2017-03 | With Krainer and Shapiro | November 2017
Using detailed bank balance sheet data we examine how banks respond to a net worth shock. We make use of variation in banks’ loan exposure to industries adversely affected by the oil price declines of 2014 and the implied variation in losses resulting from credit deterioration in those industries. In response to these losses, exposed banks reduced the risk of their balance sheets by shifting away from portfolio lending and towards assets with lower risk weights. Banks tightened credit on corporate lending and on mortgages that they would ultimately hold in their portfolio. However, they expanded credit for mortgages to be securitized. Our results imply that previous work suggesting that banks tighten credit in response to a shock provides only a partial story and is in some ways misleading. It appears that banks respond to a negative shock by de-risking rather than a uniform reduction in lending. In terms of the ultimate impact on borrowers, we find that the shock had only a minimal impact on the overall quantity of loans supplied to firms or households, reflecting substitution to other sources of financing.
2016-26 | With Giacomini and McKenna | September 2016
Stress testing has become an important component of macroprudential regulation yet its goals and implementation are still being debated, reflecting the difficulty of designing such frameworks in the context of enormous model uncertainty. We illustrate methods for responding to possible misspecifications in models used for assessing bank vulnerabilities. We show how ‘exponential tilting’ allows the incorporation of external judgment, captured in moment conditions, into a forecasting model as a partial correction for misspecification. We also make use of methods from robust control to seek the most relevant dimensions in which a regulator’s forecasting model might be misspecified – a search for a ‘worst case’ model that is a ‘twisted’ version of the regulator’s initial forecasting model. Finally, we show how the two approaches can be blended so that one can search for a worst case model subject to restrictions on its properties, informed by the regulator’s judgment. We demonstrate the methods using the New York Fed’s CLASS model, a top-down capital stress testing framework that projects the effect of macroeconomic scenarios on U.S. banking firms.
2015-13 | With McKenna | September 2015
Despite the general consensus that stress testing has been useful in financial and macro-prudential regulation, test techniques are still being debated. This paper proposes using robust forecasting analysis to construct adverse scenarios using a benchmark model that includes a modified worst-case distribution. These scenarios give regulators a way to identify vulnerabilities, while acknowledging that models may be misspecified in unknown ways.
2013-29 | September 2013
What determines the frequency domain properties of a stochastic process? How much risk comes from high frequencies, business cycle frequencies or low frequency swings? If these properties are under the influence of an agent, who is compensated by a principal according to the distribution of risk across frequencies, then the nature of this contracting problem will affect the spectral properties of the endogenous outcome. We imagine two thought experiments: in the first, the principal (or `regulator’) is myopic with regard to certain frequencies – he is characterized by a filter – and the agent (`bank’) chooses to hide risk by shifting power from frequencies to which the regulator is attuned to those to which he is not. Thus, the regulator is fooled into thinking there has been an overall reduction in risk when, in fact, there has simply been a frequency shift. In the second thought experiment, the regulator is not myopic, but simply cares more about risk from certain frequencies, perhaps due to the preferences of the constituents he represents or because certain types of market incompleteness make certain frequencies of risk more damaging. We model this intuition by positing a filter design problem for the agent and also by a particular type of portfolio selection problem, in which the agent chooses among investment projects with different spectral properties. We discuss implications of these models for macroprudential policy and regulatory arbitrage.