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The recent financial crisis has highlighted the limits of the “originate to distribute” model of banking, but its nexus with the macroeconomy and monetary policy remains unexplored. I build a DSGE model with banks (along the lines of Holmström and Tirole [28] and Parlour and Plantin [39] and examine its properties with and without active secondary markets for credit risk transfer. The possibility of transferring credit reduces the impact of liquidity shocks on bank balance sheets, but also reduces the bank incentive to monitor. As a result, secondary markets allow to release bank capital and exacerbate the effect of productivity and other macroeconomic shocks on output and inflation. By offering a possibility of capital recycling and by reducing bank monitoring, secondary credit markets in general equilibrium allow banks to take on more risk. Keywords: Credit Risk Transfer , Dual Moral Hazard , Monetary Policy , Liquidity , Welfare JEL Classification: E3, E5, G3 First Draft: December 2009, This Draft: September 2010
Price pressures
(2010)
We study price pressures in stock prices—price deviations from fundamental value due to a risk-averse intermediary supplying liquidity to asynchronously arriving investors. Empirically, twelve years of daily New York Stock Exchange intermediary data reveal economically large price pressures. A $100,000 inventory shock causes an average price pressure of 0.28% with a half-life of 0.92 days. Price pressure causes average transitory volatility in daily stock returns of 0.49%. Price pressure effects are substantially larger with longer durations in smaller stocks. Theoretically, in a simple dynamic inventory model the ‘representative’ intermediary uses price pressure to control risk through inventory mean reversion. She trades off the revenue loss due to price pressure against the price risk associated with remaining in a nonzero inventory state. The model’s closed-form solution identifies the intermediary’s relative risk aversion and the distribution of investors’ private values for trading from the observed time series patterns. These allow us to estimate the social costs—deviations from constrained Pareto efficiency—due to price pressure which average 0.35 basis points of the value traded. JEL Classification: G12, G14, D53, D61