anatomy of financial crises
TRANSCRIPT
Anatomy of financial crises
February 2014
Dieter Guffens
KBC Chief Economist Department
2
Overview
Part I: Crises are more than ‘volatility’
Part II: Crises are inevitable
Part III: History repeats itself
Part IV: Lessons can be learned
Part V: Takeaways
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Overview
Part I: Crises are more than ‘volatility’
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I Crises are more than ‘volatility’ Bond yields during the past 5000 years
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I Crises are more than ‘volatility’ Tulip mania (1636-37)
-95%
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I Crises are more than ‘volatility’ South Sea bubble (1720)
-90%
Share price of South Sea Company
(in GBP)
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I Crises are more than ‘volatility’ Germany early 1920s
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I Crises are more than ‘volatility’ US bond yields 197Os
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US 10 year bond yields in %
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I Crises are more than ‘volatility’ Belgian bond yields 197Os
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Belgian 10 year bond yields in %
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I Crises are more than ‘volatility’ US 1929
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S&P Composite crash
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I Crises are more than ‘volatility’ Effect of the 1929 crash an the Great Depression on Belgium
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Belgian unemployment rate in %
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Belgian stock market index
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I Crises are more than ‘volatility’ Effect of the 1929 crash an the Great Depression on Belgium
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Belgian 10 year government bond yield
(in %)
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Belgian stock market index
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I Crises are more than ‘volatility’ The Japanese equity and real estate crash (1990)
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Japanese equity and real estate (Jan 1985=100)
Equity (MSCI)
Listed real estate (Topix)
+422 ppt
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I Crises are more than ‘volatility’ Effect of Japanses crash in bond yields (1990s)
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Japanese 10 year government bond yield in %
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I Crises are more than ‘volatility’ Financial sector crisis since 2007
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EMU Corporate BBB financial spread to swap in
bps
Financials
+24 ppt
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Overview
Part I: Crises are more than ‘volatility’
Part II: Crises are inevitable
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II.1 Irrational markets facilitate crises
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II.2 Bubbles are possible because of market irrationality
In efficient markets, irrational exaggerations are highly unlikely
However, grounds for market inefficiency and irrationality include
“The limits of arbitrage” (Shleifer and Vishny, 1997): there are cost of arbitration, e.g. the fate of LTCM in 1998
Keynes’ “greater fool” game
Psychological biases (Daniel Kahnemann, “Prospect theory”): investors’ utility is reference based, e.g. on the profits earned by others
Multiple equilibria exist, dependent on expectations. Changes are often triggered by expectation ‘shifts’ (Kindleberger’s ‘displacements’)
Exaggeration cycles are inherent in market economies
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II.3 Bubbles are possible because of the ‘debt nature’ of our money
Our ‘money’ is a debt certificate of the state or a private economic agent
Coins and bills are debt certificates of the state
They are legal tender,…
… in particular they can be used to settle tax debt towards the state
All other money (deposits, accounts, etc…) are debt instruments of private agents, mostly banks
Since our monetary system is based on debt and credit, occasional crises are not avoidable
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II.4 Fractional Reserve Banking increases vulnerability to crises even more
Deposits
10% Reserve
Requirements Loans Total 'Broad money'
10000 1000 9000 10000
9000 900 8100 19000
8100 810 7290 27100
7290 729 6561 34390
6561 656 5905 40951
5905 590 5314 46856
5314 531 4783 52170
4783 478 4305 56953
4305 430 3874 61258
--- --- --- ---
--- --- --- ---
--- --- --- ---
2824 282 2542 74581
--- --- --- ---
--- --- --- ---
--- --- --- ---
1 --- --- 100000
Total: 100000 10000 90000 100000
10.000 EUR become 100.000 EUR
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II.4 Fractional Reserve Banking Multi equilibria create a ‘coordination problem’
Person A and B each have deposits of 100 in the bank
As a result of Fractional Reserve Banking, the bank has only reserves of 10
Each person has two possible strategies: to withdraw or not
In the case of no withdrawal, the fact that the person can use the bank’s service (safety of deposits and payments) has an additional monetary value of 1
There are two stable Nash equilibria
The existence of a lender of last resort can shift the Nash equilibrium from bank run to the cooperative equilibrium
Withdraw Do not withdraw
Withdraw (5,5) (10,0)
Do not withdraw
(0,10) (101,101)
Pay-off matrix
Person B
P
e
r
s
o
n
A
Two stable Nash equilibria
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II.4 Fractional Reserve Banking increases vulnerability to crises
Defining property: not all deposits covered by reserves
This makes the system vulnerable to bank runs
Instability is NOT caused by the nature of fiat money, it applies to gold standard as well
One way to avoid bank runs would be a system of Full Reserve or 100% Reserve Banking (Milton Friedman)
Problem: liquidity provision, banking sector cannot play its role of financial intermediation, leading to credit crunch
Not currently practiced as a system
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II. 5 Saving for the future also requires debt accumulation
Saving in fixed income asset = creating a claim on future output
But: someone must promise to give up that part of future output, i.e. incur debt
Implication: saving and debt are two sides of the same coin: one’s savings are someone else’s debt
Savings are only possible to the extent that someone else is prepared to incur debt for the same amount
If literarily all debts are repaid, all money would disappear and our monetary system would collapse: back to barter trade
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Overview
Part I: Crises are more than ‘volatility’
Part II: Crises are inevitable
Part III: History repeats itself
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III.1 Anatomy of crises Kindleberger-Minsky model
Displacement
Boom
Euphoria
Crisis
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III.1 Anatomy of crises Kindleberger-Minsky model
Displacement
Boom
Euphoria
Crisis
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III. 1 Anatomy of crises ‘Displacement’ phase
A ‘displacement’ is an long and pervasive exogenous shock to the macro-economic system that changes expectations and perceived profit opportunities
Outbreak or end of wars
Widespread adoption of new inventions (IT, transportation)
Unexpected change of economic policies, e.g. financial deregulation or disinflationary monetary policy since the early 1980s
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III.1 Historically ‘displacements’ can boost economic growth enormously…
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574
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Real world GDP per capita (1913 = 100)
Source: Angus Maddison (2001); IMF; UN
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…especially when supported by globalisation Lower barriers to exchange and communication
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250Ocean freight (per ton)
Air transport (per 100 passengermile)
Telephone call (3 min. New YorkLonden)
Decreasing costs of transport and communication (in 1990 USD)
Source: IMF; WTO
and lower average world import tariffs
(for members WTO, in %)
Ave
rage
im
po
rt ta
riff
in %
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III.1 Anatomy of crises Kindleberger-Minsky model
Displacement
Boom
Euphoria
Crisis
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III.1 Anatomy of crises The ‘boom‘ phase
The changed perception of profit opportunities leads to increased investment and production
This phase is fuelled by a strong expansion of credit
The expansion of credit is inherently unstable (see also earlier) Minsky’s ‘Financial Instability Hypothesis’
Credit is unstable and inherently pro-cyclical
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III.1 Anatomy of crises Kindleberger-Minsky model
Displacement
Boom
Euphoria
Crisis
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III.1 Anatomy of crises The ‘euphoria‘ phase
Speculative investors appear
Growth is increasingly driven by leverage via the credit channel, ultimately leading to exaggeration
Three types of investors, with decreasing quality of debt: hedge, speculative and Ponzi investors
The average quality of debt gradually deteriorates
The boom becomes increasingly debt driven
“There is nothing so disturbing to one’s well being and judgment as to see a friend get rich.” (Anna Schwartz)
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III.1 Types of finance
Hedge finance
• Capital and interest can be financed by cash-flow from investment
Speculative finance
• Only interest can be paid from cash-flow from investment
• For capital repayment, the investor relies on new credit or rolling-over of existing debt
Ponzi finance
• For both capital and interest payments, the investor relies on capital gains on his aquired asset D
imin
ishin
g q
ualit
y o
f debt
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• Warren Buffet (2003) : “Credit default swaps are financial weapons of mass destruction”
• Paul Volcker (2009): “The only real innovation of the past decades in the financial industry is the ATM”
III.1 Innovation in the financial sector plays an ambiguous role in the ‘euphoria’ phase
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III.1Anatomy of crises Kindleberger-Minsky model
Displacement
Boom
Euphoria
Crisis
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III.1 Anatomy of crises The ‘crisis‘ phase
The key mechanism leading to the crisis, is the accumulation of debt of increasingly worse quality
New entrants to speculation are increasingly balanced by insiders who wish to withdraw
The price of the speculative assets fall and some speculative or Ponzi investors are unable to repay their loans
Possible triggers include:
the failure of a bank (e.g. Lehman)
the revelation of a swindle (e.g. The original Ponzi scheme)
Sudden realisation that the speculative asset is overpriced (e.g. the Amsterdam tulips)
Rush to liquidity to ‘liquidate’ the speculative asset and deleverage
Credit crunch: banks cease to lend on the collateral of such assets
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III.1 Anatomy of crises The ‘crisis‘ phase
Like speculation, the ‘liquidation’ process is feeding on itself
The process stops when
either asset prices have fallen so much, that some investors are willing to invest in the less liquid asset again;
or trade is cut off or suspended;
or sufficient liquidity is provided to meet the demand for cash
- the need for a ‘lender of last resort’
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III.2 Historical examples
The Amsterdam Tulip mania (1636-37)
The South Sea bubble (1720)
German hyper inflation (early 1920s)
Displacement Boom in war against Spain
Treaty of Utrecht 1713: British (slave) trade with South America
Treaty of Versailles
Speculative asset
Tulip bulbs, among other things
South Sea Company shares
German FX debt denominated in gold
Monetary expansion
Private credit Sword Blade Bank
German central bank
Lender of last resort
None Bank of England (since 1694)
none
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III.2 Historical examples
Wall Street crash (1929)
Japanese real estate and stock market crash
Asian crisis 1997
Displacement End of post-war boom
Economic expansion phase
Financial deregulation, exchange rate pegs
Speculative asset
US stocks Nikkei shares and land
e.g. real estate, unsustainable investments
Monetary expansion
Stocks bought on margin-calls
from low interest rate policy
Bank lending
Lender of last resort
Federal Reserve Bank of Japan IMF, World Bank, ADB
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III.2 Historical examples
Sharp rise US bond yields (late 1960s and 70s)
US sub-prime crisis (2008)
EMU Sovereign debt crisis (2010-)
Displacement Transition from Bretton Woods system to pure fiat money
Financial deregulation, idea of ‘ownership society’
Creation of EMU, leading to artificially low interest rates
Speculative asset
Overly expansive economic policy
US real estate Rising Sovereign debt
Monetary expansion
Via current account deficits
Bank credit, Originate-and-distribute model
International capital markets
Lender of last resort
Rest of the world and Federal Reserve
Federal Reserve ECB to some extent (OMTs)
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Overview
Part I: Crises are more than ‘volatility’
Part II: Crises are inevitable
Part III: History repeats itself
Part IV: Lessons can be learned
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IV.1 Kindleberger Minsky model as early warning Bitcoin ?
USD per Bitcoin
+1100%
Bitcoin euphoria 2013
Displacement Fear currency debasement by malicious governments. Criminal opportunities
Speculative asset
‘Bitcoins’, with no intrinsic value nor legal tender
Monetary expansion
Private credit A lot of ‘Ponzi’ investors
Lender of last resort
None
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IV.1 Kindleberger Minsky model as early warning Federal Reserve balance sheet ?
Fed balance sheet (local currency, Jan 2007=100)
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Fed ECB
Central banks’ balance sheet expansions since 2008
Displacement Fear of new Depression. This time it’s different: monetary expansion is non-inflationary
Speculative asset Large scale buying of US Treasuries and Mortgage Backed Securities = credit provision. Rollovers are consistent with Minsky’s speculative investors
Monetary expansion
Credit expansion via creation of central bank money
Lender of last resort
Federal Reserve
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Outstanding amount of private debt (in % of GDP)
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US
EMU
China
IV.1 Kindleberger Minsky model as early warning Chinese debt crisis ?
Chinese investment boom and debt build-up after 2008
Displacement Start Quantitative Easing Federal Reserve in combination with RMB peg to USD. China ‘imports’ US expansionary monetary policy
Speculative asset Investment boom financed by cheap credit
Monetary expansion
Credit growth facilitated by monetary inflow from US and artificially low interest rates
Lender of last resort
Chinese central Bank
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IV.1 Kindleberger Minsky model as early warning Chinese debt crisis ?
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180 Private sector debt in % of GDP
Share of new credit other thanbank loans (in %, right)
“Shadow financing” increasingly important
Chinese investment boom and debt build-up after 2008
Displacement Start Quantitative Easing Federal Reserve in combination with RMB peg to USD. China ‘imports’ US expansionary monetary policy
Speculative asset Investment boom financed by cheap credit
Monetary expansion
Credit growth facilitated by monetary inflow from US and artificially low interest rates
Lender of last resort
Chinese central Bank
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IV.1 Kindleberger Minsky as early warning Emerging Markets: could 1997 crisis happen again ?
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Latin America Asia ex China
External deficits Emerging Markets building up again (current account balances, in % of GDP)
Rising external deficits Emerging Markets since mid-2000s
Displacement Low global bond yields (savings glut). End of commodity and energy super-cycle
Speculative asset Investment boom
Monetary expansion
External deficits financed by inflow of first FDIs than of portfolio investments
Lender of last resort
None (IMF to some extent)
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IV.1 Kindleberger Minsky as early warning Emerging Markets: could 1997 crisis happen again ?
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Private sector
Public sector
Brasil India
Rising private and public sector debt (in % of GDP)
Turkey
Rising external deficits Emerging Markets since mid-2000s
Displacement Low global bond yields (savings glut). End of commodity and energy super-cycle
Speculative asset Investment boom
Monetary expansion
External deficits financed by inflow of first FDIs than of portfolio investments
Lender of last resort
None (IMF to some extent)
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IV.2 Lessons can be learned: regulation and policy
Regulation and policy institutions can help to avoid crises…
e.g. by fulfilling the role of Lender of Last Resort
Regulation to make credit growth less pro-cyclical (e.g. Basel III: counter cyclical capital buffers)
Expectation shift by creation of OMTs by the ECB
- The ECB promises to do ‘whatever it takes’ (i.e. buy potentially unlimited amounts of sovereign bonds) to prevent a forced EMU exit of member states
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IV.2 Lessons to be learned: regulation and policy
… or cause them
Example 1: “regulation” creating destructive incentives:
- Role of rating agencies partly based on regulatory definition of risk weighted assets
- Stress test in financial sector leading to further deleveraging
Example 2: the Tulip mania in the Netherlands in 1620s
- Change in legislation with respect to tulip futures and options contracts
Example 3: financial deregulation after the ‘80s
- E.g. originate and distribute model via financial engineering (packaging and selling risk)
- Volcker: ‘The only useful financial innovation in the past 30 years was the Automated Teller Machine (ATM)’
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IV.3 Lessons for quantitative risk management: the illusion of safety
Black Swans: an event with a digital probability distribution is virtually unmanageable
“Fat tail” risks can be addressed by using appropriate alternative distributions
However, statistical distributions are not stable (invariant) over time
Are “fragile” under stress (Taleb Nassim)
Correlations in crisis times tend to rise
What was thought to be unlikely, is not unlikely at all
Probability distributions tend to change just at the time they are needed
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IV.3 The example of Value at Risk
Consider a portfolio consisting of assets A and B with equal weights.
The variances and covariance of their returns are respectively Var(A), Var(B) and Covar(A,B)
The portfolio return variance then equals
Var (portfolio) = 𝑉𝑎𝑟 𝐴 +𝑉𝑎𝑟 𝐵 +2 𝐶𝑜𝑣𝑎𝑟(𝐴,𝐵)
2
This means that the variance (and hence the standard deviation) of the portfolio return increases as the correlation between the assets increases, all else equal.
This is precisely what happens in financial crises
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Implied volatility spikes in times of crises… (in %)
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IV.3 The example of Value at Risk Equity volatility increases sharply in times of financial crises
…such as the fall of Lehman (2008) (implied volatility in %)
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IV.3 Value at Risk of 24.9%...
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A (normal) return distribution with mean 8% and standard deviation of 20%
Value at Risk = 24.9% (with 5% probability)
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… is really 41.3% in times of crisis
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0.005
0.01
0.015
0.02
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
(Normal) return distributions with mean 8%
STDEV = 30%
STDEV = 20%
Value at Risk = 41.3% (with 5% probability)
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IV.3 Lesson for quantitative risk modelling
Time dependency of correlation data creates an illusion of safety
“In complex systems, such as financial systems, correlations are not constant but vary in time. [...] The average correlation among stocks scales linearly with market stress. [...] Consequently, the diversification effect which should protect a portfolio melts away in times of market loss, just when it would most urgently be needed.” (Preis et al. (2012))
One way to address time-dependency of risk models could be using state-dependent correlation data, i.e. conditional (state dependent) instead of unconditional correlations
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Overview
Part I: Crises are more than ‘volatility’
Part II: Crises are inevitable
Part III: History repeats itself
Part IV: Lessons can be learned
Part V: Takeaways
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V Takeaways
Market movements during financial crises are much stronger than normal volatility
Occasional financial crises/bubbles are unavoidable
Our financial system is inherently unstable (debt money, fractional reserve banking,…)
There are limits to rational behaviour of economic agents
Credit cycles are at the core of most financial crises
A typical crises consists of several phases: displacement, boom, euphoria and bust
This model can be applied to identify potential new crises
Financial regulation can mitigate crises, or exacerbate them
A false sense of safety in quantitative risk management should be avoided