# Detecting Structural Breaks in China’s FX Regime

##### Edit: This post is in its infancy. Work is still ongoing as far as deriving insight from the data is concerned. More content and economic insight is expected to be added to this post as and when progress is made in that direction.

This is an attempt to detect structural breaks in China’s FX regime using Frenkel Wei regression methodology (this was later improved by Perron and Bai). I came up with the motivation to check for these structural breaks while attending a guest lecture on FX regimes by Dr. Ajay Shah delivered at IGIDR. This is work that I and two other classmates are working on as a term paper project under the supervision of Dr. Rajeswari Sengupta.

The code below can be replicated and run as is, to get same results.

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 ## if fxregime or strucchange package is absent from installed packages, download it and load it if(!require('fxregime')){ install.packages("fxregime") } if(!require('strucchange')){ install.packages("strucchange") } ## load packages library("fxregime") library('strucchange') # load the necessary data related to exchange rates - 'FXRatesCHF' # this dataset treats CHF as unit currency data("FXRatesCHF", package = "fxregime") ## compute returns for CNY (and explanatory currencies) ## since China abolished fixed USD regime cny <- fxreturns("CNY", frequency = "daily", start = as.Date("2005-07-25"), end = as.Date("2010-02-12"), other = c("USD", "JPY", "EUR", "GBP")) ## compute all segmented regression with minimal segment size of ## h = 100 and maximal number of breaks = 10 regx <- fxregimes(CNY ~ USD + JPY + EUR + GBP, data = cny, h = 100, breaks = 10, ic = "BIC") ## Print summary of regression results summary(regx) ## minimum BIC is attained for 2-segment (1-break) model plot(regx) round(coef(regx), digits = 3) sqrt(coef(regx)[, "(Variance)"]) ## inspect associated confidence intervals cit <- confint(regx, level = 0.9) cit breakdates(cit) ## plot LM statistics along with confidence interval flm <- fxlm(CNY ~ USD + JPY + EUR + GBP, data = cny) scus <- gefp(flm, fit = NULL) plot(scus, functional = supLM(0.1)) ## add lines related to breaks to your plot lines(cit)

As can be seen in the figure below, the structural breaks correspond to the vertical bars. We are still working on understanding the motivations of China’s central bank in varying the degree of the managed float exchange rate. EDIT (May 16, 2016):

The code above uses data provided by the package itself. If you wished to replicate this analysis on data after 2010, you will have to use your own data. We used Quandl, which lets you get 10 premium datasets for free. An API key (for only 10 calls on premium datasets) is provided if you register there. Foreign exchange rate data (2000 onward till date) apparently, is premium data. You can find these here.

Here are the (partial) results and code to work the same methodology on the data from 2010 to 2016: This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode characters
 ## 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)))

We got breaks in 2010 and in 2015 (when China’s stock markets crashed). We would have hoped for more breaks (we can still get them), but that would depend on the parameters chosen for our regression.

## One thought on “Detecting Structural Breaks in China’s FX Regime”

1. Ste says:

Excel and CSV files are not available

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