Global Dollar Mapping: Indispensable Currency

Posted on Jun 30, 2019

Project GitHub | LinkedIn:   Niki   Moritz   Hao-Wei   Matthew   Oren

The skills we demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

The global US dollar underwrites financial stability in a global economy. Economic actors across the world, from central banks to corporations and pension funds, find increasing reason to hold assets denominated in US dollars circulating through the offshore trading networks that feed through the capital markets of London. At the forefront of these assets are US Treasury bills, which, in representing the debt of the most powerfully taxed state at the center of world economy, play a pivotal role as the investor's ultimate "safe haven" in times of crisis.

This project aims to visualise the build-up of dollar claims and liabilities over the decades as a proxy for dollar flows across national economies. The RShiny app to render this visualisation can be found here, and the code for it here.


The Bank of International Settlements presents a clean set of locational banking data, which aggregates banking activity reported by anonymised banks. The dataset, by tracking counterparties in over 200 countries from the offices of 22 advanced economies, 12 offshore financial centres, and 12 emerging economies, is a rich enough trove to begin approximating the extent of dollar flows.


For this visualisation, we reduce the dataset from over 130000 observations of 190 variables to fewer than 4000 by focusing on counterparty claims and liabilities denominated in the major reserve currencies: US dollars, euros, pounds sterling, yen, and swiss francs.

We normalise claims and liabilities by GDP. We took a world GDP dataset, standardised in 2010 US dollars, from the US Department of Agriculture. To match our primary dataset, we imputed several missing years, cleaned identifying columns, and reformatted the data into a CSV file.

We choose to further normalise our figures into basis points, or one-hundredth of one percent, in order to consistently scale the map between countries with little exposure versus countries highly exposed to the dollar network.


We achieve a visualisation that faithfully cross-references the past few decades of economic history. It paints, particularly, an excellent view of the Great Financial Crisis.

In 2000, the greatest accumulators of US dollar claims are the various offshore banking hubs, the only regions to reach 5000 basis points, or 50% of GDP's worth of claims.

Meanwhile in the United States, a sea change is being forced into the demand-curve for financial "safe assets." China's rise and the associated commodities boom of the 2000s brings with it an insatiable appetite, among emerging economies, for US Treasurys. Domestically the arrival of huge institutional cash pools amassed by corporations, life insurers, and pension funds ahead of an aging Boomer demographic leads to an overabundance of safe asset demand.

Banks, in response, spool up financial engineering techniques polished over decades into a new business model: supplying pseudo-safe assets at scale. The 30-year US mortgage becomes the designated engine of this manufactory.


By 2007, accumulated dollar claims have exploded worldwide. (Note: specific claims can be observed on the RShiny app by hovering over countries). 

The London offices have become the unregulated dealers in mortgage derivatives, leveraging balance sheets to heights that Wall St. can only dream of. This is the age of the truly global bank, scaled to massive size with its businesses spread across the world.

To "roll over" massive claims in USD, a constant inflow of US dollars must be obtained by actors dealing in USD assets. But crisis hits when the system is shaken by the downturn in US housing markets. Funding grows expensive, and then lenders refuse to supply US dollars to counterparties even suspected of insolvency.

A Crisis

Contrary to common impressions, the crisis is not merely an Anglo-Saxon phenomenon. The European banks have mortgaged their Euro assets to borrow in US dollars, joining the bonanza in London. By doing so, they are able to scale up preexisting businesses and lend into infrastructure bubbles across all of Europe; bubbles that far outstrip the American housing market when normalised to GDP.

On the eve of crisis, the European financial system is short $1 trillion USD to continue rolling over their USD-denominated claims. These dollars will be supplied by global emergency swap lines mobilised at the US Federal Reserve. The rest of the world essentially becomes the 13th District of the US Federal Reserve banking system. 

A time-series examination of claims and liabilities in individual countries can be rewarding, as well. The contraction in value of South Korea's dollar claims, from 2008 to 2009, is an excellent example on the risks of integration into the global financial system.

Korean Banks

Korean banks are barely exposed to US mortgage derivatives, but corporations have found it advantageous to hedge their profits via the dollar-funding market, to the order of $150 billion in short-term dollar loans. Once the extent to which corporate finance is entangled in crisis becomes clear, even Korean government-backed banks find themselves cut off from funding markets altogether.

Only a swap line from the Fed to the Korean central bank prevents a severe export-led recession from turning into a depression.

Future Work

We aim to achieve a visualisation of global dollar claims, and do so. Given time, we would segment and aggregate flow patterns in greater detail to conduct network analysis of the global financial system. The locational banking dataset provided by the BIS is versatile, and in combination with their consolidated banking dataset could also yield sectoral analysis of viable banking businesses in the global economic environment.

About Author

Justin L. Ng

The author is an enthusiast of data-driven decision-making. He is a graduate of Rice University, where he studied engineering and economics, and of the Collegiate School.
View all posts by Justin L. Ng >

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI