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Comovements in monetary policy

Revealing international correlations with FRED

Reporters and Fed watchers in the U.S. usually think about monetary policy in a domestic framework. But because business conditions, including commodity prices, are correlated internationally, central banks tend to move their policy rates up and down together and their inflation and interest rates tend to be correlated. FRED makes it easy to see these international comovements of macro and policy variables.

The first graph shows comovement in inflation rates from 1970 to the present for four economies: the U.S., Japan, the U.K., and the euro area. Inflation rose in the 1970s as central banks failed to combat the effects of commodity price increases on the general price level and inflation expectations became established.

Before the Financial Crisis of 2007-2009, almost all central banks in the developed world implemented monetary policy mainly by buying and selling short-term bonds to influence short-term interest rates or “policy rates.” The second graph shows the comovement in these policy rates from 1970 to the present for the Federal Reserve, the Bank of Japan, and the Bank of England: These central banks first hiked their policy rates in the 1979-1981 period to combat inflation and were then able to reduce those rates in the 1980s after inflation fell.

The second graph also shows that the Federal Reserve, the Bank of England, and the Bank of Japan lowered their short-term interest rates to zero during the Financial Crisis. To maintain price stability and continue to stimulate their economies, they turned to “unconventional” monetary policies that included buying long-term bonds to reduce long-term interest rates.

The value of the assets of central banks is one (albeit imperfect) way of measuring the monetary stimulus of unconventional policy. The third graph shows the assets of four central banks using an index for their values in 2008. The index value, rather than the value in each respective currency, allows a rough but easy comparison of the relative monetary stimulus. Central bank asset holdings have all increased greatly over the past decades. The Federal Reserve and the Bank of England had the first large responses in 2008-2009. The Bank of Japan began to accumulate assets in earnest starting in 2013. And the European Central Bank did likewise starting in 2015.

How these graphs were created: First graph: Search for “consumer price index for all urban consumers,” select the seasonally adjusted monthly version of the appropriate series, and click “Add to Graph.” From the “Edit Graph” panel’s “Add Line” tab, add the monthly versions of the three series “Consumer Price Index of All Items in Japan,” “Consumer Price Index of All Items in the United Kingdom,” and “Harmonized Index of Consumer Prices: All Items for Euro Area (19 Countries).” For each of these four lines, change the units to “Percent Change from Year Ago.” Lastly, change the start date to 1970-01-01.
Second graph: Search for “effective federal funds rate,” select the appropriate monthly series, and click “Add to Graph.” From the “Edit Graph” panel’s “Add Line” tab, add the monthly versions of the two series “Immediate Rates: Less than 24 hours: Central Bank Rates for Japan” and “Bank of England Policy Rate in the United Kingdom.” Lastly, change the start date to 1970-01-01.
Third graph: Search for “All Federal Reserve Banks: Total Assets,” select the appropriate series, and click “Add to Graph.” From the “Edit Graph” panel’s “Add Line” tab, add the three series “Bank of Japan: Total Assets for Japan,” “Total Central Bank Assets for United Kingdom,” and “Central Bank Assets for Euro Area (11-19 Countries).” For each of these four lines, change the units to “Index (Scale value to 100 for chosen date)” and select the date 2008-01-01. Lastly, change the start date to 2004-01-01.

Suggested by Chris Neely.

View on FRED, series used in this post: BOERUKM, CP0000EZ19M086NEST, CPIAUCSL, ECBASSETS, FEDFUNDS, GBRCPIALLMINMEI, IRSTCB01JPM156N, JPNASSETS, JPNCPIALLMINMEI, UKASSETS, WALCL

Oil prices and breakeven inflation rates revisited

In an earlier FRED Blog post, we highlighted the simultaneous decline in the 5-year breakeven inflation rate and the price of oil in 2014. (The 5-year breakeven inflation rates are obtained from 5-year Treasury inflation-indexed constant maturity securities and are thought to represent the market’s expectation of CPI at a 5-year horizon.) At that time, we argued that markets might have believed that the drop in oil prices reflected a slowing in global demand that might result in a persistent decline in consumer prices. In this post, we make a longer comparison—from 2011 to 2019—between the same two series shown in the original graph.

The graph above shows that the correlation between the breakeven inflation rate and oil prices is not limited to the steep decline that occurred in 2014. Indeed, the correlation between the two series over the entire period shown (January 2011 through March 2019) is 0.65. Prior to 2015, the two series appear to occasionally move together. The comovement was particularly obvious when the two series exhibited large changes, rising together in early 2011, falling together in late 2011, etc. From January 2011 to January 2015, the correlation between the series was 0.49. From January 2015 to March 2019, the correlation between the two series became even more apparent, rising to 0.85.

A few academic papers have tried to analyze the cause of the comovement, but the high degree of correlation between the two series remains puzzling. Even if changes in oil prices pass through to consumer prices, one wouldn’t expect such a close correspondence between oil prices today and consumer prices at a 5-year horizon.

How this graph was made: Search for “crude oil prices,” select the series “Crude Oil Prices: West Texas Intermediate (WTI) – Cushing, Oklahoma” with a daily frequency,  and click “Add to Graph.” From the “Edit Graph” panel, select the “Add Line” option: Search for “5-year breakeven inflation,” select the first series shown (“5-Year Breakeven Inflation Rate, Daily, Percent, NSA”), and add the data series. In the “Format” tab, change the y-axis position from left to right for the breakeven inflation rate and set the start date to 2011-01-01.

Suggested by Michael Owyang and Hannah Shell.

View on FRED, series used in this post: DCOILWTICO, T5YIE

What’s normal for financial data?

"Norming" indicators such as the St. Louis Fed Financial Stress Index

Financial data are useful for many reasons. One (perhaps subtle) reason is that they are never revised. Markets determine the prices and quantities of assets at the time of the transaction and that’s that. As such, once you observe the value of a particular financial variable at a particular point in time, you know it will remain at that value forever.

One might assume, then, that the St. Louis Fed Financial Stress Index, which includes 18 series of financial data to measure stress in the markets, would also remain the same forever. Well, the graph shows us something different: It plots 10 distinct vintages of the index starting with the first, from March 2010, and then one from every year since then. Despite the fact that this index is composed entirely of unrevised financial data, the lines of past data are not exact replicas of each other, so the index clearly changes over time. But the reason for this variation lies in the construction of the index and not in the underlying data.

The index is calculated as a weighted average of the underlying financial series. So, what changes are the weights used in this calculation when new data are released. These weights are functions of the means, variances, and correlations of those 18 financial data series used in the index; and these means, variances, and correlations are recalculated every time another observation is added. In March 2010, the first index was constructed based on data ranging from 1993 through early 2010. In the most recent vintage, from April 2019, the index was constructed based on data ranging from 1993 through early 2019. Since the means, variances, and correlations differ across the two samples, the weights differ as well, and therefore we have different paths for the index.

Why should we care about this? The entire premise of the index is that it is useful for identifying periods of financial stress that are higher or lower than normal. But what’s normal? Take a look at the “Notes” section of the series page for this index: Stress is considered normal when the index takes the value zero. So an index value greater than zero indicates above-normal levels of financial stress and a value less than zero indicates below-normal levels.

Unfortunately, the index’s position above or below zero changes across vintages. For example, consider the index’s value in 1996: The first (March 2010) vintage of the index indicated that financial stress was less than normal in 1996. But this isn’t the case in more-recent vintages, which indicate that financial stress was higher than normal in 1996.

The bottom line is that “normal” is relative to the total sample available. It’s not an absolute statement about financial markets at any given time and shouldn’t be interpreted as such.

How this graph was created: From ALFRED, search for “St. Louis Fed Financial Stress Index.” Check the appropriate series in the search results and click “Add to Graph.” By default, ALFRED creates a bar chart; to change to a line graph, use the “Graph type” menu under the “Format” tab. To include the earliest vintage, click on the “Edit Graph” button, go to the “Edit Line 1” tab, and select vintage 2010-03-05. To add the next vintage, go to the “Add Line” tab, search for and select the St. Louis Fed Financial Stress Index, click “Add data series,” go to the “Edit Line 2” tab, and select vintage 2011-03-03. Repeat this process until all desired vintages have been added. Finally, adjust the range of the plot so the end date is 2010-02-28.

Suggested by Michael McCracken and Joseph McGillicuddy.

View on FRED, series used in this post: STLFSI


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