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Law_Model_analysis.Rmd
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Law_Model_analysis.Rmd
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---
output: html_document
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
rm(list = ls()) # limpiar memoria
options(scipen = 999) # Evitar notacion cientifica
Sys.setlocale("LC_ALL","English")
source("libraries/load_packages.R")
source('libraries/PlotLAW.R')
fecha.salida <- format( as.Date(Sys.Date()) , "%Y%m%d" )
```
```{r load data base}
DBSection4 <- read_csv2("outputs/law_db.csv")
# str(DBSection4)
```
```{r annual data base}
AnnualDB <- DBSection4 %>%
filter(month(date) == 12)
# filter(date>= "2004-12-01")
AnnualDB <- AnnualDB %>%
group_by(wbcode3) %>%
mutate(PNL_valuation_anual = Valuation.effect.monthly.acum - dplyr::lag(x = Valuation.effect.monthly.acum, n = 1),
PNL_carry_cip = CarryCIP.effect.monthly.acum - dplyr::lag(x = CarryCIP.effect.monthly.acum, n = 1),
PNL_carry_embi = CarrySpread.effect.monthly.acum - dplyr::lag(x = CarrySpread.effect.monthly.acum, n = 1),
PNL.total.cip = PNL_valuation_anual + PNL_carry_cip,
PNL_valuation_anual_gdp = PNL_valuation_anual / (MGDP*1000),
PNL_carry_cip_gdp = PNL_carry_cip / (MGDP*1000),
PNL_carry_embi_gdp = PNL_carry_embi / (MGDP*1000),
PNL.total.cip.gdp = PNL.total.cip / (MGDP*1000) )
# names(AnnualDB)
AnnualDB_GDP <- AnnualDB %>%
select(wbcode3, date, PNL_valuation_anual_gdp, PNL_carry_cip_gdp, PNL.total.cip.gdp, PNL_carry_embi_gdp)
AnnualDB_LEVELS <- AnnualDB %>%
select(wbcode3, date, PNL_valuation_anual, PNL_carry_cip, PNL_carry_embi, PNL.total.cip, PNL_valuation_anual_gdp)
```
```{r BOX PLOT}
GatherGDP <- AnnualDB_GDP %>%
select(wbcode3,date, PNL.total.cip.gdp, PNL_carry_embi_gdp) %>%
rename(LAW = PNL.total.cip.gdp) %>%
rename(Self_Insurance = PNL_carry_embi_gdp) %>%
gather(., Methodology, value, 3:4) %>%
mutate(value = value *100)
boxplot_chart <- ggplot(GatherGDP, aes(x=wbcode3, y=value, fill=Methodology))+
geom_boxplot(outlier.colour="black", outlier.shape=16, outlier.size=2)+
theme_minimal() +
# geom_jitter(width = 0.2)+
scale_y_continuous("Percent of GDP") +
labs(x="")+
theme(legend.position="bottom")
# ggsave("outputs/law_boxplot_chart.png", width = 7, height = 4)
```
```{r cuadro resumen cip}
SummaryTable <- AnnualDB_GDP %>%
filter(date != c("2008-12-01", "2009-12-01")) %>%
# filter(wbcode3 == "ARG") %>%
group_by(wbcode3) %>%
summarize(mean_valuation = round(mean(PNL_valuation_anual_gdp, na.rm=TRUE)*100,2),
mean_carry = round(mean(PNL_carry_cip_gdp, na.rm=TRUE)*100,2),
mean_law = round(mean(PNL.total.cip.gdp, na.rm=TRUE)*100,2),
mean_si = round(mean(PNL_carry_embi_gdp, na.rm=TRUE)*100,2))
kable(SummaryTable,
col.names = c("Country", "Valuation", "Carry(1)", "Total","Self Insurance (2)"),
caption = "As % of GDP" ) %>%
footnote(number = c("Proxied by the Covered Interest Parity", "Proxied by EMBI + UST Term Premium (5y-Fed Funds)")) %>%
kable_styling(bootstrap_options = c("striped", "bordered","hover", "responsive"),
full_width = F,
position = "center") %>%
add_header_above(c("", "Leaning Against The Wind" = 3, ""))
```
```{r arg}
SummaryTableArg <- AnnualDB_GDP %>%
# filter(date != c("2008-12-01", "2009-12-01")) %>%
filter(wbcode3 == "ARG") %>%
select (wbcode3, date, PNL_valuation_anual_gdp, PNL_carry_cip_gdp, PNL.total.cip.gdp, PNL_carry_embi_gdp) %>%
mutate(PNL_valuation_anual_gdp = round(PNL_valuation_anual_gdp*100,2),
PNL_carry_cip_gdp = round(PNL_carry_cip_gdp*100,2),
PNL.total.cip.gdp = round(PNL.total.cip.gdp*100,2),
PNL_carry_embi_gdp = round(PNL_carry_embi_gdp*100,2))
kable(SummaryTableArg,
col.names = c("Country", "date", "Valuation", "Carry(1)", "Total","Self Insurance (2)"),
caption = "As % of GDP" ) %>%
footnote(number = c("Proxied by the Covered Interest Parity", "Proxied by EMBI + UST Term Premium (5y-Fed Funds)")) %>%
kable_styling(bootstrap_options = c("striped", "bordered","hover", "responsive"),
full_width = F,
position = "center") %>%
add_header_above(c("","", "Leaning Against The Wind" = 3, ""))
```
```{r chart }
chart1 <- AnnualDB_GDP %>%
select(wbcode3,date, PNL.total.cip.gdp, PNL_valuation_anual_gdp, PNL_carry_cip_gdp) %>%
rename(Total_Result = PNL.total.cip.gdp) %>%
rename(Valuation_Result = PNL_valuation_anual_gdp) %>%
rename(Carry_Result = PNL_carry_cip_gdp) %>%
mutate(date2 = as.Date(format(date, "%Y-01-01"))) %>%
gather(., Reference, value, 4:5) %>%
mutate(value = value *100)
chart1 %>%
ggplot(aes(date2, value))+
geom_col(data=subset(chart1, Reference != "Total_Result"),
aes(fill=Reference),
position = position_stack(reverse = TRUE))+
geom_point(data=subset(chart1, Reference == "Total_Result"),
aes(shape = Reference),
color='black',
size=0.9)+
facet_wrap(~ wbcode3)+
labs(x="",
y="Percent of GDP",
shape=NULL)+
scale_x_date("",date_breaks = "2 year",labels = date_format("'%y"))+
theme_minimal() +
theme(legend.position="bottom",
# plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.border = element_rect(fill = NA, colour="gray40", size=.5),
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major.x = element_line(colour = "gray80",size = .5),
panel.grid.major.y = element_line(colour = "gray80",size = .5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
ggsave("outputs/law_Monthly_P&L.png", width = 7, height = 4)
```
```{r grafico de tasas}
tasas <- DBSection4 %>%
select(wbcode3, date, TNA.Carry, TNA.spread) %>%
rename(CIP = TNA.Carry) %>%
rename(EMBI_USTtm = TNA.spread) %>%
mutate(CIP = CIP*100) %>%
gather(., TNA, value, 3:4)
ggplot(tasas, aes(x=date, y=value, color=TNA))+
geom_line()+
facet_wrap(~wbcode3, scales = "free")+
scale_x_date(labels = date_format("%y"), date_breaks = "2 year","")+
theme(legend.position="top",
axis.line = element_blank())
```
```{r grafico en niveles, eval=FALSE, include=FALSE}
chart_df_2 <- DBSection4 %>%
select(wbcode3, date, NER, Valuation.effect.monthly.acum, CarryCIP.effect.monthly.acum, PNL.total.monthly.acum1) %>%
mutate(Valuation.effect.monthly.acum = Valuation.effect.monthly.acum/1000,
CarryCIP.effect.monthly.acum = CarryCIP.effect.monthly.acum/1000,
PNL.total.monthly.acum1 = PNL.total.monthly.acum1 / 1000)
PlotARG <- LAWPlot(country = 'ARG')
PlotBRA <- LAWPlot(country = 'BRA')
PlotKOR <- LAWPlot(country = 'KOR')
PlotMEX <- LAWPlot(country = 'MEX')
PlotRUS <- LAWPlot(country = 'RUS')
PlotTUR <- LAWPlot(country = 'TUR')
plot_grid(PlotARG,PlotBRA,PlotKOR, PlotMEX, PlotRUS, PlotTUR)
```