syslosseval

The goal of syslosseval is to provide the data and R-functions to support the analysis of the paper “Systemic Loss Evaluation” by Thomas Breuer, Martin Summer and Branko Urosevic. You can download the paper at https://ideas.repec.org/p/onb/oenbwp/235.html#download The code and the data published in this repository support the analysis of this paper.

Installation

This package is not on CRAN. You can only install the the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("Martin-Summer-1090/syslosseval")

Example

When you install the package there will be in total seven datasets available to you. These datasets are:

Dataset number	Dataset name	Data Description
1	eba_exposures_2016	Exposure data from the EBA 2016 stress test
2	eba_exposures_2020	Exposure data from the EBA 2020 transparency exercises
3	eba_impairments_2016	Impairment data from the EBA 2016 stress test
4	eba_impairments_2020	Imputed impairments data based on IMF methods
5	sovereign_bond_indices	Daily values of sovereign bond indices from 2009-2019
6	average_daily_volume_sovereign	Average daily volumes of sovereign bonds from 2009 -2019
7	example_multiple_equilibria	A toy example, where multiple equilibria occur

A detailed description of how the data are compiled is given in the paper in appendix B. Alternatively you can look at the scripts make_balance_sheets_2016.R, make_balance_sheets_2020.R, make_price_volume_data.R and make_2020_impairment_scenarios.R, which are contained in the syslosseval_raw_data.tar.gz in the data-rawfolder of the project source code.

Here is a basic example where you: Prepare a dataframe with exposures and impairments under the one year ahead EBA stress scenario in the EBA 2016 stress test. Prepare all the matrices and vectors needed to make a systemic loss evaluation Compute a fire sale equilibrium for these data.

library(syslosseval)
## basic example code

stress_data <- make_stress_data(eba_exposures_2016, eba_impairments_2016, 1, 2015)
state_variables <- make_state_variables(stress_data)
fixed_point_computation_function(mat = state_variables, lb = 33, data_idx = sovereign_bond_indices, 
                                 data_adv = average_daily_volume_sovereign, base_year = 2015, constant = 1.5)
#> # A tibble: 8 x 7
#>   sec_class       delta_lower iter_lower delta_upper iter_upper delta_max unique
#>   <chr>                 <dbl>      <int>       <dbl>      <int>     <dbl> <lgl> 
#> 1 DE                 0.00492          10    0.00492           9  0.0146   TRUE  
#> 2 ES                 0.000640         10    0.000640          9  0.0191   TRUE  
#> 3 FR                 0.00688          10    0.00688           9  0.0203   TRUE  
#> 4 GB                 0.0106           10    0.0106            9  0.0166   TRUE  
#> 5 IT                 0.0117           10    0.0117            9  0.0322   TRUE  
#> 6 JP                 0.000494         10    0.000494          9  0.000907 TRUE  
#> 7 US                 0.00434          10    0.00434           9  0.00908  TRUE  
#> 8 Rest_of_the_wo…    0.00132          10    0.00132           9  0.00418  TRUE

If you only want to expect particular dataframes, you can do so by writing them to an object. Say you want to inspect the EBA 2016 exposure and impairment data you could do the following:

exposures <- eba_exposures_2016
impairments <- eba_impairments_2016

If you are not familiar with R or you prefer to work rather in Python, Mathematica, Matlab, Excel or any other language you prefer you could export these data to a csv file by writing by using the write.csv() function (or the write.csv2()) depending on the settings of your system), load them into another program and work from there. In this case you do not have available the functions which prepare state variables, compute fixed points etc. If you don’t know how to use write.csv() or write.csv2(), please consult the help functions of R either by using the Help pane in R studio or by typing ?write.csv at the R prompt.