Financial Intermediaries, Credit Shocks and Business Cycles
| Date | 01 February 2016 |
| Published date | 01 February 2016 |
| Author | Yasin Mimir |
| DOI | http://doi.org/10.1111/obes.12099 |
42
©2015 The Department of Economics, University of Oxford and JohnWiley & Sons Ltd.
OXFORD BULLETIN OF ECONOMICSAND STATISTICS, 78, 1 (2016) 0305–9049
doi: 10.1111/obes.12099
Financial Intermediaries, Credit Shocks and Business
Cycles*
Ya s .
in M.
im.
ir†
†Istanbul School of Central Banking, Central Bank of the Republic of Turkey, Fener
KalamI¸s Cad. AtlIhan Sok. No: 30/A KadIk¨oy, Istanbul, Turkey
(e-mail: yasin.mimir@tcmb.gov.tr)
Abstract
We document the cyclical properties of aggregate balance sheet variables of the US com-
mercial banks: (i) Bank credits and deposits are less volatile than output, while net worth
and leverage ratio are several times more volatile, (ii) bank credits and net worth are
procyclical, while deposits, leverage ratio and loan spread are countercyclical. We then
present a real business cycle model with a financial sector to investigate howthe dynamics
of macroeconomic aggregates and balance sheet variables of the US banks are influenced
by empirically disciplined shocks to bank net worth.Both calibrated and estimated versions
of the model show that these financial shocks are important not only for explaining the
dynamics of financial flows but also for the dynamics of macroeconomic variables.We find
that the recent deterioration in aggregate net worth of the US banking sector contributed
significantly to the 2007–09 recession.
I. Introduction
What are the cyclical properties of aggregate balance sheet variables of the US banking
sector?1How important are financial shocks relative to standard productivity shocks in
driving real and financial business cycles in the United States? To address these questions,
*I would like to thank the Editor and two anonymous Referees for useful comments on an earlier version of
this article. I also would like to thank S. Bora˘gan Aruoba, Sanjay K. Chugh, Pablo N. D’Erasmo, Anton Korinek,
Enrique G. Mendoza, John Shea and Enes Sunel as well as seminar participants at the Board of Governors of the
Federal Reserve System, Bank of Canada, Bank of England, Central Bank of the Republic of Turkey, University
of Maryland, Uppsala University, New Economic School, Bilkent University, KocUniversity, Ozyegin University,
TOBB-ETU, METU-NCC, Istanbul Technical University, 2013 Borsa Istanbul Financeand Economics Conference,
2011 Annual Meeting of the Society for Economic Dynamics, 2011 International Conference on Computing in
Economics and Finance, 2011 Eastern EconomicAssociation Conference, 2010 Midwest Macroeconomics Meetings,
2010 International Conference of Middle East Economic Association, 2010 International Conference on Economic
Modeling for their constructive comments and suggestions. I am also grateful to the Federal Reserve Board for their
hospitality.All remaining errors are mine. An earlier version of this paper was part of mydisser tation at the University
of Maryland College Park. The views expressedhere are those of the author and do not necessarily reflect the views
of the Central Bank of the Republic of Turkey.
JEL Classification numbers: E10, E20, E32, E44
1Throughout the paper, we use the terms ‘aggregate balance sheet variables’, ‘aggregate financial flows’ and
‘financial variables’ interchangeably.
Financial intermediaries, credit shocks 43
this study proposes an equilibrium real business cycle model with a financial sector, that
is capable of matching the fluctuations in both standard macroeconomic aggregates and
balance sheet variables of the banking sector observed in the US data. Although a grow-
ing body of literature studies the relevance of financial shocks together with an explicit
modelling of frictions in financial sector, the behaviour of aggregate financial variables in
the US banking sector and how they interact with real variables over the business cycle
have not been fully explored in the literature.2Most previous studies have not tried to
match fluctuations in both standard macro variables and aggregate balance sheet variables
of the US banking sector simultaneously. In this paper, we show that financial shocks to
the banking sector contribute significantly to explaining the observed dynamics of real and
financial variables.3Financial shocks play a major role in driving real fluctuations due to
their impact on the tightness of bank capital constraint and hence credit spread.
We first systematically document the business cycle properties of aggregate financial
variables, using the data on the US commercial banks from the Federal Reserve Board.4
The following empirical facts emerge from the analysis: (i) Bank credits, deposits and loan
spread are less volatile than output, while net worth and leverageratio are more volatile, (ii)
bank credits and net worth are procyclical, while deposits, leverage ratio and loan spread
are countercyclical and (iii) financial variables lead the output fluctuations by one to three
quarters.
We then assess the quantitative performance of a theoretical model by its ability to
match these empirical facts. In particular, there are two main departures from an otherwise
standard real business cycle framework. The first departure is that we introduce an active
banking sector with financial frictions into the model, which are modelled as in Gertler
and Karadi (2011). Financial frictions require that banks borrow funds from households
and their ability to borrow is limited due to a moral hazard (costly enforcement) problem,
inducing an endogenous capital constraint for banks in obtaining deposits.5This departure
is needed to have balance sheet fluctuations of financial sector matter for real fluctuations.
The second departure is that the model incorporates shocks to bank net worth (i.e. ‘financial
shocks’) that alter the ability of banks to borrow and to extend credit to non-financial
businesses following Hancock, Laing and Wilcox (1995), Peek and Rosengren (1997,
2000), Brunnermeier and Pedersen (2009), Meh and Moran (2010), Woodford and Curdia
(2010), Mendoza and Quadrini (2010), and Iacoviello (2014).6However, the specific type
2See Christiano et al. (2010), Dib (2010), Meh and Moran (2010), Gertler and Kiyotaki (2010), Gertler and Karadi
(2011), Kollmann, Enders and Muller (2011), Gertler et al. (2012). These papers don’t explicitly study the cyclical
properties of the aggregate balance sheet variables of the US banking sector over the business cycle.
3Throughout the text, we use the terms ‘financial shocks’, ‘net worth shocks’ and ‘bank capital shocks’inter-
changeably.
4Wealso document the business cycle properties of agg regatefinancial variables of the whole US financial sector
from 1952 to 2009, using the Flow of Funds data. The results are available in the online Appendix C.
5Hellmann, Murdock and Stiglitz (2000) argue that moral hazard in banking sector plays a crucial role in most of
the US economic downturns in the last century.Moreover, the presence of the agency problem makes the balance sheet
structure of financial sector matter for real fluctuations, invalidating the application of Modigliani-Miller theorem to
the model economy presented below.
6In the context of the theoretical model in this paper, the net worth shock can be interpreted as a redistribution
shock, which transfers some portion of the wealth from financial intermediaries to households. However, because
of the moral hazard problem between households and bankers, it distorts intermediaries’role of allocating resources
between households and firms, inducing large real effects.A complete model of the determination of the fluctuations
©2015 The Department of Economics, University of Oxford and JohnWiley & Sons Ltd
44 Bulletin
of the shock we introduce is different from these studies and none of these papers considers
the quantitative contribution of net worth shocks in explaining business cycle fluctuations
of macroeconomic variables and aggregate financial flows of the US banking sector.
Weuse two different methodological approaches to investigatethe business cycle impli-
cations of financial shocks following Jermann and Quadrini (2012). In the first approach,
we construct the time series of financial shocks as the residuals from the law of motion for
bank net worth, using empirical data for credit spread, leverage ratio, deposit rate and net
worth.This approach is similar to the standard method for constr ucting productivityshocks
as Solow residuals from the production function using empirical series for output, capital
and labour.7The resulting shock series showthat US economy is severely hit by negativefi-
nancial shocks in the Great Recession. For the sake of this particular approach we abstract
from various real and nominal rigidities that are generally considered in medium scale
DSGE models such as Christiano, Eichenbaum and Evans (2005) and Smets and Wouters
(2007) to elucidate the underlying mechanism as clearly as possible.We also abstract from
various types of shocks usually incorporated into the medium scale models. The way that
our shock series are constructed makes them independent of the number of shocks added
into the model.
Using a parsimonious model with standard productivity and bank net worth shocks
constructed from the data, we obtain three main results. First, the benchmark model driven
by both standard productivity and financial shocks is able to deliver most of the stylized
cyclical facts about real and financial variables simultaneously. Second, financial shocks
to banking sector are important not only for explaining the dynamics of financial variables
but also for the dynamics of standard macroeconomic variables. In particular, the model
simulations show that the benchmark model driven by both shocks has better predictions
about investment, hours and output than the frictionless version of the model (which is
standard RBC model with capital adjustment costs) and then the model driven only by
productivity shocks. The benchmark model also performs better than the model with only
productivity shocks in terms of its predictions about aggregate financial variables.8Third,
the tightness of bank capital constraint given by the Lagrange multiplier in the theoretical
model (which determines the banks’ ability to extend credit to non-financial firms) tracks
the index of tightening credit standards (whichshows the adverse changes in banks’ lending)
constructed by the Federal Reserve Board quite well. This result may also imply that the
Lagrange multipliers attached to the financial constraints in DSGE models with credit
frictions might contain valuable real-time information about the financial conditions of an
economy.
The second approach we used to evaluate the business cycle implications of financial
shocks is to estimate the model with Bayesian methods. This structural estimation is
undertaken using an extended model with more shocks and frictions. The extended model
in net worth of banks is beyond the scope of this study, because our goal is to analyse the quantitative effects of
movements in net worth of financial sector on business cycle fluctuations of real and financial variables.
7We also consider some alternative measures of financial shocks, including the one constructed based on loan
losses incurred by US commercial banks (using the charge-off and delinquency rates data compiled by the Federal
Reserve Board). The construction of these alternative measures and their simulation results are availablein the online
Appendix D.The main results of the study do not change under these alter nativemeasures.
8The RBC model with capital adjustment costs has no predictions about financial variables since balance sheets
of banks in that model are indeterminate.
©2015 The Department of Economics, University of Oxford and JohnWiley & Sons Ltd
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