Transcript Macrohonours Lecture 8x

Lecture 8 – Banking and financial crises
8.1 The banking system in the IS-PC-MR model
8.2 Bank’s role and balance sheets
8.3 Bank behaviour – cycles and crises
8.4 Asset price bubbles
8.5 Financial accelerator
8.6 Bank-leverage-centered crisis
8.7 Conclusion
8.1 The banking system in the IS-PC-MR model
• “Doing Economics as if the last 30 years have
happened”
• In this case conceptualising and problematising
the role of banks and the financial sector in our
macroeconomic models
• For example - how do we incorporate banks and
the financial system into the 3 equation model –
IS-PC-MR?
– The bank mark-up (effect lending rate) causes
movements along IS curve
– Degree of bank credit rationing causes shifts of the IS
curve
(1) Banks mark-ups
• Banks play a vital role in borrowing and lending in the economy and
in the transmission of monetary policy - this is reflected in the
model’s IS curve
• IS curve is based on the assumption that firms and households can
implement their planned spending decisions using the banking
systems money, credit and payment systems
– CB sets policy rate (rP)
– Firms and households take decisions based on the lending rate (rL)
– For CB policy there needs to be a predictable relationship between
the policy rate and the lending rate
– lending rate (rL) > policy rate(rP) as there is a mark-up (aka margin or
spread or wedge) between the policy rate and the lending rate
– Banks set this mark-up based on their optimising conduct to cover
their costs in providing bank account facilities, assessing
creditworthiness, etc.
IS-PC-MR model with bank mark-up
• In Fig 5.1a the CB sets its policy rate (rP) to deliver the desired
lending rate (rs) (shown on vertical axis of IS diagram)
• In Fig 5.1b there is an increase in the banking mark-up (e.g.
because banks regard loans they have made as riskier)
– the lending rate increases to r0 even though the policy rate (rP)
does not change
– This leads to a contraction of aggregate demand and output falls
(as y1 < ye)
• Key point – Shocks in the banking system have real
macroeconomic effects e.g.
– (1) increase in bank mark-up leads to fall in AD (negative
movements along IS curve),
– (2) as banks engage in increased credit rationing some households
and firms face credit constraints (IS curve shifts left with credit
rationing, also leading to a contraction of y)
Figure 5.1
In reality there are various
interest rates in the economy
• Fig 5.3 shows that in the UK there are a spectrum
of interest rates in the money and capital markets
• The policy rate (rP) is the rate that banks can lend
money from the CB - defined as rP in the model
• Money market rates are closer to the policy rate
(rP) – also defined as rP in the model
• Other rates – such as the 5 year mortgage rate
are defined as the lending rate (r) in the model
There are various interest rates in
the economy (Fig 5.3)
Evidence of breakdown in the
relationship rP and rL
• Fig 5.4 suggests a stable relationship between rP and rL
in normal times
• This normal relationship was disrupted by the financial
crisis from 2009 (when rP was reduced sharply by CB’s
but rL did not fall as rapidly mainly due to the
increased mark-up of banks in response to the risk that
they perceived)
• The normal transmission mechanism of monetary
policy broke down after 2009 i.e. changes in rP no
longer predictably changed borrowing costs for
households (r)
• (Recall earlier Greenspan’s conundrum during the
Great Moderation – where he raised rP but rL fell)
Figure 5.4
What determines the size of the Bank mark-up?
• Risk, Risk Tolerance and Bank Equity explain the gap
between rP and rL
• rL = (1 + μB)rP
• μB is the bank mark-up
• μB rises i.e. rL rises relative to rP if:
– The riskiness of projects increases
– A bank’s risk tolerance falls
– The size of bank equity falls
• μB depends positively on risk and negatively on risk
tolerance and size of equity
• Another factor that can push up μB is if the banks have
market power (pricing power) i.e. if the banking sector
is not competitive
(2) Credit rationing and collateral
• Credit rationing takes place when banks deny potential
borrowers access to credit e.g. redlining suburbs, or low
income groups
• If credit is rationing is increased then effectively the IS
curve shift leftwards (with contractionary effect on y)
• If credit is rationing is reduced then effectively the IS curve
shift rightwards i.e. (expansionary effect on y)
• Example: Increased housing prices can play a role in
reducing credit rationing – why
– Houses serve as collateral on mortgage loans e.g. borrower will
lose the house to the bank if he or she fails to pay back their
bond, when house prices are going up the borrower can
borrow more as they have more collateral, but when housing
prices fall and the banks repossess houses when monthly bond
payments are not made, then the value of the bank’s collateral
falls
Why do banks ration credit?
• Banks ration credit, due to Credit risk and Information problems /
asymmetries e.g.:
– Banks rely on proxy forms of information e.g. the wealthier you are
and the more of your own money you can invest in a project the easier
it will be to borrow from a bank, borrowers with good business plans,
but insufficient wealth to invest in their project may be denied access
to credit
– banks wish to avoid moral hazard which occurs when a borrower
exerts less effort than he or she would have exerted if they used their
own money,
– Banks face an adverse selection problem as due to the fact that they
do not have sufficient information to know that the borrower will be
able to repay a loan, they charge a higher interest rate to cover the
risk that the borrowers self-reported future earnings plans might fail
to materialise. This results in a situation where individuals with a
higher probability of success self-select away from bank credit (e.g. to
other forms of finance), adversely for the banks this leaves the weaker
applicants dominating the pool of those seeking bank credit, therefore
the banks refuse credit to a number of these weaker applicants
8.2 Bank’s role and balance sheets
• Intermediation role played by banks and the
fractional reserve system
• Bank Solvency – lender of last resort and
bailouts
• Bank Balance sheets
Key roles played by banks in the economy
(1) Maturity transformation
– Savers want access to their money in the s-r
– Borrowers need access to funds for l-r projects
– Maturity mismatch creates a liquidity risk for banks –
because banks only hold a fraction of deposits in
liquid form in their reserve accounts at CB – hence
called a fractional reserve banking system
• (2) Aggregation
– Banks aggregate small savings quantities and make
larger lumpier loans
• (3) Risk pooling
– Large banks can withstand a certain proportion of
defaults without putting the savings system at risk
Bank Solvency – Lender of Last Resort and Bailouts
• Banks can play there role of maturity transformation,
aggregation, etc and be more profitable if they keep
their reserves low (as reserves are low return
compared to profits made on borrowing)
• But, in a fractional reserve system (where reserves are
kept to a regulated minimum) a bank will be unable to
pay out to everyone if all depositors try to withdraw
their funds at once e.g. a bank run
• A bank is solvent if assets > liabilities (so if there is a
run on the bank, they merely require liquidity, here CB
provides liquidity be acting a Lender of Last Resort)
• A bank is insolvent if assets < liabilities (in this case the
bank must close or be bailed out by govt / taxpayer)
Solutions to avoid a bank run on a solvent bank
• (1) CB makes it known that it stands as a lender of last
resort facility (also know as liquidity backstop) i.e. CB
provides liquidity to a bank that does not have enough
cash to meet its s-r liabilities (e.g. if bank run) but where
the bank is solvent (i.e. assets > liabilities)
• (2) Deposit insurance – where bank deposits below a
certain level (eg USD250000 in US) will be honoured in full
if a bank is unable to do so (which effectively removes the
rational for bank runs)
• Note: Design of lender of last resort and deposit insurance
must be carefully designed to balance:
– Protection of public from bank runs / losses
– Avoidance of moral hazard i.e. avoid overly risky behaviour by
banks who know that they will be saved no matter how
dangerous their conduct)
Bail-out of insolvent banks
• Bank insolvent if:
• value of assets < value of liabilities or if bank owns
less than it owes
• If insolvent a bank will go out of business unless it is
bailed out by government
• Note: systemic risk - even a small number of banks
going insolvent can have a chain reaction and effect
the whole banking system
• Both approaches – savings banks and letting banks fail
carry risks (read Tim Geithner Stress Test Reflections on
the Financial Crisis – in which he compares saving
banks to the role of a fire fighter)
Bank Balance sheets
• Bank Balance Sheets are a device for viewing
solvency – as it keeps track on changes to assets
and liabilities
• Net worth (equity which is owed to
shareholders) = assets – liabilities
• Equity = what the bank owns or is owed – what
the bank owes to others
• See Fig 5.9 - Bank is solvent as assets = 100 and
liabilities = 96, therefore net worth (equity) = 4
• Banks can become insolvent if:
– Assets drop in value so that Assets – Liabilities < 0
Figure 5.9
Other aspects from a Bank’s Balance Sheet
• (1) You can reorganise the bank sheet identity as
follows:
• Assets = Liabilities + Net Worth (equity)
• A bank funds its assets through a combination of
debt (liabilities) and equity (new worth)
• (2) Leverage (total assets/equity) = 100/4 = 25
• Note: leverage at Barclays PLC (UK) (fig 5.10) is
very high (36.4) – such high leverage played an
important role in the build up to the 2008-09
financial crisis
Figure 5.10
8.3 Financial cycles and crises
• Now turn to an analysis of potential
pathologies – destablising features of the
financial system
• Cannot always be assumed, as it has been in
our models so far, that the financial system
works correctly
• Some studies have discerned a financial cycle
(as distinct from the business cycle) which is a
cause of macroeconomic instability
The Financial Cycle
• Business cycles - based on fluctuations in GDP (and
related employment and inflation fluctuations)
• Financial cycles – based on fluctuations in key financial
variables, such as credit extension and housing prices
• Upswings and downswings in the financial cycle
typically last longer than the business cycle
• Major blind spot in macroeconomic theory at the heart
of the 2008-09 financial crisis that triggered the Great
Recession:
– Financial variables like housing prices and bank credit
extension are not targeted by Inflation targeting CB’s
– In reality, potentially dangerous upswings in the financial
cycle can develop alongside successful inflation targeting
Workings of the financial cycle
• (1) during upswing of financial cycle – housing prices rise more
rapidly than over the long-run and banks extend more credit, a
positive feedback process amplifies rising house prices and levels of
debt and borrowing in the banking sector and household sector (as
per 6.1 and 6.2)
• (2) upswing ends with collapse in house prices and a banking crisis
• (3) during downswing of the financial cycle – households and banks
reduce their levels of debt (know as deleveraging),
– banks to repair their balance sheets set a higher spread rate above the
policy rate and are less willing to make loans
– Households increase their savings and some need to recover from
negative equity (where they owe more on their house than their house
is worth)
• (4) these balance sheet (or wealth) effects imply a deeper recession
(slow down in growth) than would have been the case with out such
wealth effects
• (5) public sector debt also tends to increase sharply as:
– Recession means that growth slows and tax revenues decrease
– Govt’s may need to use resources to bail-out failing banks
Stylised facts about the financial cycle
• Based on work done by economists at the Bank for
International Settlements (BIS) in Basel led by Claudio Borio
• BIS financial sector measure reflects fluctuations in three
financial variables:
– Private credit
– Private credit to GDP ratio
– Residential property prices
• Why these three variables?
– They follow very similar patterns over time and their peaks are
often associated with banking crises
– Stock market prices are not included in the financial sector
measure, as they exhibit more short-run volatility than the
other three variables and their peaks are less frequently
associated with crises
Stylised facts about the financial cycle
• Gap between business cycle peaks is typically 5 to 6 years in
advanced countries, but financial cycles are two or three times
longer
• Three stylised facts about financial cycles:
– Banks play a key role in the cycle through their lending and borrowing
behaviour (i.e. both asset and liability sides of their balance sheets)
– The housing sector is pro-cyclical and the purchase of new and existing
housing stock is often financed via borrowing from banks
– The inter-relationship between banks and housing is central to the
financial cycle, the peak of the financial cycle is often followed by a
banking crisis
• Fig 6.3 shows the business and financial cycles for the United States
from the 1970’s to 2011
– Financial cycle is more prolonged as compared to the business cycle
– Although financial cycles are not derived from data on banking crises,
the peaks of the financial cycle in the USA coincide almost exactly with
the onset of the last two banking crises.
Figure 6.3
Figure 6.4
• The financial cycle is constructed based on following
underlying series:
– Private credit to GDP ratio (Fig 6.4a)
– Real residential property prices (Fig 6.4b)
• The cyclical components are estimated relative to trend
i.e. they are the fluctuations in the series around the
long–run trend
• House prices rose steadily in the US from the early
1970’s to the late 1990’s, boomed in the early to
mid2000’s and then fell dramatically between 2006 and
2011 (in 2006Q1 house prices were 25 times higher
than what they were in 1970)
• Borrowing by US households and firms (private credit
to GDP ratio) increased continually between 1970 and
the financial crisis of 2008-09
Figure 6.4
International comparison
• UK - similar to the US with an upswing in the financial cycle beginning in
the mid 1990’s and ending with the crisis in 2008-09
• Sweden and Japan – the financial cycle did not move above trend in the
2000’s as both countries had experienced large banking sector crashes
in the late-80’s and early 90’s
• Germany – the amplitude of Germany’s financial cycle is smaller than
the other countries and the crisis occurs at a trough in the German
financial cycle
– this is due to the fact that home ownership levels are low in Germany
– in fact the German banking crisis in 2007 was imported from the US
subprime crisis, rather than being home grown i.e. German Banks invested
in US financial assets that lost value)
– the German case highlight the important impact of institutional
arrangements in economics
• Note: the amplitude of financial and business cycles cannot be
compared, the relative length of financial and business cycles can be
compared
Figure 6.5
Key drivers of financial cycles
• Financial cycles are driven by factors such as:
– Housing prices bubbles – where demand for a durable
asset (investment asset) like housing actually rises
with increasing prices, and vice versa demand falls
with falling prices (unlike a normal good where
demand falls with rising prices, and demand rises with
falling prices)
– The Financial accelerator i.e. the positive feedback
process between asset prices and the macro-economy
where house prices rise this leads to an increase in AD
(as people can borrow more and invest and consume
more) and vice versa when housing prices fall there is
a fall in AD
8.4 Asset price bubbles
• Fig 6.6 indicates price dynamic processes in different
kinds of markets - there is price stability along the 45
degree line where Pt = Pt+1
• 1st scenario – normal good (non durable good)
– where price dynamic equation (PDE) is less steep then the
45 degree line
– when there is a positive shock to the price in time t then at
t+1 the price begins to fall back to the equilibrium
– slope of PDE less than 1 so change in price is less than 1 in
next period
– S+D curves show there is excess supply at the higher price,
and adjustment takes place (falling price and rising
demand until S=D at the original price)
Figure 6.6
Asset price bubbles
• 2nd scenario – price bubble (durable good)
– The PDE line is steeper than than the 45 degree line
– A positive price shock at time t leads to a higher price in
period t+1 (as the slope of the PDE curve is >1 then a
change to a price will be followed by a yet higher increase
in a price in later periods)
– This can happen in the the market for a durable good
where there is a belief that the price of a good is going to
rise further, there will be a capital gain in buying more of
the good and holding it as the price rises (as result
increasing asset price leads to increased Demand)
– This is represented by an upward shift in the D curve in the
S+D space
Asset price bubbles
• Third scenario – multiple equilibria
– There is an S shaped PDE curve
– There are three intersections with the 45 degree
curve – stable upper and a stable lower
intersections (where slope of PSE is <1) and a
steep unstable intersection in the middle (where
the slope of the PSE >1)
– The economy will eventually be pulled to the
stable high or stable low price equilibrium
Bursting of a price bubble
• As prices rise they deviate more and more away from their initial
fundamental value, if something happens and some think people that the
price might fall, then the following can happen (as per Fig 6.7)
• PDE: Pt+1=F(Pt,At), where At is a variable that shifts the curve
• So if a fraction of people expect that the price will fall in the next period
then A decline to A’ this shifts the PDE curve downwards (equilibrium
moves from A to B)
• In next period, more people believe that the price may fall and A falls
further to A’’
• A tipping point is reached at the point of tangency of PDE and the 45
degree line
– at the point of tangency the slope of both curves is 1,
– to the right of the point of tangency the slope of the PDE curve is <1 (hence
prices relatively stable)
– To the left of the point of tangency the slope of the PDE curve is >1 (hence
prices are unstable)
• The price will then fall sharply until it again reaches a stable equilibrium at
point C
• This is the character of a market that will experience repeated booms and
busts (e.g. a housing market)
Figure 6.7
House price cycles
• Fig 6.1 shows the housing aspect of the financial cycle
• when house prices are on the way up this leads to a
higher value of collateral for borrowers, an increase in
household borrowing, increased house buying, further
pushing up house prices, and then the cycle of rising
house prices continues…
• when house prices are on the way down this leads to
a lower value of collateral for borrowers, a fall in
household borrowing, decreased house buying, further
pushing down house prices, and then the cycle of
falling house prices continues… (so-called plain vanilla
crisis)
Figure 6.1
Housing-based (plain vanilla) financial crisis
• Plain vanilla in the sense that it does not rely on the more
sophisticated behaviour of banks involving novel financial
instruments, it is a crisis based purely on the effects of
falling housing prices
• 1. Property bubble bursts and house prices, wealth of
households, fall and some are unable to service their
mortgage
• 2. Houses are repossessed by the banks and sold at a loss
• 3. The net worth of banks falls due to losses that they incur
on their mortgage book
• 4. An exposed bank can become insolvent as the value of
assets shrink and wipe out its capital cushion (equity)
8.5 Financial accelerator
• A credit constrained household will spend more
when the credit constraint is relaxed.
• The credit constraint is relaxed and the IS curve
shifts up when asset prices rise (specifically house
prices in countries such as SA, UK and US where
houses can be used as collateral for bank loans)
• This positive feedback process between asset
prices and the macro-economy is called the
financial accelerator
Steps of financial accelerator
• 1. Positive shock pushes up housing prices (e.g. banks enter
sub-prime market)
• 2. Households borrow more when credit constraint is
relaxed by rising house prices, as value of collateral
increases
• 3. IS curve shifts rightwards as households use borrowing to
consume more and buy more housing
• 5. the increased demand for housing pushes housing prices
up further
• The financial accelerator amplifies and propagates the
positive effect of rising house prices by pushing up AD
through-out the economy
• Note: The financial accelerator process works in reverse if
house prices start to fall (i.e. if house prices fall AD is
reduced)
Housing feedback process
• Initially there is some shock pushing up property prices e.g.
banks may decide to increase their loan to value (LTV) ratio
(e.g. you may borrow 90% or 100% or 110% of the value of
the property):
• Fig. 6.8a shows a housing market in which house prices rise
to P1 and P2 with rising demand but then in the long-run
return to P0 as more houses are built in the open area
around the city
– If prices are persistently rising in the context of these
fundamentals (where the long run supply curve is flat), then the
price rise is likely to be a bubble (being driven by extrapolative
expectations driving the bubble process)
• Fig 6.8b shows a housing market, in a city that cannot
expand the housing stock, and house prices rise from P1 to
P2 as the demand curve shifts upwards
– this is not a bubble as it is based on fundamentals (of a vertical
housing supply curve) of the housing market, but as house
prices are rising this scenario still fuels the financial accelerator)
Figure 6.8
8.6 Bank-leverage-centered crisis
• This section is about investment banks and their asset
trading activities (not retail banks that focus on housing
loans)
• Investment banks (shadow banks, etc.) use a large number
of mortgages to produce financial assets, known as
securitised assets e.g. Mortgage-backed securities (MBS)
• These new assets are then traded and become subject to
laws of supply an demand, quite different from the
underlying assets which they are based on
• As these asset prices change – these lead to bank
leveraged-centered upswings and downswings
• Note: key fact is that there has been a dramatic increase in
the leverage of banks (i.e. rising ratio of assets to equity).
See Fig 6.10 in UK leverage rose from a ratio of 20 in 2000
to 48 in 2008
Elements of bank-leverage crisis
• Both investment banks (IB) and savers invest in financial assets – savers
are assumed to be risk averse and IB’s are risk neutral
• As demand for risky assets goes up (e.g. due to a belief that risk has
fallen, asset price rises). Risk averse savers reduce their holdings, but IB’s
hold more as long as the expected return is positive.
• Hence financial assets are transferred from savers to IB’s
• As a result leverage (asset:equity ratio) of IB’s goes up substantially (see
Fig 6.10)
• As asset prices continue to rise, IB experience a capital gain as the value
of the securities they hold rises, this pushes up the equity of IB’s (as they
use mark to market valuations)
• A banks have more equity, savers are prepared to lend more and IB’s buy
even more assets (value of assets = equity x leverage – and both equity
and leverage are rising as asset prices rise)
• When asset prices fall (e.g. due to change in belief about riskiness of the
system) this leads to major capital losses, putting solvency of some IB’s
at risk
Simple value at risk model
• P = Price of assets and F = number of assets
• (1+r) = expected return form the asset (return
not policy rate)
• If (1+r) > P then the expected return is positive
• Bank spends PF buying assets for as long as
return is positive (1 + r – P > 0)
• Maximum value of assets bank can buy is PF =
B + e (i.e. the amount they can borrow plus
equity)
Simple value at risk model
• Question: how much will banks borrow (B)?
– This depends on savers – savers will lend to IB’s at the money
market rate (= policy rate rP), as long as this is risk free
– Savers assume that the maximum possible risk per F asset is z
(possibly inferring z from the ratings agency rating system)
– Lowest possible return for the IB is (1 + r – z)F
– Savers will lend B as long as they can get back at least (1 + rP)B
– In worst case, IB gets lowest possible return minus cost of
borrowing i.e. (1 + r – rP – z)F
– If we assume rP = 0, the maximum IB can borrow is: max B = (1 +
r – Z)F
• Therefore. Maximum assets value IB can buy is:
PF = max B + e = (1 + r – z)F + e
Simple value at risk model
• Maximum value of assets IB can purchase is
PF = (1 + r – z)F + e
• Solving for F (i.e. IB demand for assets F):
• F = e / (z – (1 + r – P))
• This indicates that IB’s demand for F assets:
– rises with its equity (e) and
– rises as risk (z) falls relative to return (r)
• Also, leverage = λ = assets / equity :
λ = F/e = 1 / ((z – (1 + r – P)) or
λ = F/e = 1 / risk – return to IB
• This indicates that leverage λ will rise when risk (z) falls
relative to return (r) i.e. leverage can go very high if risks falls
• Example if ratings agencies report that risk has fallen as they
did in 2008-09 when securities were rated AAA which should
not have been
Numerical example
Numerical example
• Period 0: risk =0.12; return = 1 + r – P = 1 + 0.07 – 1
= 7%, so leverage is λ = 1/(0.12-(1+0.07-1) = 20 and
IB holdings of F0 = λ0e0= 20X10 = 200
• Period 1: risk =0.04; return = 1 + r – P = 1 + 0.07 –
1.05 = 2%, so leverage is λ = 1/(0.04-(1+0.07-1.05) =
50 and IB holdings of F1 = λ0e0=50 X 10 = 500
(falling risk pushes up demand for assets F)
• Period 2: capital gains increase investment bank
capital from e0=10 to e2 =20, there F2 = λ0e0 = 50 X
20 = 1000 (increasing equity pushes up demand for
assets F)
Numerical example
• Period 2 equity is calculated from the capital gain made on
F0 assets following the rise in price from 1 to 1.05 between
period 0 and period 1 i.e. 200 X 1.05 - 200 = 10 i.e. equity
doubled from e0 = 10 to e2 = 20
• In periods 1 and 2 the world feels safe and risk is low. IB’s
make small profits on huge holdings of assets, reflecting high
leverage and the belief that assets were almost completely
safe
• This is in spite of the reduced expected return from 7% to
2%, as risk has gone down more from 0.12 to 0.04, with the
return still positive IB’s increase their holdings to F2 = 1000
• The funding split for these asset holdings F2 = 1000 is 20 for
equity and 980 based on debt (borrowing by the IB) (See Fig
6.11)
• This high leverage (λ = 1000/20 = 50) makes IB’s vulnerable
to bankruptcy
Fall in risk boosts IB asset holdings
• IB will always buy as many assets as possible if
the expected profit is positive (they are risk
neutral)
• The amount they can buy is constrained by their
equity and by the amount they can borrow (i.e.
the amount savers will lend to them)
• A fall in risk boosts IB’s asset holdings because it
loosens their funding constraint by increasing
both their equity and the amount they can
borrow from savers
Crash – return of risk
• In period 3: scary bad news arrives and risk
shifts back up to its original level 0.12
• The demand for securitised assets will fall and
asset prices will fall from 1.05 to 1
• The capital loss for the IB is 0.05 X 1000 = 50
• The IB’s equity is 20 in period 2 and this is
wiped out by its capital loss of 50
• The IB is bankrupt
Upswing
• Sources of lower risk:
– Successful inflation targeting bringing macro stability and
reduced volatility
– Rising house prices i.e. the value of the bank’s collateral is rising
relative to the fixed purchase price of the house
– Financial innovation where mortgages are tranched and
grouped into new financial assets (mortgaged backed securities)
with different levels of risk, and products offering protection
from risk, a form of insurance against the defaults of such
securities e.g. credit default swaps
• As perceived risk falls – demand rises for assets and asset
prices rise
• As these asset prices rise – risk neutral IB increase their
demand for these assets
• IB’s are able to fund very large increases in asset holdings
by (a) higher equity associated with mark to market
valuation of assets and (b) increased borrowing from savers
Downswing
• Trigger is reversal of upward trend in housing
prices
• Perceived risk goes up
• The demand for securitised assets falls and the
price of these assets falls
• IB can become insolvent (capital loss > equity)
• As banking system is interconnected banks
reduce their willingness to make new loans
• AD falls as financial accelerator amplifies and
propagates the downswing in the business cycle
Paradox of credibility
• Hyman Minsky (1982) highlighted how a benign period
in the economy could sow the seeds for a subsequent
crisis
• The Paradox of credibility - when everyone thinks risk
has gone down they behave in such a way that makes
the system riskier
• This implies that the economy is modelled based on
the ‘false belief’ that risk has fallen permanently
• This seems to go against economic rationality, but
perhaps can be explained by behavioural economics
(which is based on human rationality (or animal spirits)
rather than perfect rationality)
8.7 Conclusion
• This lecture has focused on the drivers of the financial
cycle and the chief causes of systemic banking crises
(e.g. triggered for IB’s when the economy moves from
perceived low risk to perceived high risk)
• What are the costs of such crises?
– Reinhart and Rogoff (2009) found that such crises are
typically drawn out affairs with three main characteristics:
– Deep and prolonged asset price collapses – declines in real
house prices average over 35% over a period of 6 years,
and equity prices down by 55% over 3 years
– Unemployment rises by 7 percentage points over 4 years
and real GDP per capita contracts by 9% over 2 years
– Government debt explodes rising on average by 86% in 3
years (due mainly to falls in tax revenues, but also due to
bank bail-out costs)