SystemicRisk
Estimates semiparametric systemic risk (CoVaR and MES) regression models and evaluates systemic risk forecasts
Install / Use
/learn @TimoDimi/SystemicRiskREADME
<!-- README.md is generated from README.Rmd. Please edit that file -->
SystemicRisk
<!-- badges: start --> <!-- badges: end -->The SystemicRisk package fits (dynamic) CoVaR and MES Regression Models in the sense that it semiparametrically models the Value at Risk (VaR), i.e., the $\beta$-quantile of $X_t$ jointly with the $\alpha|\beta$-CoVaR or the $\beta$-MES of a series $Y_t$ given $X_t$.
Installation
You can install the development version of SystemicRisk from GitHub with:
# install.packages("devtools")
devtools::install_github("TimoDimi/SystemicRisk")
Example
This is a basic example which shows you how to solve a common problem:
library(quantmod)
#> Loading required package: xts
#> Loading required package: zoo
#>
#> Attaching package: 'zoo'
#> The following objects are masked from 'package:base':
#>
#> as.Date, as.Date.numeric
#> Loading required package: TTR
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
library(tidyr)
library(dplyr)
#>
#> ######################### Warning from 'xts' package ##########################
#> # #
#> # The dplyr lag() function breaks how base R's lag() function is supposed to #
#> # work, which breaks lag(my_xts). Calls to lag(my_xts) that you type or #
#> # source() into this session won't work correctly. #
#> # #
#> # Use stats::lag() to make sure you're not using dplyr::lag(), or you can add #
#> # conflictRules('dplyr', exclude = 'lag') to your .Rprofile to stop #
#> # dplyr from breaking base R's lag() function. #
#> # #
#> # Code in packages is not affected. It's protected by R's namespace mechanism #
#> # Set `options(xts.warn_dplyr_breaks_lag = FALSE)` to suppress this warning. #
#> # #
#> ###############################################################################
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:xts':
#>
#> first, last
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tsibble)
#>
#> Attaching package: 'tsibble'
#> The following object is masked from 'package:zoo':
#>
#> index
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, union
library(SystemicRisk)
# Get data from Yahoo Finance
data_Symbols <- lapply(c("JPM", "^GSPC"), function(x) {
getSymbols(x,
from = "2000/01/01",
to = "2021/12/31",
periodicity = "daily",
auto.assign = FALSE) %>%
data.frame(Date=zoo::index(.), check.names=FALSE) %>%
rename_all(~stringr::str_replace_all(., paste0(x,"."), ""))
})
names(data_Symbols) <- c("JPM", "SP500")
# Collect data as a tsibble with x and y as column names
data_Assets <- dplyr::bind_rows(data_Symbols, .id = "Asset") %>%
dplyr::group_by(Asset) %>%
dplyr::mutate(Date=lubridate::as_date(Date),
NegReturn= -100*(log(Close) - log(lag(Close)))) %>%
dplyr::select(Date, Asset, NegReturn) %>%
stats::na.omit() %>%
tidyr::pivot_wider(names_from=Asset, values_from=NegReturn) %>%
dplyr::rename(x=JPM, y=SP500) %>%
tsibble::as_tsibble(index=Date)
# Fit a CoCAViaR-SAV-fullA model
CoCAViaR_obj <- SRM(data=data_Assets,
model="CoCAViaR_SAV_fullA",
risk_measure="CoVaR",
beta=0.95,
alpha=0.95)
# Get standard errors and print its summary
summary(CoCAViaR_obj)
#>
#> VaR Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.054091 0.160081 0.3379 0.735455
#> lag |X| 0.168976 0.052671 3.2081 0.001344 **
#> lag |Y| 0.178439 0.124771 1.4301 0.152734
#> lag VaR 0.853420 0.059332 14.3837 < 2.2e-16 ***
#>
#> CoVaR Parameter Estimates:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.25853 0.58157 0.4445 0.6567
#> lag |X| 0.11912 0.17040 0.6991 0.4845
#> lag |Y| 0.39401 0.30868 1.2764 0.2019
#> lag CoVaR 0.79223 0.19874 3.9863 6.798e-05 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Plot the time series and estimated (in-sample) VaR/CoVaR
p <- plot(CoCAViaR_obj)
#> Warning: Removed 2 rows containing missing values (`geom_line()`).
## Download VIX index:
data_VIX <- getSymbols("^VIX",
from = "2000/01/01",
to = "2021/12/31",
periodicity = "daily",
auto.assign = FALSE) %>%
data.frame(Date=zoo::index(.), check.names=FALSE) %>%
select(Date, VIX.Close) %>%
rename(VIX = VIX.Close)
#> Warning: ^VIX contains missing values. Some functions will not work if objects
#> contain missing values in the middle of the series. Consider using na.omit(),
#> na.approx(), na.fill(), etc to remove or replace them.
# Merge data frames
data_Assets_VIX <- full_join(data_Assets, data_VIX, by="Date") %>%
mutate(Intercept = 1) %>%
select(Date, x, y, VIX, Intercept) %>%
na.omit()
# Fit a linear MES regression model (with the lagged absolute values of X_t as explanatory variables)
MES_reg <- SRM(data=data_Assets_VIX,
model="joint_linear",
risk_measure="MES",
beta=0.95)
# Get standard errors and print its summary
summary(MES_reg)
#>
#> VaR Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> VIX 0.209109 0.011799 17.7223 < 2.2e-16 ***
#> Intercept -1.092828 0.178286 -6.1296 9.417e-10 ***
#>
#> MES Parameter Estimates:
#> Estimate Std. Error t value Pr(>|t|)
#> VIX 0.124891 0.018235 6.8488 8.255e-12 ***
#> Intercept -0.662195 0.318142 -2.0814 0.03744 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Plot the time series and estimated (in-sample) VaR/MES
p2 <- plot(MES_reg)
#> Warning: Removed 2 rows containing missing values (`geom_line()`).
