In MichaelGurkov/FinSynch:

For illustration purpose I present in this section the result of the analysis for the case of Israel.

ggplot(country_df %>%
         filter(Country == "Israel") %>%
         select(Date, Total_Credit_real, HousePrice, GDP_real, FD) %>%
         rename(Credit = Total_Credit_real, GDP = GDP_real) %>%
         mutate(GDP = GDP * 10 ^ (-3)) %>%
         gather(key = Indicator, value = Val, -Date),
       aes(x = Date, y = Val, group = Indicator)) +
  geom_line() +
  labs(x = "", y = "", title = "Main indicators for Israel") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90, size = 5),
        plot.title = element_text(hjust = 0.5)) +
  facet_wrap(~Indicator, scales = "free")

The distribution of Israel's bilateral cross border credit is shown in figure \ref{plot_israel_bank_linkages}. Israel's cross border credit is quite concentrated: United states is the largest counterparty followed by Switzerland and United Kingdom.

```r", fig.pos="H"}

net_df = raw_data$bis_lbs %>% filter(grepl("Israel",CountryPair)) %>% filter(Date == as.yearqtr("2018 Q4")) %>% select(CountryPair, Balance) %>% group_by(CountryPair) %>% summarise(Balance = mean(Balance, na.rm = TRUE) * 10 ^ (-3)) %>% ungroup() %>% mutate(CountryPair = gsub("Israel","",CountryPair)) %>% mutate(CountryPair = gsub("-","",CountryPair)) %>% mutate(CountryPair = gsub("\s","",CountryPair)) %>% rename(Country = CountryPair)

ggplot(net_df, aes(x = reorder(Country, Balance), y = Balance)) + geom_bar(stat = "identity") + scale_y_continuous(label = comma) + labs(x = "", y = "Total cross border bilateral credit (USD billions)", title = paste("Israel's total cross border bilateral credit", " counterparties in 2018 Q4")) + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) + coord_flip()

Figure \ref{plot_israel_us} shows the correlation between financial synchronization and banking integration for Israel - United States pair (United States was the largest Israel's counterpart in 2018). In order to control for the effect of other factors I first regress financial synchronization and banking integration on control variables set (including fixed effect). The figure plots the residuals of this first step regressions.

```r", fig.pos="H"}

bank_clean = plm(sub("Fin_synch","bank_gdp",
                     gsub("lag(bank_gdp,1) *","",reg_formula, fixed = TRUE),
                     fixed = TRUE),
                 data = fin_reg_df, index = c("CountryPair","Date"),
                 model = "within", effect = "twoways")

synch_clean = plm(gsub("lag(bank_gdp,1) *","",reg_formula, fixed = TRUE),
                  data = fin_reg_df, index = c("CountryPair","Date"),
                  model = "within", effect = "twoways")

df_clean = list(bank_clean$model$CountryPair,
                bank_clean$residuals, synch_clean$residuals) %>%
  reduce(cbind.data.frame) %>%
  setNames(c("CountryPair","Bank","FinSynch"))

ggplot(df_clean %>%
         filter(CountryPair == "Israel-United_States"),
       aes(x = Bank, y = FinSynch)) +
  geom_point() +
  labs(x = "Banking integration (log of bank credit to gdp)",
       y = "Financial synchronization (percent)",
       title = "Israel-United States \n (netting fixed effects and controls)") +
  geom_smooth(method = "lm", se = FALSE) +
  theme_bw() +
  theme(plot.title = element_text(hjust = 0.5))

The effect of banking linkages in Israel sub sample is similiar to the effect in the total sample. A one standard deviation change in banking linkages reduces the synchronization by r abs(round(coef_vec[names(coef_vec) == "lag(bank_gdp, 1)"] * sd_ratio_isr,2)) standard deviations.

MichaelGurkov/FinSynch documentation built on Oct. 30, 2019, 9:27 p.m.