The Federal Reserve Bank of New York works to promote sound and well-functioning financial systems and markets through its provision of industry and payment services, advancement of infrastructure reform in key markets and training and educational support to international institutions.
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We examine the properties of a method for fixing Libor rates that is based on transactions data and multi-day sampling windows. The use of a sampling window may mitigate problems caused by thin transaction volumes in unsecured wholesale term funding markets. Using two partial data sets of loan transactions, we estimate how the use of different sampling windows could affect the statistical properties of Libor fixings at various maturities. Our methodology, which is based on a multiplicative estimate of sampling noise that avoids the need for interest rate data, uses only the timing and sizes of transactions. Limitations of this sampling-window approach are also discussed.