Abstract
This paper uses long-range dependence techniques to analyse two important features of the US Federal Funds effective rate, namely its persistence and cyclical behaviour. It examines annual, monthly, bi-weekly and weekly data, from 1954 until 2010. Two models are considered. One is based on an I(d) specification with AR(2) disturbances and the other on two fractional differencing structures, one at the zero and the other at a cyclical frequency. Thus, the two approaches differ in the way the cyclical component of the process is modelled. In both cases we obtain evidence of long memory and fractional integration. The in-sample goodness-of-fit analysis supports the second specification in the majority of cases. An out-of-sample forecasting experiment also suggests that the long-memory model with two fractional differencing parameters is the most adequate one, especially over long horizons.
Highlights
The Federal Funds rate is the interest rate at which depository institutions in the US lend each other overnight balances held at the Federal Reserve System, which are known as Federal Funds
The rate is negotiated between banks, and its weighted average across all transactions is known as the Federal Funds effective rate
Using recent techniques based on the concept of long-range dependence, in this paper we explicitly model two well-known features of interest rates in general which appear to characterise the Federal Funds rate, namely their persistence and cyclical behaviour, mostly overlooked in previous studies
Summary
The Federal Funds rate is the interest rate at which depository institutions in the US lend each other overnight (normally without a collateral) balances held at the Federal Reserve System (the Fed), which are known as Federal Funds. Sarno, Thornton, and Valente (2005) provide the most extensive study of the forecasting performance of a variety of models of the Federal Funds rate proposed in the literature They consider both univariate (randow walk, ARMA, EGARCH, Markov-switching etc.) and multivariate (M-TAR, BTAR, MS-VECM) specifications, and find that the best forecasting model is a univariate one using the current difference between the effective and the target rate to forecast the future effective rate (combination forecasts only yield marginal improvements). Most of the models found in the literature to describe the behaviour of the Federal Funds rate (and of interest rates in general) assume nonstationarity and are based on first-differenced series This is true, for instance of all the univariate specifications considered in Sarno et al (2005), which imply that the series are I(1), without mean reversion and with permanent effects of shocks.
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