This paper extends the benchmark macro-finance (MF) model by introducing, next to the standard macroeconomic factors, additional liquidity-related and return-forecasting factors. Liquidity factors are obtained from a decomposition of the money market spread, while the return-forecasting (risk premium) factor is extracted by imposing a single-factor structure on the 1-period expected excess holding return. The model is estimated on U.S. data using Markov chain Monte Carlo techniques. Two findings stand out. First, the model significantly outperforms most structural and nonstructural MF yield curve models in terms of the cross-sectional fit of the yield curve. Second, financial shocks have a statistically and economically significant impact on the yield curve.

We analyze how time-varying bank-specific capital requirements affect bank lending to the non-financial corporate sector as well as banks' balance sheet adjustments. To do so, we relate Pillar 2 capital requirements to a comprehensive corporate credit register coupled with bank and firm balance sheet data. Our analysis consists of three components. First, we investigate how capital requirements affect the supply of bank credit to the corporate sector, both on the intensive and extensive margin, as well as for different types of credit. Subsequently, we document how bank and firm characteristics as well as the monetary policy stance impact the relationship between bank capital requirements and the supply of credit. Finally, we examine how time-varying bank-specific capital requirements affect banks' balance sheet composition.

We estimate the ‘fundamental’ component of euro area sovereign bond yield spreads, i.e. the part of bond spreads that can be justified by country-specific economic factors, euro area economic fundamentals, and international influences. The yield spread decomposition is achieved using a multi-market, no-arbitrage affine term structure model with a unique pricing kernel. More specifically, we use the canonical representation proposed by Joslin et al. (2011) and introduce next to standard spanned factors a set of unspanned macro factors, as in Joslin et al. (forthcoming). The model is applied to yield curve data from Belgium, France, Germany, Italy, and Spain over the period 2005–2013. Overall, our results show that economic fundamentals are the dominant drivers behind sovereign bond spreads. Nevertheless, shocks unrelated to the fundamental component of the spread have played an important role in the dynamics of bond spreads since the intensification of the sovereign debt crisis in the summer of 2011.

This paper provides evidence on the strategic lending decisions made by banks facing a negative funding shock. Using bank–firm level credit data, we show that banks reallocate credit within their loan portfolio in at least three different ways. First, banks reallocate to sectors where they have a high market share. Second, they also reallocate to sectors in which they are more specialized. Third, they reallocate credit toward low-risk firms. These reallocation effects are economically large. A standard deviation increase in sector market share, sector specialization, or firm soundness reduces the transmission of the funding shock to credit supply by 22%, 8%, and 10%, respectively.