Macroprudential Policy Under Uncertainty
Saleem Bahaj & Angus Foulis
21st November 2016
The views expressed in this presentation are those of the presenter and not necessarily those of the Bank of England
or members of the MPC, FPC or PRA Board.
Introduction
• Post-crisis, macroprudential policymakers have acquired
a suite of tools whose impacts are largely untested…
• …and a target variable which is inherently unobservable.
• This work: how should policymakers react to this?
– Is caution warranted?
– Or does uncertainty require a more active stance?
• What we do in the paper: suite of reduced form models
to explore some of the relevant issues.
• Key message: uncertainty does not justify inaction.
2
The results of Brainard (1967)
Chart 1: Estimates of the GDP impact of a
Chart 2: Swathe of Bank Balance Sheet
1ppt increase in Capital Requirements.
Indicators (up=more vulnerable)
Source: Macroeconomic Assessment Group
Source: Bank of England
3
A stylised stabilisation problem
Policymaker seeks to stabilise financial stability,
• Where is a policy tool.
• The impact of the tool, , is uncertain,
• The term denotes a shock
:
.
The
The policymaker’s
optimal choiceloss
of function,
is therefore:, is:
4
Other stories and mechanisms
• But is that the whole story? Are there other ways
uncertainty can influence how active policy should be?
• In the paper we explore a few other mechanisms:
–
–
–
–
Asymmetric preferences
Robust control
Learning
Private sector uncertainty
• These all could point towards more active policy
5
Is symmetric stabilisation the right way to
think about macroprudential policy?
• The costs of financial crises may be much larger than
overly tight regulation.
• Theoretical models that deliver endogenous crisis via
occasionally binding constraints (e.g Bianchi and
Mendoza (2010)) illustrate how severe asymmetries
can emerge.
• A stylised way to capture this is to have an
asymmetric objective function for the policymaker.
6
An asymmetric loss function: Linex
Policymaker seeks to stabilise financial stability,
• Where is a policy tool.
• The impact of the tool, , is uncertain,
• The term denotes a shock
The policymaker’s loss function,
:
.
, is:
7
Graph of Linex Function
8
Results with asymmetric preferences
1)
for
and
sufficiently large.
• If the loss function is sufficiently asymmetric the
policymaker should react to uncertainty about what it’s tool
does by using it more to insure against the worse.
2)
for
and
sufficiently large.
• The same logic applies.
9
Targeting the worst case outcome (robustly)
• The Linex function is an ad-hoc objective and still relies
on known distributions.
• Financial policymakers may face Knightian uncertainty
• An alternative, microfounded (see Gilboa and Schmeidler
(1989)) approach is to use robust control (minimax);
where policy is set to avoid large losses in the worst case:
• Place more weight on models that signal risks.
• Already embedded within stress testing.
10
The robust control problem
Policymaker seeks to stabilise financial stability,
:
• Where is a policy tool.
• The impact of the tool, , is uncertain,
• The term denotes a shock
.
The optimal
policymaker’s
choiceproblem
of is therefore:
is to choose
in order to:
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Graphical Illustration
12
Learning and Experimentation
• A benefit of activism: The more a tool is used, the more
we can learn about it’s impact.
– Natural experiments can help but policy is still a learning by
doing process.
• Political constraints may be paramount.
– We can’t treat the economy as a lab rat…
– … but learning is an argument against inaction bias in the face of
uncertainty.
13
Learning model
Now imagine there are two periods
policymaker has objective:
And:
As
before,
before choosing
Tradeoff: use
and the
, but the policymaker sees
:
to stabilise
or reduce
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Optimal Policy Choices with Learning
15
Private Sector Uncertainty
• The public are also uncertain about risks to financial stability,
the impact of macroprudential policy, and how the nascent
macroprudential policymakers will themselves behave.
• These uncertainties can reduce the effectiveness of policy.
• Specifically, the signaling power of policy is diminished if
private sector is uncertain why the policymakers are acting.
• A more uncertain public may necessitate a more active policy
stance.
16
Setup: Private Sector’s Problem
There are two states of the world, high and low. The
private sector chooses a level of risk taking, .
This increases the probability of the low state and the
payoff in the high state, , but lowers the pay off in the
low state, , specifically:
Where
is a variable chosen by the policymaker.
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The Policymakers Problem
The policymaker’s problem is identical with 3
exceptions:
1) The policymaker chooses
and not
.
2) The marginal payoff from in the high state is
lower for the policymaker:
.
3) There is an additional cost attached to the low
state:
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Information Sets
The private sector has the following priors:
The policymaker sector knows
same prior over .
and
The policymaker moves first, hence
over for the private sector:
, but has the
serves as a signal
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Solution
Private sector’s optimal choice is:
Method of undetermined coefficients to solve for
The final solution for is then:
and
With the parameters on and positive.
Result: Greater private sector uncertainty over policy
objectives results in more active policy:
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Conclusion
• Uncertainty comes in many forms and influences optimal policy
in different ways.
• There is not a general prescription that greater uncertainty
requires less active policy.
• If anything, in the situation currently facing macroprudential
policymakers, uncertainty may call for greater action.
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