Perspectives on economics and finances with GFD

The Worst Hyperinflations in History: Hungary

  If you were to ask most people which country suffered the worst inflation in history, they would answer Germany, since Germany’s hyperinflation after World War I is probably the most famous. By 1923 when Germany finally put an end to its hyperinflation, it took 1 trillion old Marks to get 1 new Rentenmark. As devastating as the German inflation was, there were three hyperinflations that made the German case look amateurish: Hungary in 1946, Yugoslavia in 1992-1993 and Zimbabwe from 2004 to 2009. Of these three, Hungary’s was the worst of them all. Hungary was no stranger to hyperinflation. The Austro-Hungarian Empire was on the losing side of World War I and was broken up after the war. The new nation of Hungary lacked the proper government structures, so it turned to printing money to fill the hole in its budget. Before World War I, there were 5 Kronen to the US Dollar, but by 1924 there were 70,000 Kronen to the US Dollar. So Hungary replaced the Kronen with Pengö at the rate of 12,500 Pengö to the Kronen in 1926.

Hungary was spared much of World War II’s destruction until 1944 when it became a battleground between Russia and Germany, and half of Hungary’s industrial capacity was destroyed and 90% was damaged. Transportation was difficult because most of the rail lines and locomotives had been destroyed. What remained had either been taken by the Nazis back to Germany or seized as reparations by the Russians. Prices were already rising in Hungary after the war because production capacity fell due to the destruction. With no tax base to rely upon, the Hungarian government decided to stimulate the economy by printing money. It loaned money to banks at low rates who then loaned the money to companies. The government hired workers directly, they provided loans to consumers, and they gave money to people. The government literally flooded the country with money to get the economy going again. Money may not have grown on trees, but it certainly flowed off the printing presses.
To see how quickly the money supply rose, consider the fact that the currency in circulation stood at 25 billion Pengö in July 1945, rose to 1.646 trillion by January 1946, to 65 quadrillion (million billion) Pengö by May 1946 and to 47 septillion (trillion trillion) Pengö by July 1946. How bad was the inflation? Something that cost 379 Pengö in September 1945, cost 72,330 Pengö by January 1945, 453,886 Pengö by February, 1,872,910 by March, 35,790,276 Pengö by April, 11.267 billion Pengö by May 31, 862 billion Pengö by June 15, 954 trillion Pengö by June 30, 3 billion billion Pengö by July 7, 11 trillion billion Pengö by July 15 and 1 trillion trillion Pengö by July 22, 1946. Obviously, the inflation was devastating to the mathematically challenged. At the height of the inflation, prices were rising at the rate of 150,000% PER DAY. By then, the government had stopped collecting taxes altogether because even a single day’s delay in collecting taxes wiped out the value of the money the government collected. Before the war, in March 1941, there were 5 Pengö to the US Dollar, by June 1944, there were 33 Pengö to the USD and in August 1945 when the real hyperinflation began, there were already 1320 Pengö to the USD. Then, the Pengö collapsed. There were 100,000 Pengö to the USD by November 1945, 1.75 million by March 1946, 59 billion by April 1946, 42 quadrillion by May 1946 and 460 trillion trillion by July 1946. Of course, Hungary had taken some failed measures to reduce the inflation. In December 1945, the government imposed a 75% capital levy by making people turn in 400 Pengö and receive 100 Pengö back with a stamp on the banknotes to indicate they were legal tender. But they didn’t stop printing money. The hyperinflation made it even more difficult for the government to collect taxes, so they introduced the Adopengö which supposedly was indexed to inflation, but even the indexed Adopengö succumbed to the inflation. By July 1946 there were 2 million trillion Adopengö to the Pengö. So how did people cope with this onslaught of money? How did the government that printed the money handle so many zeroes? The solution was simple: change the name of the currency. The Pengö was replaced by the Milpengö (1,000,000 Pengö) which in turn was replaced by the Bilpengö (1,000,000,000,000 Pengö) which was replaced by the inflation-indexed Adopengö.
The banknotes would have the same picture on them, but be a different color. The Milliard Pengö was lavender, the Milliard Milpengö was blue and the Milliard Bilpengö was green, but except for the color, the notes looked alike. Someone who lived through the hyperinflation said they gave up on looking at the denominations and when someone bought something the cashier would say that their bread cost them two blues and a green. The Milliard Bilpengö, pictured here, is the highest denomination note ever printed since it was equal to a Billion Trillion Pengö. Unfortunately, at the end of the inflation, it was only worth about twelve cents USD.

The Forint replaced the Pengö on August 1, 1946 at the rate of 400,000 Quadrillion Pengö to the Forint; however, the stabilization worked, and prices remained relatively stable in Hungary into the 1960s. As for all the old Pengö, they were thrown away because they were worthless. So did the inflation achieve the goal of stimulating production? The hyperinflation did raise Hungary’s industrial capacity, got the railroads moving again, and got much of the capital stock replaced. However, workers lost 80% of their wages and creditors were wiped out. Politically, however, Hungary’s fate was sealed by the Communists, who eventually seized power and turned the Republic of Hungary into the People’s Republic of Hungary in 1949 with a new constitution modelled on that of the Soviet Union.

Global Financial Data Adds over 1600 Exchange Rate Files to the GFDatabase


Global Financial Data is happy to announce that it has added over 1600 new exchange rate files to the GFDatabase, doubling the number of exchange rate files within the database.

All of the new files are cross exchange rates between currencies other than the United States Dollar with over 100 cross rates to the Euro, 70 cross rates to the British Pound, 50 to the Canadian Dollar, and so forth. Virtually all of the series have daily data that extend back to 1900. Many of the series have monthly data that extend back into the 1800s. If you know the currency codes that are used by the ISO, you can put together the symbols for these currencies. The first three letters are the ISO currency code for the currency the data are in terms of, while the second group of three letters is how many currency units are needed to obtain the primary currency. As an example, CADEUR would tell you how many Euros are obtained for a Canadian Dollar. EURCAD would tell you how many Canadian Dollars are obtained for a Euro.

These exchange rates are all listed in the Main Indicators as Canadian Dollar Exchange Rate, Euro Exchange Rate, so you can get quick lists of the cross exchange rates available for each currency. If you would like a full list of these additions to the GFDatabase, please feel free to contact your sales representative.

Government Benchmark Bond and Total Return Series Extended and Updated

Global Financial Data has updated a number of its long-term government bond series. These updates include both the 10-year Benchmark Bond files as well as the Total Return Series for Government Bonds. Data for several of these countries, such as Austria, Denmark, France, the Netherlands, Russia, Spain and Sweden now extend back to the 1700s. Other series have been extended further back into the 1800s and have improved their granularity. During the past few years, Global Financial Data has added extensively to its Fixed Income Securities Database which now includes over 6000 files. This database includes data for both corporate and government bonds from New York, London, Paris, Amsterdam and other exchanges. All of these exchanges were international in their coverage, listing bonds from countries throughout the world.
The data for many of these bonds was unavailable when these files were originally created. Using the extensive histories on individual securities the Fixed Income Security Database now includes, we have updated the data in the Benchmark Bond and Total Return Series. This not only allowed us to extend many of the series back in time, but also allowed us to improve the periodicity of the data, providing monthly data where only annual data were available before. This has also led to recalculation of the historical values for some of the Total Return Series. Global Financial Data continues to search for data sources that allow us to provide data to our customers that are unavailable anywhere else. Should you have any questions about these series, please feel free to contact your sales representative.

GFD Announces Data Partnership with Updata to Provide Superior Technical Analysis

Global Financial Data is pleased to announce its partnership with Updata to provide its long-term, historical data series through their software platform. Updata is the leader in providing tools that allow unparalleled technical analysis of data from other sources. Through Updata, GFD’s subscribers can use the multitude of tools that Updata offers to analyze, backtest and screen the extensive data series that GFD provides to its clients.
Updata gives end-users of Global Financial Data an array of tools to maximize use of the data. The integration is seamless and provides the following core possibilities:
  1. The widest range of high-end technical analysis techniques
  2. Quick and easy manipulation and visualization of data sets
  3. The ability to cross compare and analyze with data from over 20 other vendors
  4. Powerful back-testing capability – with over 800 pre-written indicators and studies
  5. Productivity tools for faster report writing and publishing
The GFDatabase includes data on over 100,000 series with data back to the 1200s covering total returns for asset allocation, commodities, economic data, equities, exchange rates, fixed income, government and debt, international trade and national income statistics. GFD also offers the most extensive equity databases for the United States and the United Kingdom available anywhere. The U.S. Stocks Database includes over 50,000 securities going back to 1789 covering all regional and national exchanges. The U.K. Stocks Database includes over 15,000 securities from the London Stock Exchange and extends back to 1692. “GFD is excited about partnering with Updata. The combination of the most extensive financial database available anywhere and Updata’s unique charting capabilities will give its users the ability to do analysis most traders could only dream about.” Bryan Taylor, CEO, Global Financial Data “Updata is very excited to bring out of the box analytics on top of Global Financial Data. It really does maximize the true value of this unique data set with the best analytical tools available. I have been running through the data myself in recent weeks and find more and more fascinating charts as I go. I wouldn’t want to be without GFD in my system now.” David Linton, CEO, Updata  


Global Financial Data was founded 20 years ago to provide the most complete, long-term financial and economic data series available anywhere. The GFDatabase is used by hundreds of money managers and by major universities throughout the world. Data from GFD has been used in over a thousand academic articles and books, including the research of Nobel Prize winners. The company’s headquarters is in San Juan Capistrano, California.

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Our comprehensive financial databases span global markets offering data never compiled into an electronic format. We create and generate our own proprietary data series while we continue to investigate new sources and extend existing series whenever possible. GFD supports full data transparency to enable our users to verify financial data points, tracing them back to the original source documents. GFD is the original supplier of complete historical data.