Interest Rate Forecasts Research Articles

The Relationship Between Interest Rates and Agricultural Commodity Price Dynamics

ABSTRACTThe U.S. Federal Reserve has undertaken several interest rate interventions in the past decade. This study explores the relationship between U.S. corn and soybean prices and Federal Reserve monetary policy interventions, in the short and long run. We use a nonlinear autoregressive distributed lag model to capture three key features of agricultural commodity price responses to changes in interest rates. First, we observe that corn and soybean prices initially overreact to changes in interest rates, overshooting their long‐run equilibrium before gradually reverting. Second, in the short run, the relationship between soybean prices and interest rates is asymmetric; the initial price change is more pronounced for an interest rate increase than for a decrease of the same magnitude. Lastly, both corn and soybean prices display symmetry in the long run. These results help to understand how changes in interest rates affect commodity prices. Assessing the relationship between agricultural commodity prices and interest rates can assist farmers and processors in planning investments and loans based on interest rate forecasts.

Perceptions About Monetary Policy

Abstract We estimate perceptions about the Federal Reserve’s monetary policy rule from panel data on professional forecasts of interest rates and macroeconomic conditions. The perceived dependence of the federal funds rate on economic conditions varies substantially over time, in particular over the monetary policy cycle. Forecasters update their perceptions about the Fed’s policy rule in response to monetary policy actions, measured by high-frequency interest rate surprises, suggesting that they have imperfect information about the rule. Monetary policy perceptions matter for monetary transmission, as they affect the sensitivity of interest rates to macroeconomic news, term premia in long-term bonds, and the response of the stock market to monetary policy surprises. A simple learning model with forecaster heterogeneity and incomplete information about the policy rule motivates and explains our empirical findings.

The Implementation of Holt-Winters Method to Forecast the Loan Interest Rate of Indonesia

This study aimed to anticipate the rupiah loan interest rates at commercial banks in Indonesia by employing the Holt-winters method. This study employs data on rupiah loan interest rates from commercial banks in Indonesia. The data comprises a time series element, with monthly intervals spanning from January 2013 to November 2015, which was obtained from the official website of BPS Indonesia. The study demonstrates that the Holt-winters technique yields the most accurate forecasts, as indicated by a Root Mean Square Error (RMSE) of 0.19720630. The parameters alpha, beta, and gamma, set at 0.6, 0.6, and 0.6 respectively, constitute the optimal configuration for this method. These results indicate that the Holt-winters method is an effective tool for capturing seasonality, trends, and patterns in credit interest rate data, making it a reliable choice for future loan interest rate forecasting. The findings of this study are expected to significantly contribute to strategic decision-making in the banking sector, particularly in risk management and loan interest rate strategy determination.

The term structure of interest rates and economic activity: Evidence from the COVID‐19 pandemic

AbstractThis paper tests the accuracy and predictability of two term structure models using both yields‐only and factor‐augmented specifications focusing on the recent COVID‐19 crisis. In addition, we test the predictive ability of the yield curve on economic activity for the United States and other advanced countries. We provide evidence that models with an enhanced information set, including COVID‐19 factors, improve interest rate forecasts for this period. Also, we point out that term structure models can determine future variations in economic activity but are time‐ and country‐sensitive. Finally, out‐of‐sample analysis reveals that the use of factor‐augmented term structure models, to reflect the current economic and market conditions, improves their forecasting accuracy.

Perceptions about Monetary Policy

We estimate perceptions about the Federal Reserve’s monetary policy rule from panel data on professional forecasts of interest rates and macroeconomic conditions. The perceived dependence of the federal funds rate on economic conditions varies substantially over time, including over the monetary policy cycle. Forecasters update their perceptions about the Fed’s policy rule in response to monetary policy actions, measured by high-frequency interest rate surprises, suggesting that they have imperfect information about this rule. Monetary policy perceptions matter for monetary transmission, as they affect the sensitivity of interest rates to macroeconomic news, term premia in long-term bonds, and the response of the stock market to monetary policy surprises. A simple learning model with forecaster heterogeneity and incomplete information about the policy rule motivates and explains our empirical findings.

A Comprehensive Empirical Evaluation of Biases in Expectation Formation

We revisit predictability of forecast errors in macroeconomic survey data, which is often taken as evidence of behavioral biases at odds with rational expectations. We argue that to reject rational expectations, one must be able to predict forecast errors out of sample. However, the regressions used in the literature often perform poorly out of sample. The models seem unstable and could not have helped to improve forecasts with access only to available information. We do find some notable exceptions to this finding, in particular mean bias in interest rate forecasts, that survive our out-of-sample tests. Our findings help narrow down the set of biases that merit closer attention of researchers in behavioral macroeconomics.

Forward guidance and expectation formation: A narrative approach

SummaryHow forward guidance influences expectations is not fully understood. To study this, I construct central bank data that includes forward guidance and its attributes, central bank projections, and quantitative easing, which I combine with survey data. I describe how, when, and where forward guidance has worked. I estimate that forecasters revised their interest rate forecasts in the intended direction by five basis points on average following a forward guidance change. I provide estimates for The Federal Reserve, European Central Bank, Bank of England, Bank of Canada, Reserve Bank of Australia, Reserve Bank of New Zealand, Sveriges Riksbank, and Norges Bank.

Forecasting Interest Rates and Maturity Indicators in the Russian Banking System

Forecasting Interest Rates and Maturity Indicators in the Russian Banking System

Predicting Exchange Rate under UIRP Framework with Support Vector Regression

This study aimed to forecast the exchange rate between the Vietnamese dong and the US dollar for the following month in the context of the COVID-19 pandemic. It used the Support Vector Regression (SVR) algorithm under the Uncovered Interest Rate Parity (UIRP) theoretical framework; the results are compared with the Ordinary Least Square (OLS) regression model and the Random Walk (RW) model under the rolling window method. The data included the VND/USD exchange rate, the bank interest rate for the 1-month term, and the 1-month T-bill from January 01, 2020, to September 11, 2021. The research discovered a linear link between the two nations' exchange rates and interest rate differentials. Interest rate differentials are input variables to forecast interest rate differentials. Furthermore, the connection between the exchange rate and interest rate differentials during this era does not support the UIRP hypothesis; hence, the error for OLS predictions remains large. The study provided a model to forecast future exchange rates by combining the UIRP theoretical framework and the SVR algorithm. The UIRP theoretical framework can anticipate exchange rate differentials using the input variable and the interest rates between two nations. Meanwhile, the SVR algorithm is a robust machine learning technique that enhances prediction accuracy. Doi: 10.28991/ESJ-2022-06-03-014 Full Text: PDF

Parametric estimation of hidden Markov models by least squares type estimation and deconvolution

This paper develops a simple and computationally efficient parametric approach to the estimation of general hidden Markov models (HMMs). For non-Gaussian HMMs, the computation of the maximum likelihood estimator (MLE) involves a high-dimensional integral that has no analytical solution and can be difficult to approach accurately. We develop a new alternative method based on the theory of estimating functions and a deconvolution strategy. Our procedure requires the same assumptions as the MLE and deconvolution estimators. We provide theoretical guarantees about the performance of the resulting estimator; its consistency and asymptotic normality are established. This leads to the construction of confidence intervals. Monte Carlo experiments are investigated and compared with the MLE. Finally, we illustrate our approach using real data for ex-ante interest rate forecasts.

Forecasting Interest Rates with Shifting Endpoints: The Role of the Demographic Age Structure

Forecasting Interest Rates with Shifting Endpoints: The Role of the Demographic Age Structure

Extension of capital budgeting techniques using interval-valued Fermatean fuzzy sets

Capital budgeting requires dealing with high uncertainty from the unknown characteristics of cash flow, interest rate, and study period forecasts for future periods. Many fuzzy extensions of capital budgeting techniques have been proposed and used in a wide range of applications to deal with uncertainty. In this paper, a new fuzzy extension of the most used capital budgeting techniques is proposed. In this content, first interval-valued Fermatean fuzzy sets (IVFFSs) are defined, and the algebraic and aggregation operations are determined for interval-valued Fermatean fuzzy (IVFF) numbers. The formulations of IVFF net present value, IVFF equivalent uniform annual value, and IVFF benefit-cost ratio (B/C) methods are generated. To validate the proposed methods, proposed formulations are illustrated with a hypothetical example, and the results are compared with classical fuzzy capital budgeting techniques.

The Social Value of Information: A Test of a Beauty and Nonbeauty Contest

AbstractWe demonstrate how the precision of public and private information can be measured and their welfare implications can be assessed. To this end, we develop and apply a procedure to test welfare results from a beauty and nonbeauty contest model based on survey forecasts of interest rates in a large country sample over an extended period of time. In most countries, interest rate forecasts are unbiased. In half of the countries, a higher precision of public information regarding interest rates increases welfare. This applies in particular to small open and emerging market economies. During forward guidance, public information is less precise than private information. On average, forecasters place greater weight on public than private information and rapidly correct errors contained in private information, while errors in public information last over the whole forecast horizon.

Subjective Bond Returns and Belief Aggregation

AbstractThis paper proposes an aggregation scheme of subjective bond return expectations based on the historical accuracy of professional interest rate forecasters. We use disaggregated survey data on bond returns and document large disagreement in the cross-sectional distribution and persistence in forecast accuracy. Our aggregate subjective belief proxy outperforms equal weighting schemes, and its dynamics are significantly different from statistical forecasting models. With this measure in hand, we study the relationship between quantities of risk and subjective expectations of excess returns and demonstrate a strong link between the two, even if such a relationship is difficult to detect using realized returns.Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Quantitative or Qualitative Forward Guidance: Does it Matter?*

Every monetary policy decision by the Reserve Bank of New Zealand (RBNZ) is accompanied by a written statement about the state of the economy and the policy outlook, but only every second decision by a published interest rate forecast. We exploit this difference to study the relative influences of qualitative and quantitative forward guidance. We find that announcements that include an interest rate forecast and announcements that only include written statements lead to very similar market reactions across the yield curve. Our results imply that central bank communication is important, but that the exact form of that communication is less critical.

Interest rate forecasts in Latin America

PurposeThis paper aims to assess the quality of interest rate forecasts for the money markets in Argentina, Brazil, Chile, Mexico and Venezuela for the period between 2001 and 2019. Future interest rate trends are of key significance for many business-related decisions. Thus, reliable interest rate forecasts are essential, for example, for banks that make profits by carrying out maturity transformations.Design/methodology/approachThe data that we analyze were collected by Consensus Economics through a monthly survey with over 120 renowned economists and were published between 2001 and 2019 in the journal Latin American Consensus Forecasts. The authors use the Diebold-Mariano test, the sign accuracy test, the TOTA coefficient and the unbiasedness test to determine the precision and biasedness of the forecasts.FindingsThe research reveals that the forecasting work carried out in Brazil, Chile and Mexico is remarkably successful. The quality of forecasts from Argentina and Venezuela, on the other hand, is significantly poorer.Originality/valueOver 50 studies have already been published with regard to the accuracy of interest rate forecasts, emphasizing the importance of the topic. However, interest rate forecasts for Latin American money markets have hardly been considered thus far. The paper closes this research gap. Overall, the analyzed database amounts to a total of 209 forecast time series with 28,451 individual interest rate forecasts. This study is thus far more comprehensive than all previous studies.

Testing for the rationality of central bank interest rate forecasts

In this paper, we study the bias in interest rate projections of five central banks, namely the central banks of the Czech Republic, New Zealand, Norway, Sweden, and the USA. We examine whether central bank projections are based on an asymmetric loss function and report evidence that central banks perceive an overprojection of their longer-term interest rate forecasts as twice as costly as an underprojection of the same size. We find that forecast rationality is consistent with biased interest rate projections under the assumption of an asymmetric loss function, which contributes to explaining the behavior of the examined central banks and their forecasts.

Estimating robustness

I estimate and evaluate a model with a representative agent who is concerned that the persistence properties of her baseline model of consumption and inflation are misspecified. Coping with model uncertainty, she discovers a pessimistically biased worst-case model that dictates her behavior. I combine interest rates and aggregate macro series with cross-equation restrictions implied by robust control theory to estimate this worst-case distribution and show that (1) the model's predictions about key features of the yield curve are in line with the data, and (2) the degree of pessimism underlying these findings is plausible. Interpreting the worst-case as the agent's subjective belief, I derive model implied interest rate forecasts and compare them with analogous survey expectations. I find that the model can replicate the dynamics and average level of bias found in the survey.

Versatile forward guidance: escaping or switching?

We examine forward guidance when an economy faces negative natural real interest rates and subsequent supply shocks. We introduce two versatile designs: escaping and switching. In the former, the central bank escapes low interest-rate commitment when inflation reaches a self-chosen threshold. In the latter, the central bank can switch from interest-rate forecasts to inflation forecasts any time. Central bankers are scrupulous and face intrinsic (or extrinsic) costs when they deviate from their policy announcements in the future. We show that switching forward guidance is preferable over escaping forward guidance if and only if negative real interest rate shocks are moderate. Furthermore, with the polynomial chaos expansion method and Sobol’ Indices, we identify the decisive parameters and show that our findings are globally robust to parameter uncertainty.

Evaluating survey-based forecasts of interest rates and macroeconomic variables

PurposeThis study examines the forecasting performance of the professional analysts participating in the Blue Chip Economic Indicators Survey using an alternative methodological research design.Design/methodology/approachThis work employs two methodologies, namely a panel specification, with the cross-section being the forecast horizon (from 1-month to 18-months ahead forecasts) and the time period being the time that the forecast was made and a quantile regression technique, which evaluates the hidden nonmonotonic relations between the forecasts and the target variables being forecasted.FindingsThe empirical findings of this study show that survey-based forecasts of certain key macroeconomic variables are generally biased but still efficient predictors of target variables. In particular, we find that survey participants are more efficient in predicting long-term interest rates in the long-run and short-term interest rates in the short run, while the predictability of medium-term interest rates is the least accurate. Finally, our empirical analysis suggests that currency fluctuations are very hard to predict in the short run, while we show that survey-based forecasts are among the most accurate predictors of GDP deflator and growth.Practical implicationsEvaluating the accuracy of economic forecasts is critical since market participants and policymakers utilize such data (as one of several inputs) for making investment, financial and policy decisions. Therefore, the quality of a decision depends, in part, on the quality of the forecast. Our empirical results should have immediate implications for asset pricing models that use interest rates and inflation forecasts as variables.Originality/valueThe present study marks a methodological departure from existing empirical attempts as it proposes a simpler yet powerful approach in order to investigate the efficiency of professional forecasts. The employed empirical specifications enable market participants to investigate the information content of forecasts over different forecast horizons and the temporal evolution of forecast quality.