Transcript Presentation

Monetary policy, banking and
systemic risk in open economies
Jaromir Benes (IMF)
Andrea Gerali (Banco d’Italia)
David Vavra (Czech National Bank)
Plan of presentation
• Motivation
• Features added
• Prototypical SOE model
• Policy experiments
Motivation
• A simple (DSGE) model framework with interactions
between real and banking sectors
• Provide dynamic and macro consistency in systemic
risk, early warning, or contagion exercises
• Integrated approach to monetary cum macroprudential policies
• Evaluate policy options under various constraints
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–
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Shock to bank capitalisation
Currency mismatches
Maturity mismatches
Booms and busts in asset prices
Motivation
• Reminiscences of 20 years ago
– Monetary policy paradigm not established in the early
1990s as it is now
– Model-based frameworks popped up (BoC, RNBZ) with
many ad-hoc features that became justified by proper
theory only later on
• Our work tries to incorporate some of the important
links between real economy and banking (and
monetary policy and macro-prudential policy) taking a
few shortcuts to keep the framework operational, e.g.
– no explicit debt/loan contracts
– cost function increasing in banks’ leverage
Features added
• Banks as agents with their own net worth
– Bank capital subject to regulation
– Bank capitalisation affects lending rates and volumes
– Fresh capital not trivial to raise
• Banks bear (some of the) aggregate macro risk:
non-performing loans
– Different from most of the current literature. Bernanke,
Gertler & Gilchrist 1998 accelerator assumes debt contract
contingent upon macro outcomes
– Bank capital subject to losses
Features added
• Simple housing
– fixed supply of houses
– house prices subject to bubbles
• Multi-period loans
– banks issue multi-period loans, refinance themselves short
– hence exposed to maturity mismatches
• ...hence multiple balance-sheet effects
– currency mismatch risk
– loan-to-value ratio affects premium
– maturity mismatch risk
Design of the real sector
Exports
Consumption
Local
production
Imports
Intermediates
Design of the real sector
• Simple but flexible to get aggregate elasticities right
• Roundabout production function
Pro-cyclical real marginal cost, no explicit labour market
• Imports both directly consumed (Leontieff) and used
as inputs (Cobb-Douglas)
– Helps to flexibly calibrate the aggregate elasticity of import
demand and exchange rate pass-through to the CPI
• Price-elastic export demand with export prices
subject to costs of deviating from world prices
– A wide range of assumptions about responses in export
prices and export volumes
• Simple housing (fixed supply of houses) => LTV
Design of the banking sector
Foreign funds
Loans to
consumers
Bank capital
(equity, net
worth)
Design of the banking sector
• Consumers net debtors at all time, foreign borrowing
intermediated through banks
• Banks combine foreign funds and their own net
worth (bank capital, equity) to make loans
• Bank capital is made indispensable by introducing a
cost function increasing in leverage:
– Regulatory costs
– Reputational costs
– Smooth cost function rather than an inequality constraint
analogy with inventory stock-out models
Design of the banking sector
• Banks extend multi-period loans
• Multi-period loans can be handled easily on the
consumers’ side
• …but to keep the problem tractable on the banks’
side, we in fact split the bank into its “wholesale”
and “retail” branches that take the other’s decisions
as given
• This split is (for ease of this exposition) not presented
here
Banks
(For ease of notation here: all assets and liabilities except F
denominated in local currency.)
• Balance sheet Lt  Bt  Ft  Et
• Gross earnings
Vt :  RL ,t 1Lt 1 1  gt   Rt 1Bt 1  R F
St
*
t 1 t 1 St 1
Et 1 · f  Lt 1 / Et 1 
Non-performing
loans
Banks’ costs
increasing in
leverage
Banks
• Banks must follow a fixed dividend policy
dividends
dt   ·Vt
new equity Et  (1   )·Vt
• This is to give bank capital non-trivial role
– capital not easy to raise fresh capital
– consumers (owners) cannot simply pour money into banks
to re-capitalise them
– shock to capital (leverage) costly for the banks
What does the cost function do?
• Prevents banks from going infinitely leveraged—
return on equity diminishes in leverage
RE ,t : RL ,t 1 1  gt   Rt  ELtt  Rt  f (Lt / Et )
• Affects marginal cost of lending => lending rates
RL ,t  Rt  f   t / et 
• After a hypothetical shock to bank capital:
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–
–
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the total costs increases…
…but the marginal costs increase more still
so does retail lending rate
lending volumes drops in response
Non-performing loans
• NPLs are still repaid by the consumers, but the
repayments never reach the bank
• NPLs are an ad-hoc function of some macro variables
• We experiment with NPL functions decreasing in
– loan-to-value ratios (=used in simulations here)
– loan-to-current-income ratios
• Non-linear function with a “threshold”
• Must be, though, sigmoid (flattens for very large
values) – otherwise the simulation would explode
Non-performing loans
Multi-period loans
• Model the effects of the existence of multi-period
(nominal) loans, not portfolio/term-structure choice
• Introduce a “geometric” loan
– infinite number of geometrically decaying instalments
– instalments cannot be re-negotiated at a later time
• Why geometric?
– everything can be expressed recursively
– average maturity (Macaulay’s duration) can be calibrated
using just one parameter
– no new state variables needed to mimic very long terms
Multi-period loans
• Average maturity (duration) imposed, not
determined endogenously or optimally.
• Consumer ex-ante intertemporal choice not affected
(up to first order) by multi-period loans: Euler
equation still has the underlying one-period rate in it.
• Ex post, duration of loans matters to the extent the
economy is hit by unforeseen shocks (e.g. large
increases in short-term rates make consumers better
off if they go long).
Simulation experiments
• Expose the country to a premium shock
• Full dollarisation of the banking sector and the loans
• NPLs function of loan-to-value ratio
• Simulate two policy regimes
– IT with a flexible exchange rate
– An exchange rate peg
• Simulate two magnitudes of the shock
– a “small” shock (100 bp)
– a “large” shock (1,000 bp) going beyond the threshold of the
NPL function, resulting in sizeable balance sheet effects
100 bp country premium shock
1,000 bp country premium shock
Comments on simulations
• First, notice the non-linearities from the NPL function
– Large shock simulation is more than just 10x the small
shock simul (the shock is 10x bigger, in a linear world the
simulations would be identical, just the magnitudes would
multiply), and variables have different profiles
– Output loss under IT is closer to output loss under peg in
large shock simul (than in small shock simul) – this the
balance sheet effect. A large depreciation (plus drops in
house prices) raises the loan-to-value ratio significantly,
and triggers defaults (NPLs)
• Second, let’s turn to the large shock simulation.
Banks run huge losses in the first period (unexpected
NPLs were not reflected in the lending rate setting)
Comments on simulations
• How do the banks react to losses and drops in bank
capital?
• Their total costs increase, but even more so the
marginal costs. The banks raise the lending rates
significantly.
• This has two main implications:
– The banks start making profits, and cumulate bank capital
again (recall profits are the only source of new capital).
– Demand for loans drops.
Comments on simulations
• In real world, the banks would not lift the lending
rates so much (above 50 % PA in simulations – not
shown in the graphs), but would combine lending
rate increases with credit rationing.
• However, with credit rationing, the shadow value of
loans would increase exactly so as to depress
demand sufficiently to restore equilibrium at the
rationed levels.
• Whether the drop in loans is because of credit
rationing or high market rates is therefore irrelevant.
Comments on simulations
• Differences in the two policy regimes:
– An IT central bank can “transform” an interest rate shock
to an exchange rate shock (by cutting the rates). The
exchange rate shock is more favourable to the real
economy, because re-directs demand from foreign goods
towards local goods, whereas interest rates shocks depress
overall demand.
– On the other hand, flexible exchange rates can trigger
large valuation effects, seeing the households default on
their debt, and the banks run losses.