Frankel Wei Regression for 2010-2016

FrankelWei_2010_2016.r

## if fxregime is absent from installed packages, download it and load it
if(!require('fxregime')){
  install.packages("fxregime")
}
## load package 
library("fxregime")
# load the necessary data related to exchange rates - 'FXRatesCHF'
# this dataset treats CHF as unit currency

# install / load Quandl
if(!require('Quandl')){
  install.packages("Quandl")
}
library(Quandl)

# Extract and load currency data series with respect to CHF from Quandl

# Extract data series from Quandl. Each Quandl user will have unique api_key
# upon signing up. The freemium version allows access up to 10 fx rate data sets
# USDCHF <- Quandl("CUR/CHF", api_key="p2GsFxccPGFSw7n1-NF9")
# write.csv(USDCHF, file = "USDCHF.csv")

# USDCNY <- Quandl("CUR/CNY", api_key="p2GsFxccPGFSw7n1-NF9")
# write.csv(USDCNY, file = "USDCNY.csv")

# USDJPY <- Quandl("CUR/JPY", api_key="p2GsFxccPGFSw7n1-NF9")
# write.csv(USDJPY, file = "USDJPY.csv")

# USDEUR <- Quandl("CUR/EUR", api_key="p2GsFxccPGFSw7n1-NF9")
# write.csv(USDEUR, file = "USDEUR.csv")

# USDGBP <- Quandl("CUR/GBP", api_key="p2GsFxccPGFSw7n1-NF9")
# write.csv(USDGBP, file = "USDGBP.csv")

# load the data sets into R

USDCHF <- read.csv("G:/China's Economic Woes/USDCHF.csv")
USDCHF <- USDCHF[,2:3]
USDCNY <- read.csv("G:/China's Economic Woes/USDCNY.csv")
USDCNY <- USDCNY[,2:3]
USDEUR <- read.csv("G:/China's Economic Woes/USDEUR.csv")
USDEUR <- USDEUR[,2:3]
USDGBP <- read.csv("G:/China's Economic Woes/USDGBP.csv")
USDGBP <- USDGBP[,2:3]
USDJPY <- read.csv("G:/China's Economic Woes/USDJPY.csv")
USDJPY <- USDJPY[,2:3]

start = 1 # corresponds to 2016-05-12
end = 2272 # corresponds to 2010-02-12

dates <- as.Date(USDCHF[start:end,1])
USD <- 1/USDCHF[start:end,2]
CNY <- USDCNY[start:end,2]/USD
JPY <- USDJPY[start:end,2]/USD
EUR <- USDEUR[start:end,2]/USD
GBP <- USDGBP[start:end,2]/USD

# reverse the order of the vectors to reflect dates from 2005 - 2010 instead of
# the other way around

USD <- USD[length(USD):1]
CNY <- CNY[length(CNY):1]
JPY <- JPY[length(JPY):1]
EUR <- EUR[length(EUR):1]
GBP <- GBP[length(GBP):1]
dates <- dates[length(dates):1]

df <- data.frame(CNY, USD, JPY, EUR, GBP)
df$weekday <- weekdays(dates)
row.names(df) <- dates
df <- subset(df, weekday != 'Sunday')
df <- subset(df, weekday != 'Saturday')
df <- df[,1:5]
zoo_df <- as.zoo(df)


# Code to replicate analysis
cny_rep <- fxreturns("CNY", data = zoo_df, frequency = "daily",
                     other = c("USD", "JPY", "EUR", "GBP")) 
time(cny_rep) <- as.Date(row.names(df)[2:1627])
regx_rep <- fxregimes(CNY ~ USD + JPY + EUR + GBP,
                      data = cny_rep, h = 100, breaks = 10, ic = "BIC")


summary(regx_rep)

## minimum BIC is attained for 2-segment (5-break) model
plot(regx_rep)
round(coef(regx_rep), digits = 3)
sqrt(coef(regx_rep)[, "(Variance)"])

## inspect associated confidence intervals
cit_rep <- confint(regx_rep, level = 0.9)
breakdates(cit_rep)

## plot LM statistics along with confidence interval
flm_rep <- fxlm(CNY ~ USD + JPY + EUR + GBP, data = cny_rep)
scus_rep <- gefp(flm_rep, fit = NULL)
plot(scus_rep, functional = supLM(0.1))
## add lines related to breaks to your plot
lines(cit_rep)

apply(cny_rep,1,function(x) sum(is.na(x)))