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.

## if fxregime or strucchange package is absent from installed packages, download it and load it
## load packages
# 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
## minimum BIC is attained for 2-segment (1-break) model
round(coef(regx), digits = 3)
sqrt(coef(regx)[, "(Variance)"])
## inspect associated confidence intervals
cit <- confint(regx, level = 0.9)
## 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

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:


## if fxregime is absent from installed packages, download it and load it
## load package
# load the necessary data related to exchange rates - 'FXRatesCHF'
# this dataset treats CHF as unit currency
# install / load 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")
USDCNY <- read.csv("G:/China's Economic Woes/USDCNY.csv")
USDEUR <- read.csv("G:/China's Economic Woes/USDEUR.csv")
USDGBP <- read.csv("G:/China's Economic Woes/USDGBP.csv")
USDJPY <- read.csv("G:/China's Economic Woes/USDJPY.csv")
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")
## minimum BIC is attained for 2-segment (5-break) model
round(coef(regx_rep), digits = 3)
sqrt(coef(regx_rep)[, "(Variance)"])
## inspect associated confidence intervals
cit_rep <- confint(regx_rep, level = 0.9)
## 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
apply(cny_rep,1,function(x) sum(

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.



Machine Learning — New Coursera Specialization from the University of Washington

I have finally embarked on my first machine learning MOOC / Specialization. I love Python, and this course uses Python as the language of choice. Also, the instructors assert that Python is widely used in industry, and is becoming the de facto language for data science in industry. They use IPython Notebook in their assignments and videos.

The specialization offered by the University of Washington consists of 5 courses and a capstone project spread across about 8 months (September through April). The specialization’s first iteration kicked off yesterday.

washingtonMachineLearningThe first course, Machine Learning Foundations: A Case Study Approach is 6 weeks long, running from September 22 through November 9.

The Instructors:

Emily Fox and Carlos Guestrin

Key Learning Outcomes
– Identify potential applications of machine learning in practice.
– Describe the core differences in analyses enabled by regression, classification, and clustering.
– Select the appropriate machine learning task for a potential application.
– Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
– Represent your data as features to serve as input to machine learning models.
– Assess the model quality in terms of relevant error metrics for each task.
– Utilize a dataset to fit a model to analyze new data.
– Build an end-to-end application that uses machine learning at its core.
– Implement these techniques in Python.

Week 1: Introductory welcome videos and the instructors’ views on the future of intelligent applications
Week 2: Predicting House Prices (Regression)
Week 3: Classification (Sentiment Analysis)
Week 4: Clustering and Similarity: Retrieving Documents
Week 5: Recommending Products
Week 6: Deep Learning: Searching for Images


It’s been 3 days since the course began, and here’s how the classmate demographic looks like: