The FRED Blog has discussed how central banks and finance ministries buy and sell foreign currencies to influence the value of their own currencies. Those purchases and sales are called interventions and FRED has data for Australia, Germany, Japan, Italy, Mexico, Switzerland, Turkey, and the United States. Today, we discuss the most recently recorded interventions from the Central Bank of Turkey and compare them with interventions from the Central Bank of Mexico.

The FRED graph above shows the amounts of U.S. dollars bought and sold by the Central Bank of Turkey in red and the Central Bank of Mexico in green. Purchases are recorded as positive values (larger than zero), sales are recorded as negative values (smaller than zero), and the sum totals for each month are the data points shown in the graph.

When a central bank sells U.S. dollars in foreign exchange markets, it increases their supply relative to the domestic currency, intending to prop up the value of that domestic currency. For example, in October 2008, the Central Bank of Mexico sold 3 billion U.S. dollars to address the ongoing depreciation of the peso. But that intervention, and several others that followed, did not prevent the depreciation of the peso over the following 12 months.

In the case of Turkey, the timing of the latest reported central bank intervention in relation to the relative value of the Turkish lira is less obvious: The December 2021 sale of 5 billion U.S. dollars took place while the domestic currency continued on its long path of steady depreciation.

These observations highlight how difficult it can be to interpret data. Obviously, the central banks are intervening to prevent something from happening. If it still happens, is it because the intervention was ineffective? Or would conditions have been even worse without the intervention? A simple graph cannot answer those questions.

How this graph was created: Search for and select “Turkish Intervention: Central Bank of Turkey Purchases of USD (Millions of USD).” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Mexican Intervention: Banco de Mexico Purchases of USD against MXN (Millions of USD).” To change the frequency of the data use the “Edit Line” panel and select “Modify Frequency: Monthly” and “Aggregation method: Sum.”

Suggested by Diego Mendez-Carbajo.

In 2010, the St. Louis Fed introduced its St. Louis Fed’s Financial Stress Index (STLFSI), which quantifies financial stress in the U.S. economy using 18 key indicators of financial market conditions—7 interest rates, 6 yield spreads, and 5 other indicators. This index, of course, can be found in FRED.

The STLFSI uses principal component analysis (PCA) to calculate the “factors” most responsible for the co-movement of several variables. By relying on multiple types of indicators, the STLFSI captures a broad, robust concept of overall financial stress. Just last year, we slightly revised the index’s methodology (creating the “STLFSI 2.0” or “STLFSI2”) to account for trends in several of the series. We’ll be revising the index again, and this post describes the motivations and details of this revision.

The London interbank offered rate, or LIBOR, measures the average interest rate at which major banks lend to each other short-term, unsecured (i.e., non-collateralized) loans. Lending to another private institution always has the risk that the institution will be unable to repay its loans, and the spread between the LIBOR and “riskless” interest rates over the same period helps quantify financial credit market risk. An increase in credit risk, all else equal, will increase the STLFSI.

Two of the indicators used in the STLFSI rely on the LIBOR: the yield difference (“spread”) between the 3-month LIBOR and the overnight index swap (the LIBOR-OIS spread) and the spread between the 3-month Treasury bill and the 3-month LIBOR (the TED spread).

But, starting this year, the LIBOR is being slowly discontinued, and Fed officials have encouraged the use of alternative measures in the meantime.* So, we are revising the STLFSI to account for this change.

Many rates have been suggested by regulators and market participants as a replacement. We, like many, have decided to replace LIBOR with the secured overnight financing rate (SOFR), which tracks the cost of short-term borrowing using transaction data on loans—collateralized by U.S. Treasury securities—in the overnight repo market. Proponents of the SOFR—including the Federal Reserve Bank of New York—argue it is a more accurate measure of bank borrowing costs than the LIBOR. The 90-day average SOFR also closely tracks the 3-month LIBOR.

One difference is that the LIBOR covers unsecured loans, while the SOFR covers secured loans (collateralized with Treasuries). Credit risk matters less in the latter case since the lender receives collateral if the borrower doesn’t pay back the loan. We see this in the graph above, where the SOFR tends to be lower than the LIBOR—reflecting the smaller risk of collateralized lending (and, thus, cost of borrowing). Nonetheless, its movements likely capture some information on changing credit risk since lenders prefer liquid cash over illiquid collateral—as evidenced by the SOFR’s co-movement with LIBOR.

A challenge in switching from LIBOR to SOFR is that the latter has a much smaller number of observations—it begins in 2018. We decided on a simple fix: We estimate what past SOFR spreads would have been, based on the LIBOR rate each day. We do this by calculating simple linear regressions that regress the SOFR spreads on their LIBOR counterparts, using average weekly observations from the SOFR’s introduction through the end of 2021, and use the regression’s estimates in our new STLFSI calculation for the years before the SOFR was introduced.

For the past several weeks, we have been tracking the new STLFSI (3.0) and comparing it with the STLFSI 2.0. As seen in the graph below, the correlation between STLFSI 2.0 and 3.0 is very high, about 0.99.

Still, there are some small but notable differences between the two indices. The biggest period of divergence is the first year or so after the SOFR was introduced (2018-19)—which makes sense, since (as we saw in our first graph) the SOFR initially did not track the LIBOR as closely as it has more recently. More interestingly, the STLFSI 2.0 tended to be slightly higher than the STLFSI 3.0 during the Great Recession, whereas the STLFSI 3.0 has tended to be higher than the STLFSI 2.0 during the COVID pandemic; indeed, it has been consistently about 0.05 index points higher than the STLFSI2 in the last year. Despite these differences, the two indices nonetheless provide consistent signals of above- or below-average financial market stress, with few occasions where one is positive and the other negative. Thus, we are confident that the new STLFSI will continue to serve as a reliable indicator for monitoring financial conditions.

* Former Federal Reserve Governor Randall Quarles noted in a speech last year that the LIBOR would not be available for any new contracts beginning in 2022. Governor Quarles also said that the Fed and other regulators sent a letter to banking organizations they oversee stating that “after 2021, the use of LIBOR in new transactions would pose safety and soundness risks.” These supervised institutions were “encouraged” to seek out an alternative reference rate for new contracts beginning on January 1, 2022. As we discussed above, the recommended alternative reference rate is the SOFR.

Why are the Fed and other regulatory institutions urging financial institutions to discontinue the use of LIBOR? As Governor Quarles and others noted, years after the STLFSI’s release, regulators have highlighted LIBOR’s shortcomings over several years. Quarles stated:

The principal problem with LIBOR is that it was not what it purported to be. It claimed to be a measure of the cost of bank funding in the London money markets, but over time it became more of an arbitrary and sometimes self-interested announcement of what banks simply wished to charge for funds.

How these graphs were created: For the first graph, just search for the St. Louis Financial Stress Index and select the series that is not discontinued. For the second graph, search for “90-day SOFR”: From the “Edit Graph” panel, use the “Add Line” tab to and search for and select “3-month LIBOR.” For the third graph, take the first and add a line searching for “STLFSI2.”

Suggested by Aaron J. Amburgey, Kevin L. Kliesen, Michael W. McCracken, and Devin Werner.

Next Monday we celebrate Dr. Martin Luther King Jr. Day, which honors the man who rose to national recognition beginning with the 1955 Montgomery bus boycotts. Dr. King was a prolific orator and arguably the most prominent and persuasive leader of the U.S. civil rights movement. Less than a week after Dr. King’s assassination, the U.S. House of Representatives voted in favor of the Fair Housing Act of 1968—a law that prohibited discrimination in the renting and purchasing of houses.

How have disparities between White and Black Americans changed since then? To answer part of the question, we look at the gaps in unemployment and homeownership rates between the groups. We define both the unemployment and homeownership gaps as the difference between the Black rate and the White rate. Ideally, both gaps would be equal to zero, indicating no disparity between the two races’ unemployment and homeownership rates.

The difference in unemployment between White and Black Americans is consistently positive, meaning that a higher percentage of Black Americans are unemployed. The gap has fluctuated substantially since the 1970s. The graph (solid black line) doesn’t show any persistent long-run trend, and the unemployment gap is about the same today as it was 50 years ago. The gap peaked in 1983 with a staggering 11.2% higher unemployment rate for Black Americans, and the gap reached its lowest point of 2.8% in 2019. For the past two years, the gap has been 3.9%.

An even larger gap, however, is the one between Black and White homeownership rates (solid blue line). A negative gap in homeownership rates can be interpreted as a lower percentage of Black Americans, compared with White Americans, owning homes. In 2020, Black homeownership was about 30% lower than White homeownership. Unlike the unemployment gap, the homeownership gap does display a long-run trend. Unfortunately, this trend is in the wrong direction—the gap in homeownership rates has increased over time.

Since the early 2000s, the difference in homeownership has gotten progressively starker, with an increase from –26.1% to –30.4% between 2003 and 2020. Because homeownership is an important mechanism for maintaining and growing wealth, these disparities are worrisome.

There has been progress since Dr. King’s passing in the late 1960s, but racial inequalities still exist and leave plenty of room for improvement. Data in FRED can provide one way to measure that progress.

How this graph was created: Search for and select “Unemployment Rate – Black or African American.” From the “Edit Graph” panel, use the “Customize data” search box to select “Unemployment Rate – White” and add and apply the formula (a) – (b). Modify the frequency to quarterly. Next, use the “Add Line” feature to search for and select “Homeownership Rates by Race and Ethnicity: Black Alone in the United-States.” Once again, use the “Customize data” to search for and select the next series, “Homeownership Rates by Race and Ethnicity: Non-Hispanic White Alone in the United States.” Add and apply the formula (a) – (b). Then go to “Format” and under line 2 change “Y-Axis position” to “Right.”

Suggested by Julian Kozlowski and Sam Jordan-Wood.