The interest rate term structure responds to shocks of all frequencies. For example, shocks to inflation are empirically associated with relatively equal changes in interest rate across all maturities, indicating a long-run impact. By contrast, shocks to real output growth tend to affect the short end of the yield curve moreso than long-term rates, suggesting a more intermediate horizon. Likewise, the monetary policies of central banks have historically most directly impacted short-term rates, but their effects can spread across the term structure through their influence on market expectations of future short rate movements (e.g., Balduzzi, Bertola, and Foresi (1997), Piazzesi (2005), and Heidari and Wu (2009)). At higher frequencies, large transactions of a particular fixed-income instrument can significantly move rates at the associated maturities, followed by quick dissipation along the yield curve through hedging practices.
In the dynamic term structure literature, no-arbitrage conditions impose strong restrictions on the cross-sectional relation between interest rates of different maturities. Despite the rapid progress in this literature over the past decade, the focus of empirical work remains on low-dimensional models, most typically with three factors. Consideration of substantially higher dimensional affine models has not previously been considered practical because of the classic curse of dimensionality that plagues model identification. A generic three-factor model can have over 20 free parameters, many of which cannot be estimated with statistical significance, and the number of parameters grows approximately quadratically with the factor dimension. However, restricting attention to low-dimensional models may inhibit empirical performance in several areas. First, low-dimensional models face limitations in the cross-sectional fitting of observed interest rates across different maturities. Although the fitting errors can appear small relative to the average interest rate level, the errors can become economically significant when one forms interest rate portfolios to neutralize the exposure to low-frequency movements (Bali, Heidari, and Wu (2009)) and similarly impact the pricing of interest rate options (Heidari and Wu (2008)). Second, low-dimensional term structure models often imply high cross-correlations between interest rates changes of different maturities, but the actual cross correlation estimates are often much lower (Dai and Singleton (2002)). Finally, low-dimensional models generate poor forecasting performances, often worse than the performance of a simple random walk assumption (Duffee (2002)).
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Much theory attempts to find dimensions of “bank specialness,” typically from a synergy of combining deposits with loans. Banks’ role as delegated monitor explains why they tend to hold large and well diversified loan portfolios, and why they tend to fund themselves mostly with debt (Diamond, 1984). Bank deposits – their main source of debt – tend to be short term and subject to a ‘sequential service constraint’, meaning that priority of payment comes on a first-come, first-served basis. This unique capital structure stems from bank-loan opaqueness. Loans make “bad” collateral because outsiders can not value them; by subjecting themselves to the possibility of a run, banks increase their borrowing capacity against their loans (Calomiris and Kahn, 1991; Flannery, 1994; Diamond and Rajan, 2001).
Fama (1985) argues that liquidity production helps explain ‘what’s different about banks’. Private information from the business checking account gives banks an advantage in lending over other intermediaries. Banks provide liquidity not only to demand depositors, however, but also to borrowers via lines of credit and un-drawn loan commitments (we use these terms interchangeably). Both contracts allow customers to receive cash on demand. The liquidity insurance role of banks exposes them to the risk that they will have insufficient cash to meet random demands from their depositors or borrowers. This paper shows that banks reduce their liquidity risk by combining transactions deposit with loan commitments, thus helping explain a key feature of the industry.
