Interest Rate Forecasting: How It’s Done

Interest rates are like the heartbeat of the economy—when they rise or fall, the ripple effects are felt everywhere, from Wall Street to Main Street. Imagine a world where businesses, investors, and even homebuyers could predict these changes with accuracy. How much easier would it be to make critical financial decisions? This is where the science—and art—of interest rate forecasting comes into play. By analyzing everything from inflation trends to geopolitical events, experts attempt to predict where rates will go next, helping to steer the financial world toward better decision-making. But how exactly do they crack this code?

1. Understanding the Determinants of Interest Rates

Before diving into forecasting methods, it’s important to understand the key factors that influence interest rates. These factors include:

Inflation expectations: Higher inflation typically leads to higher interest rates, as lenders demand compensation for the loss of purchasing power.

Economic growth: A strong economy usually leads to higher interest rates due to increased demand for credit and concerns about inflation.

Central bank policies: Central banks, like the Federal Reserve (Fed), set benchmark rates (e.g., the federal funds rate in the U.S.) and influence rates by adjusting monetary policy.

Global events: Political instability, global trade trends, or financial crises can affect investor sentiment and impact interest rates worldwide.

Now, let’s move on to the different methods used to forecast interest rates.

2. Econometric Models

Econometric models use statistical techniques to establish relationships between interest rates and their key determinants, based on historical data. These models range from simple to highly complex and include:

Single-Equation Models: These models forecast interest rates based on one equation, linking a dependent variable (e.g., future interest rates) to explanatory variables like inflation or GDP growth. For example, a simple regression model may relate the 10-year Treasury yield to inflation and output gaps.

Multivariate Time-Series Models: These models, such as Vector Autoregressions (VAR), consider multiple variables that might influence interest rates and model their interrelationships over time. For instance, a VAR model could include interest rates, inflation, employment, and industrial output, predicting how changes in one variable affect others.

3. The Yield Curve

The yield curve is one of the most commonly used tools for interest rate forecasting. It represents the relationship between interest rates (or yields) and the time to maturity for debt securities like government bonds.

The Term Structure of Interest Rates: Analysts use the yield curve to predict future interest rate movements. For instance, a steepening yield curve (where long-term rates rise faster than short-term rates) suggests that investors expect rising interest rates and inflation in the future. Conversely, an inverted yield curve (where long-term rates are lower than short-term rates) often signals an economic downturn and potential rate cuts.

The Expectations Theory: This theory suggests that long-term interest rates are an average of expected future short-term rates. If investors expect future short-term rates to rise, long-term rates will also rise, and vice versa. By analyzing the current yield curve, economists can estimate how short-term interest rates might evolve.

4. Market-Based Forecasts

Financial markets, particularly the bond and futures markets, offer real-time data that reflects investor expectations about future interest rates.

Fed Funds Futures: One of the most direct tools for forecasting interest rates is the fed funds futures market. These contracts reflect market expectations of the Federal Reserve’s benchmark interest rate, and traders closely watch the implied probabilities of rate hikes or cuts.

Interest Rate Swaps: The swap market, where parties exchange fixed and floating rate interest payments, is another place where interest rate expectations can be inferred. The prices of these swaps reflect market sentiment about future interest rates.

5. Central Bank Guidance and Analysis

Central banks provide forecasts, policy statements, and economic outlooks that influence interest rate predictions.

Forward Guidance: Central banks may explicitly communicate their intentions regarding future interest rate paths. For example, the Fed’s forward guidance often signals its expectations for short-term rates, helping shape market forecasts. While the Fed can and does change its mind, working off a central bank announcement can reduce the amount of guessing a forecaster must do. 

Taylor Rule: Economists sometimes use the Taylor Rule to predict central bank interest rate policy. This rule suggests that interest rates should be adjusted based on inflation and the deviation of actual GDP from potential GDP. By plugging in current data, including the Fed’s inflation target, the rule can provide a forecast of what interest rates might be in the future. 

6. Qualitative Analysis

While many forecasting methods rely on quantitative models, qualitative analysis also plays a key role in predicting interest rates. This involves interpreting economic trends, geopolitical risks, and central bank communications.

Central Bank Meeting Minutes: Analysts study central bank meeting minutes and speeches by policymakers to glean clues about future interest rate decisions. For example, hawkish language may suggest upcoming rate hikes, while dovish statements could indicate potential cuts.

Economic Reports: Key economic indicators like employment reports, inflation data, and GDP growth figures are carefully analyzed. If inflation exceeds the central bank’s target, for example, markets may anticipate rate hikes.

7. Challenges in Forecasting Interest Rates

While these methods and tools offer valuable insights, forecasting interest rates remains challenging due to several factors:

Policy Shifts: Central banks sometimes shift their strategies in response to new data or unforeseen circumstances, deviating from their usual frameworks. The best forecasts have to extend beyond mere statistical techniques and discuss likely changes in Fed and government policy. In fact, in 2016, interest rates shifted because of the outcome of the presidential election—so even election results can matter. 

Conclusion

Interest rate forecasting combines econometric models, market signals, and qualitative analysis. While sophisticated tools like yield curves, futures markets, and econometric models help estimate future rates, uncertainties in the global economy, central bank policies, and unexpected events make it a complex and evolving science. Ultimately, while forecasting interest rates is a complex endeavor influenced by a multitude of factors, it is essential to remember that even the best predictions are subject to unexpected shifts in global and domestic economies. By staying vigilant and adaptive, businesses, policymakers, and investors can use these forecasts not as fixed answers but as tools to navigate the uncertain waters of future economic conditions.

Franklin Carroll

Franklin Carroll is Kukun's VP of Modelling and Analytics; After finishing his studies at the University of Chicago, he worked at Fannie Mae where he helped build Collateral Underwriter, roll out the Value Verify initiative, and specialized in collateral related and credit related policy.

Published September 25, 2024