Most of the available monthly interest data series consist of monthly averages of daily observations. It is well- known that this averaging introduces spurious autocorrelation effects in the first differences of the series. It is exactly this differenced series we are interested in when estimating interest rate risk exposures e.g. This paper presents a method to filter this autocorrelation component from the averaged series. In addition we investigate the potential effect of averaging on duration analysis, viz. when estimating the relationship between interest rates and financial market variables like equity or bond prices. In contrast to interest rates the latter price series are readily available in ultimo month form. We find that combining monthly returns on market variables with changes in averaged interest rates leads to serious biases in estimated correlations (R2s), regression coefficients (durations) and their significance (t-statistics). Our theoretical findings are confirmed by an empirical investigation of US interest rates and their relationship with US equities (S&P 500 Index).

averaging, duration, interest rates, spurious autocorrelation, time series properties
Estimation (jel C13), Time-Series Models; Dynamic Quantile Regressions (jel C22), Methodology for Collecting, Estimating, and Organizing Macroeconomic Data (jel C82), Determination of Interest Rates; Term Structure of Interest Rates (jel E43), General Financial Markets: General (jel G10)
Tinbergen Institute Discussion Paper Series
Tinbergen Institute

Hallerbach, W.G.P.M. (2000). Cross- and Auto-Correlation Effects arising from Averaging: The Case of US Interest Rates and Equity Duration (No. TI 00-064/2). Tinbergen Institute Discussion Paper Series. Retrieved from