• 向量自回归模型VS风险价值模型(VAR&VaR)


    单从外观上看,VAR&VaR两个模型很容易混淆,但就模型方法和用处两者截然不同,R语言作为数据分析的有力工具,其函数包库中包含各种各样的统计模型。通过vars包可以调用向量自回归模型,通过PerformanceAnalytics包的VaR函数可以调用风险价值模型。

    模型简介

    • library(vars)

      • 向量自回归模型(Vector Autoregression),简称VAR模型,是一种常用的计量经济模型,由克里斯托弗·西姆斯(Christopher Sims)提出。VAR模型是用模型中所有当期变量对所有变量的若干滞后变量进行回归。VAR模型用来估计联合内生变量的动态关系,而不带有任何事先约束条件。它是AR模型的推广,此模型目前已得到广泛应用。
    • library(PerformanceAnalytics)=>VaR()

      • 风险价值模型(Value at Risk),通常被称作VaR方法。VaR按字面的解释就是“处于风险状态的价值”,即在一定置信水平和一定持有期内,某一金融资产或其组合在未来资产价格波动下所面临的最大损失额。JP.Morgan定义为:VaR是在既定头寸被冲销(be neutraliged)或重估前可能发生的市场价值最大损失的估计值;而Jorion则把VaR定义为:“给定置信区间的一个持有期内的最坏的预期损失”。

    向量自回归模型(Vector Autoregression)

    VAR模型R语言实例:

    library(vars)
    library(astsa) #数据包
    x = cbind(cmort, tempr, part)
    plot.ts(x , main = "", xlab = "")
    

    summary(VAR(x, p=1, type="both"))
    
    ## 
    ## VAR Estimation Results:
    ## ========================= 
    ## Endogenous variables: cmort, tempr, part 
    ## Deterministic variables: both 
    ## Sample size: 507 
    ## Log Likelihood: -5116.02 
    ## Roots of the characteristic polynomial:
    ## 0.8931 0.4953 0.1444
    ## Call:
    ## VAR(y = x, p = 1, type = "both")
    ## 
    ## 
    ## Estimation results for equation cmort: 
    ## ====================================== 
    ## cmort = cmort.l1 + tempr.l1 + part.l1 + const + trend 
    ## 
    ##           Estimate Std. Error t value Pr(>|t|)    
    ## cmort.l1  0.464824   0.036729  12.656  < 2e-16 ***
    ## tempr.l1 -0.360888   0.032188 -11.212  < 2e-16 ***
    ## part.l1   0.099415   0.019178   5.184 3.16e-07 ***
    ## const    73.227292   4.834004  15.148  < 2e-16 ***
    ## trend    -0.014459   0.001978  -7.308 1.07e-12 ***
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 
    ## 
    ## Residual standard error: 5.583 on 502 degrees of freedom
    ## Multiple R-Squared: 0.6908,  Adjusted R-squared: 0.6883 
    ## F-statistic: 280.3 on 4 and 502 DF,  p-value: < 2.2e-16 
    ## 
    ## 
    ## Estimation results for equation tempr: 
    ## ====================================== 
    ## tempr = cmort.l1 + tempr.l1 + part.l1 + const + trend 
    ## 
    ##           Estimate Std. Error t value Pr(>|t|)    
    ## cmort.l1 -0.244046   0.042105  -5.796 1.20e-08 ***
    ## tempr.l1  0.486596   0.036899  13.187  < 2e-16 ***
    ## part.l1  -0.127661   0.021985  -5.807 1.13e-08 ***
    ## const    67.585598   5.541550  12.196  < 2e-16 ***
    ## trend    -0.006912   0.002268  -3.048  0.00243 ** 
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 
    ## 
    ## Residual standard error: 6.4 on 502 degrees of freedom
    ## Multiple R-Squared: 0.5007,  Adjusted R-squared: 0.4967 
    ## F-statistic: 125.9 on 4 and 502 DF,  p-value: < 2.2e-16 
    ## 
    ## 
    ## Estimation results for equation part: 
    ## ===================================== 
    ## part = cmort.l1 + tempr.l1 + part.l1 + const + trend 
    ## 
    ##           Estimate Std. Error t value Pr(>|t|)    
    ## cmort.l1 -0.124775   0.079013  -1.579    0.115    
    ## tempr.l1 -0.476526   0.069245  -6.882 1.77e-11 ***
    ## part.l1   0.581308   0.041257  14.090  < 2e-16 ***
    ## const    67.463501  10.399163   6.487 2.10e-10 ***
    ## trend    -0.004650   0.004256  -1.093    0.275    
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 
    ## 
    ## Residual standard error: 12.01 on 502 degrees of freedom
    ## Multiple R-Squared: 0.3732,  Adjusted R-squared: 0.3683 
    ## F-statistic: 74.74 on 4 and 502 DF,  p-value: < 2.2e-16 
    ## 
    ## 
    ## 
    ## Covariance matrix of residuals:
    ##        cmort  tempr   part
    ## cmort 31.172  5.975  16.65
    ## tempr  5.975 40.965  42.32
    ## part  16.654 42.323 144.26
    ## 
    ## Correlation matrix of residuals:
    ##        cmort  tempr   part
    ## cmort 1.0000 0.1672 0.2484
    ## tempr 0.1672 1.0000 0.5506
    ## part  0.2484 0.5506 1.0000
    
    

    风险价值模型(Value at Risk)

    VaR模型R语言实例:

    library(PerformanceAnalytics)
    data(edhec)
    
    # first do normal VaR calc
    
    VaR(edhec, p=.95, method="historical")
    ##     Convertible Arbitrage CTA Global Distressed Securities
    ## VaR              -0.01916    -0.0354             -0.018875
    ##     Emerging Markets Equity Market Neutral Event Driven
    ## VaR        -0.044605             -0.006385     -0.02254
    ##     Fixed Income Arbitrage Global Macro Long/Short Equity Merger Arbitrage
    ## VaR               -0.00929     -0.01624          -0.02544        -0.013455
    ##     Relative Value Short Selling Funds of Funds
    ## VaR      -0.013175      -0.07848      -0.021265
    # now use Gaussian
    
    VaR(edhec, p=.95, method="gaussian")
    ##     Convertible Arbitrage  CTA Global Distressed Securities
    ## VaR           -0.02645782 -0.03471098            -0.0221269
    ##     Emerging Markets Equity Market Neutral Event Driven
    ## VaR      -0.05498927          -0.008761813  -0.02246202
    ##     Fixed Income Arbitrage Global Macro Long/Short Equity Merger Arbitrage
    ## VaR            -0.01900198  -0.02023018       -0.02859264      -0.01152478
    ##     Relative Value Short Selling Funds of Funds
    ## VaR    -0.01493049   -0.08617027    -0.02393888
    # now use modified Cornish Fisher calc to take non-normal distribution into account
    
    VaR(edhec, p=.95, method="modified")
    ##     Convertible Arbitrage  CTA Global Distressed Securities
    ## VaR           -0.03247395 -0.03380228            -0.0274924
    ##     Emerging Markets Equity Market Neutral Event Driven
    ## VaR      -0.06363081           -0.01134637  -0.02812515
    ##     Fixed Income Arbitrage Global Macro Long/Short Equity Merger Arbitrage
    ## VaR             -0.0246791  -0.01548247       -0.03037494      -0.01486869
    ##     Relative Value Short Selling Funds of Funds
    ## VaR    -0.01926435   -0.07431463    -0.02502852
    # now use p=.99
    
    VaR(edhec, p=.99)
    ##     Convertible Arbitrage  CTA Global Distressed Securities
    ## VaR            -0.1009223 -0.04847019           -0.06533764
    ##     Emerging Markets Equity Market Neutral Event Driven
    ## VaR       -0.1397195           -0.04404136  -0.06385154
    ##     Fixed Income Arbitrage Global Macro Long/Short Equity Merger Arbitrage
    ## VaR            -0.05850228  -0.02437999       -0.05508705      -0.03630211
    ##     Relative Value Short Selling Funds of Funds
    ## VaR      -0.050531     -0.122236    -0.05500037
    # or the equivalent alpha=.01
    
    VaR(edhec, p=.01)
    ##     Convertible Arbitrage  CTA Global Distressed Securities
    ## VaR            -0.1009223 -0.04847019           -0.06533764
    ##     Emerging Markets Equity Market Neutral Event Driven
    ## VaR       -0.1397195           -0.04404136  -0.06385154
    ##     Fixed Income Arbitrage Global Macro Long/Short Equity Merger Arbitrage
    ## VaR            -0.05850228  -0.02437999       -0.05508705      -0.03630211
    ##     Relative Value Short Selling Funds of Funds
    ## VaR      -0.050531     -0.122236    -0.05500037
    # now with outliers squished
    
    VaR(edhec, clean="boudt")
    ##     Convertible Arbitrage  CTA Global Distressed Securities
    ## VaR            -0.0192821 -0.03380228           -0.02281122
    ##     Emerging Markets Equity Market Neutral Event Driven
    ## VaR      -0.05335613          -0.006583541  -0.02588255
    ##     Fixed Income Arbitrage Global Macro Long/Short Equity Merger Arbitrage
    ## VaR            -0.01947099  -0.01612116       -0.02997413      -0.01255334
    ##     Relative Value Short Selling Funds of Funds
    ## VaR     -0.0147671   -0.07881339    -0.02474761
    # add Component VaR for the equal weighted portfolio
    
    VaR(edhec, clean="boudt", portfolio_method="component")
    ## $MVaR
    ##            [,1]
    ## [1,] 0.01206124
    ## 
    ## $contribution
    ##  Convertible Arbitrage             CTA Global  Distressed Securities 
    ##           1.189614e-03           7.392667e-05           1.380388e-03 
    ##       Emerging Markets  Equity Market Neutral           Event Driven 
    ##           3.044882e-03           3.255042e-04           1.633369e-03 
    ## Fixed Income Arbitrage           Global Macro      Long/Short Equity 
    ##           1.122597e-03           9.551128e-04           1.725166e-03 
    ##       Merger Arbitrage         Relative Value          Short Selling 
    ##           5.594788e-04           9.422577e-04          -2.647415e-03 
    ##         Funds of Funds 
    ##           1.756359e-03 
    ## 
    ## $pct_contrib_MVaR
    ##  Convertible Arbitrage             CTA Global  Distressed Securities 
    ##            0.098631120            0.006129276            0.114448260 
    ##       Emerging Markets  Equity Market Neutral           Event Driven 
    ##            0.252451840            0.026987629            0.135422963 
    ## Fixed Income Arbitrage           Global Macro      Long/Short Equity 
    ##            0.093074804            0.079188612            0.143033874 
    ##       Merger Arbitrage         Relative Value          Short Selling 
    ##            0.046386511            0.078122792           -0.219497771 
    ##         Funds of Funds 
    ##            0.145620091
    

    反馈与建议

  • 相关阅读:
    node.js简单的服务器
    简单的分页1
    定时跳转
    初始化多个vue实例对象
    js获取验证码的方法
    [z]Java代理(jdk静态代理、动态代理和cglib动态代理)
    .net操作word lib DocX
    git常用命令
    [z]查表空间使用情况
    [z]oracle job
  • 原文地址:https://www.cnblogs.com/shangfr/p/5562314.html
Copyright © 2020-2023  润新知