├── .gitignore ├── 2022-statistical-modeling.Rproj ├── MES计算 ├── 1数据预处理（python） │ └── 收益率权重计算.ipynb ├── 2MES计算（R） │ └── MES计算-终.R └── 3MES时空维度分析（python） │ └── MES时空维度分析.ipynb ├── README.md ├── 主要分析结果（数据见README.md分享链接） ├── 蓄意攻击下相对网络效率变化.jpg ├── 蓄意攻击下网络最大连通子图尺寸变化.jpg ├── 边际期望损失.jpg ├── 阈值选取参考-终.jpg ├── 随机攻击下相对网络效率变化.jpg └── 随机攻击下网络最大连通子图尺寸变化.jpg └── 复杂网络抗毁性分析 └── 统计建模1343家企业网络.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | .Ruserdata 5 | -------------------------------------------------------------------------------- /2022-statistical-modeling.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | -------------------------------------------------------------------------------- /MES计算/1数据预处理（python）/收益率权重计算.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 70, 6 | "id": "a12d812d", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import pandas as pd\n", 11 | "import numpy as np" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 71, 17 | "id": "9186df9c", 18 | "metadata": { 19 | "scrolled": false 20 | }, 21 | "outputs": [], 22 | "source": [ 23 | "data=pd.read_excel(\"回归数据.xlsx\")\n", 24 | "data=data[[\"证券名称\",\"总资产(元)\"]]\n", 25 | "for i in range(len(data)):\n", 26 | " if data[\"总资产(元)\"][i]=='——':\n", 27 | " data[\"总资产(元)\"][i]=data[\"总资产(元)\"][i-1]" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 72, 33 | "id": "9d08476a", 34 | "metadata": {}, 35 | "outputs": [], 36 | "source": [ 37 | "names=[]\n", 38 | "for i in data.drop_duplicates(subset='证券名称', keep='first', inplace=False)[\"证券名称\"]:\n", 39 | " names.append(i)" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 73, 45 | "id": "a0100b6c", 46 | "metadata": {}, 47 | "outputs": [], 48 | "source": [ 49 | "meandata={}\n", 50 | "for i in names:\n", 51 | " meandata[i]=data[data[\"证券名称\"]==i][\"总资产(元)\"].mean()" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": 74, 57 | "id": "eff48f8a", 58 | "metadata": {}, 59 | "outputs": [], 60 | "source": [ 61 | "weightdata={}\n", 62 | "for i in meandata:\n", 63 | " weightdata[i]=meandata[i]/sum(meandata.values())" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": 75, 69 | "id": "8fcc138b", 70 | "metadata": {}, 71 | "outputs": [], 72 | "source": [ 73 | "returndata=pd.read_excel(\"收益率.xlsx\")" 74 | ] 75 | }, 76 | { 77 | "cell_type": "code", 78 | "execution_count": 76, 79 | "id": "53d51694", 80 | "metadata": { 81 | "scrolled": true 82 | }, 83 | "outputs": [], 84 | "source": [ 85 | "renames=names" 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": 77, 91 | "id": "c52a26da", 92 | "metadata": {}, 93 | "outputs": [], 94 | "source": [ 95 | "renames.insert(0,\"rm\")" 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": 78, 101 | "id": "0504fc33", 102 | "metadata": {}, 103 | "outputs": [], 104 | "source": [ 105 | "renames.insert(0,\"time\")" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 79, 111 | "id": "1d76c507", 112 | "metadata": {}, 113 | "outputs": [], 114 | "source": [ 115 | "returndata_rename=returndata" 116 | ] 117 | }, 118 | { 119 | "cell_type": "code", 120 | "execution_count": 80, 121 | "id": "a8eb6b46", 122 | "metadata": {}, 123 | "outputs": [], 124 | "source": [ 125 | "returndata_rename.columns=renames" 126 | ] 127 | }, 128 | { 129 | "cell_type": "code", 130 | "execution_count": 81, 131 | "id": "2da313be", 132 | "metadata": {}, 133 | "outputs": [], 134 | "source": [ 135 | "rm=[]\n", 136 | "for j in range(len(returndata_rename)):\n", 137 | " a={}\n", 138 | " for i in weightdata:\n", 139 | " a[i]=weightdata[i]*returndata_rename[i][j]\n", 140 | " rm.append(sum(a.values()))\n", 141 | " " 142 | ] 143 | }, 144 | { 145 | "cell_type": "code", 146 | "execution_count": 82, 147 | "id": "035c253f", 148 | "metadata": {}, 149 | "outputs": [], 150 | "source": [ 151 | "returndata_rename[\"rm\"]=rm" 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": 87, 157 | "id": "44732500", 158 | "metadata": { 159 | "scrolled": true 160 | }, 161 | "outputs": [ 162 | { 163 | "data": { 164 | "text/html": [ 165 | "
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timerm万科AST国华ST星源深振业A*ST全新神州高铁中国宝安深物业A...平煤股份潞安环能中海油服中国石油中远海发招商轮船新集能源中远海控大唐发电出版传媒
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..................................................................
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\n", 474 | "
" 475 | ], 476 | "text/plain": [ 477 | " time rm 万科A ST国华 ST星源 深振业A *ST全新 \\\n", 478 | "0 2008-01-02 0.803503 0.523884 1.877400 0.546290 0.887688 1.308386 \n", 479 | "1 2008-01-03 0.612735 -1.054165 1.153791 1.598811 -0.293885 2.078884 \n", 480 | "2 2008-01-04 0.292065 1.128492 0.565601 -0.526424 2.063507 -0.053452 \n", 481 | "3 2008-01-07 0.556617 1.374187 1.421454 1.218541 1.745373 2.139300 \n", 482 | "4 2008-01-08 -0.702735 -0.433864 -0.744411 -1.159734 -1.248321 -0.153011 \n", 483 | "... ... ... ... ... ... ... ... \n", 484 | "3400 2021-12-27 0.174558 -0.044248 -0.551492 0.386042 0.492962 -0.667588 \n", 485 | "3401 2021-12-28 -0.215826 0.132609 1.678423 0.572704 -0.492962 -0.678010 \n", 486 | "3402 2021-12-29 -0.218549 -0.667132 -0.290200 0.000000 0.099041 0.377978 \n", 487 | "3403 2021-12-30 0.138935 -0.586588 0.467826 0.000000 -0.099041 1.041167 \n", 488 | "3404 2021-12-31 0.372867 1.429905 -0.400685 0.189235 0.786075 1.302981 \n", 489 | "\n", 490 | " 神州高铁 中国宝安 深物业A ... 平煤股份 潞安环能 中海油服 \\\n", 491 | "0 0.417929 0.190779 1.050627 ... -0.498708 -0.769223 0.000000 \n", 492 | "1 -0.104106 -0.657619 0.927590 ... 0.103977 -1.116756 2.278829 \n", 493 | "2 2.135471 0.027601 0.130910 ... 0.441484 0.863487 0.203529 \n", 494 | "3 -0.801181 -1.004839 -0.163700 ... 1.587437 4.060347 0.000000 \n", 495 | "4 -1.091764 -1.377007 -2.082863 ... -1.373016 -1.597612 -0.892996 \n", 496 | "... ... ... ... ... ... ... ... \n", 497 | "3400 -3.728908 -0.589619 -0.259060 ... -0.301944 -0.407443 -0.235550 \n", 498 | "3401 -2.782909 1.080006 -0.485250 ... -1.699294 -1.399241 0.847859 \n", 499 | "3402 -1.461316 0.060911 -0.339147 ... 0.470077 0.115147 0.000000 \n", 500 | "3403 1.937695 -0.274774 -0.075727 ... -0.104023 -0.346360 -0.700490 \n", 501 | "3404 2.901464 0.759010 0.751393 ... 0.155941 0.269629 0.700490 \n", 502 | "\n", 503 | " 中国石油 中远海发 招商轮船 新集能源 中远海控 大唐发电 出版传媒 \n", 504 | "0 -0.536347 0.000000 0.603196 0.163371 2.713065 0.000000 2.984877 \n", 505 | "1 0.704353 -0.902932 1.052090 -0.464491 0.485038 0.000000 -0.544261 \n", 506 | "2 0.320206 0.580035 -0.489995 0.464491 2.131538 0.000000 0.363598 \n", 507 | "3 -0.306235 1.102239 0.392437 1.835882 -0.717258 0.000000 0.260718 \n", 508 | "4 -0.153931 -1.791898 -1.021141 -1.781562 0.337406 0.000000 2.937630 \n", 509 | "... ... ... ... ... ... ... ... \n", 510 | "3400 0.087825 0.000000 0.106314 0.090762 0.796893 -0.509442 -0.466629 \n", 511 | "3401 -0.175828 0.395415 0.000000 -1.757768 2.041446 -1.831634 0.267259 \n", 512 | "3402 -0.176543 -0.263210 -0.212890 0.562809 -0.333647 -1.357281 -0.738957 \n", 513 | "3403 -0.088541 -0.397830 0.106575 -0.562809 -1.707800 0.683942 4.133109 \n", 514 | "3404 0.088541 -0.133424 0.212369 -0.570198 -0.023231 0.539503 -0.309108 \n", 515 | "\n", 516 | "[3405 rows x 1345 columns]" 517 | ] 518 | }, 519 | "execution_count": 87, 520 | "metadata": {}, 521 | "output_type": "execute_result" 522 | } 523 | ], 524 | "source": [ 525 | "returndata_rename" 526 | ] 527 | }, 528 | { 529 | "cell_type": "code", 530 | "execution_count": 86, 531 | "id": "4df25879", 532 | "metadata": {}, 533 | "outputs": [], 534 | "source": [ 535 | "returndata_rename.to_excel(\"算MES数据.xlsx\",index = False)" 536 | ] 537 | }, 538 | { 539 | "cell_type": "code", 540 | "execution_count": null, 541 | "id": "e358d21c", 542 | "metadata": {}, 543 | "outputs": [], 544 | "source": [] 545 | } 546 | ], 547 | "metadata": { 548 | "kernelspec": { 549 | "display_name": "Python 3", 550 | "language": "python", 551 | "name": "python3" 552 | }, 553 | "language_info": { 554 | "codemirror_mode": { 555 | "name": "ipython", 556 | "version": 3 557 | }, 558 | "file_extension": ".py", 559 | "mimetype": "text/x-python", 560 | "name": "python", 561 | "nbconvert_exporter": "python", 562 | "pygments_lexer": "ipython3", 563 | "version": "3.8.8" 564 | } 565 | }, 566 | "nbformat": 4, 567 | "nbformat_minor": 5 568 | } 569 | -------------------------------------------------------------------------------- /MES计算/2MES计算（R）/MES计算-终.R: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QingTianLuo/2022-statistical-modeling/24cbe859bf35639c90b0798ebf2631f75af0c691/MES计算/2MES计算（R）/MES计算-终.R -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 2022-statistical-modeling 2 | 本项目主要是对2008年1月1日-2021年12月31日我国1343家非金融企业的系统性风险进行测度并对风险传染机制进行分析，其主要内容包含以下两个部分： 3 | （1）基于DCC-GARCH模型的系统性风险（MES）测度；（2）复杂网络的抗毁性分析。 4 | 其中系统性风险（MES）测度采用R语言完成，复杂网络抗毁性分析采用python完成，此外还涉及一些的数据预处理及分析内容采用python完成。 5 | 由于数据量较大，采用百度网盘形式分享（链接：https://pan.baidu.com/s/1UFV6Pq6XPud3QEvjO1qmHw 提取码：soso） 6 | 相关参考文献及链接如下： 7 | [1]https://www.zybuluo.com/fanxy/note/1654237 8 | [2]刘莹.基于MES测度的我国金融机构系统性风险研究[J].市场研究,2019(10):32-35.DOI:10.13999/j.cnki.scyj.2019.10.011. 9 | [3]卜林,李政.我国上市金融机构系统性风险溢出研究——基于CoVaR和MES的比较分析[J].当代财经,2015(06):55-65.DOI:10.13676/j.cnki.cn36-1030/f.2015.06.007. 10 | [4]宋宁宁,韩华,吴翎燕.一种基于阈值构建金融网络的新方法[J].计算机工程与应用,2015,51(06):249-253. 11 | [5]任英华,谢佳汇,周金龙,张洁莹.基于复杂网络的商业银行流动性风险评价[J].湖南大学学报(社会科学版),2020,34(04):65-73.DOI:10.16339/j.cnki.hdxbskb.2020.04.009. 12 | [6]党亚茹,丁飞雅,高峰.我国航班流网络抗毁性实证分析[J].交通运输系统工程与信息,2012,12(06):177-185.DOI:10.16097/j.cnki.1009-6744.2012.06.001. 13 | [7]赵静,韩永启,周继彪.蓄意攻击下城市轨道交通网络抗毁性分析[J].城市道桥与防洪,2020(06):203-206+25-26.DOI:10.16799/j.cnki.csdqyfh.2020.06.062. 14 | [8]赵军产,王少薇,陆君安,王敬童.疫情背景下全球股市网络的抗毁性及预警研究[J].复杂系统与复杂性科学,2022,19(01):52-59.DOI:10.13306/j.1672-3813.2022.01.007. 15 | [9]微信公众号:单哥的科研日常 -------------------------------------------------------------------------------- /主要分析结果（数据见README.md分享链接）/蓄意攻击下相对网络效率变化.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QingTianLuo/2022-statistical-modeling/24cbe859bf35639c90b0798ebf2631f75af0c691/主要分析结果（数据见README.md分享链接）/蓄意攻击下相对网络效率变化.jpg -------------------------------------------------------------------------------- /主要分析结果（数据见README.md分享链接）/蓄意攻击下网络最大连通子图尺寸变化.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QingTianLuo/2022-statistical-modeling/24cbe859bf35639c90b0798ebf2631f75af0c691/主要分析结果（数据见README.md分享链接）/蓄意攻击下网络最大连通子图尺寸变化.jpg -------------------------------------------------------------------------------- /主要分析结果（数据见README.md分享链接）/边际期望损失.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QingTianLuo/2022-statistical-modeling/24cbe859bf35639c90b0798ebf2631f75af0c691/主要分析结果（数据见README.md分享链接）/边际期望损失.jpg -------------------------------------------------------------------------------- /主要分析结果（数据见README.md分享链接）/阈值选取参考-终.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QingTianLuo/2022-statistical-modeling/24cbe859bf35639c90b0798ebf2631f75af0c691/主要分析结果（数据见README.md分享链接）/阈值选取参考-终.jpg -------------------------------------------------------------------------------- /主要分析结果（数据见README.md分享链接）/随机攻击下相对网络效率变化.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QingTianLuo/2022-statistical-modeling/24cbe859bf35639c90b0798ebf2631f75af0c691/主要分析结果（数据见README.md分享链接）/随机攻击下相对网络效率变化.jpg -------------------------------------------------------------------------------- /主要分析结果（数据见README.md分享链接）/随机攻击下网络最大连通子图尺寸变化.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QingTianLuo/2022-statistical-modeling/24cbe859bf35639c90b0798ebf2631f75af0c691/主要分析结果（数据见README.md分享链接）/随机攻击下网络最大连通子图尺寸变化.jpg --------------------------------------------------------------------------------