-28
code1\1-s2.0-S0140988318302238-mmc1\Data in Brief, 0 , 2019-06-20
code1\1-s2.0-S0140988318302238-mmc1, 0 , 2019-05-28
code1, 0 , 2019-05-28

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • 聚类-k均值算法
    K-means算法是基于划分的思想,因此算法易于理解且实现方法简单易行,但需要人工选择初始的聚类数目即算法是带参数的。类的数目确定往往非常复杂和具有不确定性,因此需要专业的知识和行业经验才能较好的确定。而且因为初始聚类中心的选择是随机的,因此会造成部分初始聚类中心相似或者处于数据边缘,造成算法的迭代次数明显增加,甚至会因为个别数据而造成聚类失败的现象。(K-means algorithm is based on the idea of partitioning, so the algorithm is easy to understand and the implementation method is simple and feasible, but it requires manual selection of the initial number of clusters, that is, the algorithm is with parameters. The number of classes is often very complex and uncertain, so professional knowledge and industry experience are needed to better determine. Moreover, because the selection of initial clustering centers is random, some initial clustering centers will be similar or at the edge of data, resulting in a significant increase in the number of iterations of the algorithm, and even the phenomenon of clustering failure due to individual data.)
    2020-06-21 17:40:01下载
    积分:1
  • 为了方便大家学习,上传了一个贪心算法的算法代码
    为了方便大家学习,上传了一个贪心算法的算法代码-In order to facilitate them to learn, upload a greedy algorithm algorithms code
    2022-07-03 04:51:30下载
    积分:1
  • 4、学员管理系统_面向对象
    说明:  利用Python和面向对象知识实现学员管理系统(Application of Student Management System with Python)
    2020-06-21 01:40:01下载
    积分:1
  • C# WPF 在窗体上嵌套文字上下滚动的可拖动用户控件
    C# WPF 在窗体上嵌套文字上下滚动的可拖动用户控件,UserControl2.xaml 的交互逻辑。
    2022-10-11 07:55:03下载
    积分:1
  • 传热学陶文铨
    说明:  传热学经典,讲述数值法、解析法,有限差分‘有限元法分析解决工程热问题。’(The classical heat transfer theory is about numerical method, analytical method and finite difference finite element method to solve engineering thermal problems.)
    2019-01-22 22:51:25下载
    积分:1
  • MITKTest
    说明:  在mitk平台下实现三维重建功能,对图片进行分割、处理、面绘制或体绘制(Realization of three-dimensional reconstruction function on MITK platform)
    2019-03-11 20:56:28下载
    积分:1
  • 点击放大的Flash相册
    点击放大的Flash相册,每次刷新页面自动打散图片,以缩略图方式显示,随意点击一张图片,在当前放大显示,双击返回缩略图。带有Flash源文件。
    2022-03-19 15:23:41下载
    积分:1
  • Gobang game code I would like to thank everyone can experience although very old...
    五子棋游戏代码 谢谢大家能够体验一下 虽然很旧 但是还是值得回味的.-Gobang game code I would like to thank everyone can experience although very old but still memorable.
    2023-02-11 19:35:03下载
    积分:1
  • doan_in
    Doan in document with Matlab Code
    2018-12-28 00:35:15下载
    积分:1
  • 滤波算法代码
    一阶低通滤波算法,用于做传感器算法,非常实用(First-order low-pass filtering algorithm)
    2020-06-23 13:40:02下载
    积分:1
  • 696518资源总数
  • 105877会员总数
  • 14今日下载