In Martin-Summer-1090/syslosseval: Provide data and functions to support the analysis in Systemic Loss Evaluation

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(syslosseval)
library(tidyverse)
library(xtable)

The gap between total assets and total eba exposures

2016

total_assets_2016 <- eba_exposures_2016 %>%
    filter(Exposure == "Total assets") %>%
    select(LEI_code, Bank_name, Total_Amount)

total_assets_eba_2016 <- eba_exposures_2016 %>%
    filter(!(Exposure %in% c("Common tier1 equity capital", "Total assets")), Country == "Total") %>%
    group_by(LEI_code, Bank_name) %>%
    summarize(Total_Amount_EBA = sum(Total_Amount, na.rm = F))

asset_sum_compare_2016 <- left_join(total_assets_2016, total_assets_eba_2016, by = c("LEI_code", "Bank_name")) %>%
    arrange(desc(Bank_name)) %>% 
    mutate(abs_diff = Total_Amount_EBA - Total_Amount) %>% 
    mutate(rel_diff = abs_diff/Total_Amount) %>% 
    mutate(hi_lo = if_else(rel_diff > 0, "Above", "Below")) 

p_2016 <- ggplot(data = asset_sum_compare_2016,
              mapping = aes(x = Bank_name, y = rel_diff, fill = hi_lo))

  gaps_2016 <- p_2016 + geom_col() + guides(fill = FALSE) +
          labs(x = NULL, y = "Difference in percent",
          title = "Value gaps",
          subtitle = "Residual Position") +
          theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
          coord_flip()

  ggsave(filename = "../paper/Figures/gaps_2016.png", plot = gaps_2016)

hist_residual_2016 <- ggplot(data = asset_sum_compare_2016, mapping= aes(rel_diff)) + 
  geom_histogram(bins=15, color = "black", fill = "white", boundary = 0)  + 
  labs(x = "Percentage value gap", y = "Number of Banks") +
  geom_vline(xintercept = -0.084115 , color = "red", linetype = "dashed") 

 ggsave(filename = "../paper/Figures/hist_residual_2016.png", plot = hist_residual_2016)

2020

total_assets_2020 <- eba_exposures_2020 %>%
    filter(Exposure == "Total assets") %>%
    select(LEI_code, Bank_name, Total_Amount)

total_assets_eba_2020 <- eba_exposures_2020 %>%
    filter(!(Exposure %in% c("Common tier1 equity capital", "Total assets")), Country == "Total") %>%
    group_by(LEI_code, Bank_name) %>%
    summarize(Total_Amount_EBA = sum(Total_Amount, na.rm = F)) %>% 
    ungroup()

asset_sum_compare_2020 <- left_join(total_assets_2020, total_assets_eba_2020, by = c("LEI_code", "Bank_name")) %>%
    arrange(Bank_name) %>% 
    mutate(abs_diff = Total_Amount_EBA - Total_Amount) %>% 
    mutate(rel_diff = abs_diff/Total_Amount) %>% 
    mutate(hi_lo = if_else(rel_diff > 0, "Above", "Below")) 

p_2020 <- ggplot(data = asset_sum_compare_2020,
              mapping = aes(x = Bank_name, y = rel_diff, fill = hi_lo))

  gaps_2020 <- p_2020 + geom_col() + guides(fill = FALSE) +
          labs(x = NULL, y = "Difference in percent",
          title = "Value gaps",
          subtitle = "Residual Position") +
          theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, size = 0.001))+
          coord_flip()

  ggsave(filename = "../paper/Figures/gaps_2020.png", plot = gaps_2020)

hist_residual_2020 <- ggplot(data = asset_sum_compare_2020, mapping= aes(rel_diff)) + 
  geom_histogram(bins=15, color = "black", fill = "white", boundary = 0)  + 
  labs(x = "Percentage value gap", y = "Number of Banks") +
  geom_vline(xintercept = 0.11 , color = "red", linetype = "dashed")


 ggsave(filename = "../paper/Figures/hist_residual_2020.png", plot = hist_residual_2020)

Panel of exposure categories histogram

panel_2016 <- eba_exposures_2016 %>% 
  filter(!(Exposure %in% c("Total assets", "Common tier1 equity capital")), Country == "Total")

panel_2020 <- eba_exposures_2020 %>% 
  filter(!(Exposure %in% c("Total assets", "Common tier1 equity capital")), Country == "Total")

hist_panel_2016 <- ggplot(data = panel_2016, mapping = aes(x = Total_Amount/1000)) +
  geom_histogram(bins = 20, color = "black", fill = "white", boundary = 0) +
  facet_wrap(~Exposure) + 
  labs(x = "Exposure in billion Euro", y = "Number of Banks")

ggsave(filename = "../paper/Figures/hist_panel_2016.png", plot = hist_panel_2016)

hist_panel_2020 <- ggplot(data = panel_2020, mapping = aes(x = Total_Amount/1000)) +
  geom_histogram(bins = 20, color = "black", fill = "white", boundary = 0) +
  facet_wrap(~Exposure) + 
  labs(x = "Exposure in billion Euro", y = "Number of Banks")

ggsave(filename = "../paper/Figures/hist_panel_2020.png", plot = hist_panel_2020)

Plot of sovereign bond price indices

bond_indices <- sovereign_bond_indices

tsp <- ggplot(data = bond_indices, mapping = aes(x=Date, y=Value))

time_series_plot <- tsp + geom_line(aes(color = Country))

ggsave(filename = "../paper/Figures/bond_indices.png", plot = time_series_plot)

Average daily volumes

table_bonds_2016 <- average_daily_volume_sovereign %>% 
  filter(Year == 2016)

table_bonds_2020 <- average_daily_volume_sovereign %>% 
  filter(Year == 2019)

adv_2016 <- xtable(table_bonds_2016)
print(adv_2016, file = "../paper/Tables/adv_bonds_2016.txt")

adv_2020 <- xtable(table_bonds_2020)
print(adv_2020, file = "../paper/Tables/adv_bonds_2020.txt")

Bond exposures

aggregate_bonds_2016 <- eba_exposures_2016 %>% 
  filter( !(Exposure %in% c("Total assets", "Common equity tier 1 capital")), 
          Country %in% c("DE", "ES", "GB", "FR", "IT", "JP", "US", "Total")) %>% 
  group_by(Country) %>% 
  summarize(Bond_Exposure = sum(Bond_Amount, na.rm = T)) 

tab1 <- aggregate_bonds_2016 %>% 
  filter(Country != "Total")

tot <- aggregate_bonds_2016 %>% 
  filter(Country == "Total") %>% 
  select(Bond_Exposure) %>% 
  rename(Total_Bond_Exposure=Bond_Exposure)

dat_2016 <- bind_cols(tab1, tot) %>% 
  mutate(Share = Bond_Exposure/Total_Bond_Exposure) %>% 
  select(Country, Share)

bond_shares_2016 <- xtable(dat_2016)
print(bond_shares_2016, file = "../paper/Tables/bond_shares_2016.txt")

aggregate_bonds_2020 <- eba_exposures_2020 %>% 
  filter( !(Exposure %in% c("Total assets", "Common equity tier 1 capital")), 
          Country %in% c("DE", "ES", "GB", "FR", "IT", "JP", "US", "Total")) %>% 
  group_by(Country) %>% 
  summarize(Bond_Exposure = sum(Bond_Amount, na.rm = T)) 

tab1 <- aggregate_bonds_2020 %>% 
  filter(Country != "Total")

tot <- aggregate_bonds_2020 %>% 
  filter(Country == "Total") %>% 
  select(Bond_Exposure) %>% 
  rename(Total_Bond_Exposure=Bond_Exposure)

dat_2020 <- bind_cols(tab1, tot) %>% 
  mutate(Share = Bond_Exposure/Total_Bond_Exposure) %>% 
  select(Country, Share)

bond_shares_2020 <- xtable(dat_2020)
print(bond_shares_2020, file = "../paper/Tables/bond_shares_2020.txt")

  bond_exposures_eba_2016 <- eba_exposures_2016 %>%
    filter(Exposure == "Central banks and central governments", 
           Country %in% c("DE", "ES", "FR", "IT", "JP", "GB", "US", "Total")) %>%
    select(LEI_code, Bank_name, Country, Bond_Amount)

  # We bring this into a tabular form with each row a bank and each column a country. Not every bank has an
  # exposure to every country and so the table will have NA for these banks, which in our context is equivalent
  # to a bond exposure of 0, so we substitute NA with zero. In the table we can compute a new variable "Rest_of_the_world",
  # which gives us the differecne of the sum of individual country exposures and the total exposure figure.

  bond_exposures_eba_2016_all <- bond_exposures_eba_2016 %>%
    pivot_wider(names_from = Country, values_from = Bond_Amount) %>%
    mutate_all(~ replace(., is.na(.), 0)) %>%
    mutate(Rest_of_the_world = Total - (DE + ES + FR + IT + JP + GB + US)) %>%
    select(Bank_name, DE, ES, FR, GB, IT, JP, US, Rest_of_the_world) %>% 
    pivot_longer(!Bank_name, names_to = "Country" , values_to = "Bond_Exposure") %>% 
    group_by(Bank_name) %>% 
    mutate(Share = Bond_Exposure/sum(Bond_Exposure)) %>% 
    ungroup()

hist_bond_panel_2016 <- ggplot(data = bond_exposures_eba_2016_all, mapping = aes(x = Share)) +
  geom_histogram(bins = 20, color = "black", fill = "white", boundary = 0) +
  facet_wrap(~Country) + 
  labs(x = "Share in total bond exposure", y = "Number of Banks")

hist_bond_panel_2016

ggsave(filename = "../paper/Figures/hist_bond_panel_2016.png", plot = hist_bond_panel_2016)

bond_exposures_eba_2020 <- eba_exposures_2020 %>%
    filter(Exposure == "Central banks and central governments", 
           Country %in% c("DE", "ES", "FR", "IT", "JP", "GB", "US", "Total")) %>%
    select(LEI_code, Bank_name, Country, Bond_Amount)

  # We bring this into a tabular form with each row a bank and each column a country. Not every bank has an
  # exposure to every country and so the table will have NA for these banks, which in our context is equivalent
  # to a bond exposure of 0, so we substitute NA with zero. In the table we can compute a new variable "Rest_of_the_world",
  # which gives us the differecne of the sum of individual country exposures and the total exposure figure.

  bond_exposures_eba_2020_all <- bond_exposures_eba_2020 %>%
    pivot_wider(names_from = Country, values_from = Bond_Amount) %>%
    mutate_all(~ replace(., is.na(.), 0)) %>%
    mutate(Rest_of_the_world = Total - (DE + ES + FR + IT + JP + GB + US)) %>%
    select(Bank_name, DE, ES, FR, GB, IT, JP, US, Rest_of_the_world) %>% 
    pivot_longer(!Bank_name, names_to = "Country" , values_to = "Bond_Exposure") %>% 
    group_by(Bank_name) %>% 
    mutate(Share = Bond_Exposure/sum(Bond_Exposure)) %>% 
    ungroup()

hist_bond_panel_2020 <- ggplot(data = bond_exposures_eba_2020_all, mapping = aes(x = Share)) +
  geom_histogram(bins = 20, color = "black", fill = "white", boundary = 0) +
  facet_wrap(~Country) + 
  labs(x = "Share in total bond exposure", y = "Number of Banks")

hist_bond_panel_2020

ggsave(filename = "../paper/Figures/hist_bond_panel_2020.png", plot = hist_bond_panel_2020)

Martin-Summer-1090/syslosseval documentation built on Dec. 17, 2021, 3:14 a.m.