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Macroeconomics and Financial Issues in a Global Context
A Dynamic Approach To The 2007 Financial Crisis
Zanyar Golabi Azin Aliabadi Ugure Anlar
Sriram Ramanthan
5/7/2012
Supervisor: Prof. Calvo
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Introduction:
In this report, we provide a simple dynamic model to identify the main dynamics that
led to the housing crisis and the (2007) financial meltdown in the United States. In addition,
we briefly examine the effect of this crisis on the debt crisis in some European country. Our
dynamic model differs from previous research in the sense that we examine the interactions
among the main sectors that contributed to the crisis, namely government, debt-deficit
dynamics, central bank, banking sector and housing market. In other words, instead of
exploring a certain sector in depth, we look at the big picture.
Even though it is difficult, if not impossible, to model every single contributing factor in
the financial crisis, the necessity of having a systematic understanding of the financial
crisis motivated us to take a systematic approach. A balanced and well thought out policy
response to this crisis and possible future crises requires a systematic approach. Our model
can help policy makers see the effect of their policy on critical variables in the system by
observing the behavior of the system through running simulations and conducting scenario
planning and sensitivity analysis.
A review of the crisis and the time line:
In the immediate aftermath of the Glass Steagall Act‘s passing bankers operated in a
Goldilocks’s world often known as the 3-6-3 model. They borrowed at 3%, lent at 6%, and
got to the golf course to cultivate clients by 3pm. Glass Steagall separated investment
banking, which was risky finance from commercial banking which took in deposits from the
general public and thus needed to be insulated from all that risk. Almost from the
beginning, banking was under attack from innovations in other areas as other entities tried
and often succeeded in attracting assets from the public. The commonly accepted timeline is
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presented in Appendix A. For much of 2007-08 the markets entered into a free fall as the
low interest rates and volatility of 2003-06 ended and financial engineering models that are
premised on these attributes start misbehaving.
Financial Engineered models:
Mortgage-backed security, hereafter defined as an MBS, is a form of asset-backed security
classified under the umbrella of fixed income. Asset backed securities generate income
payments to its holder through an underlying collateral. The income generated from an
MBS is derived from a pool of mortgage loans. The process by which the loans are pooled
together and sold as an asset, is known as securitization. In principle, by pooling together a
diversified group of (imperfectly correlated) securities, securitization lowers the underlying
level of risk associated with these securities. The pooled mortgages are then sliced
according to their risk level (known as tranching) and rated accordingly. Each MBS has a
different level of risk, return, rating and yield. Similar to all securities, the level of risk is
directly proportional to cash flow payment, with the highest level of payments coming from
the lower rated tranches. In turn, MBS securities are further tranched into other securities,
such as collateralized mortgage and debt obligations, hereafter denoted as CMO’s and
CDO’s. This innovation, attempted to match the risk/return profile of securities to the needs
of investors by allowing banks to attract different investors according to their risk profile.
The three main MBS rating agencies have been S&P, Moody’s and Fitch. The main
originator agencies for an MBS are: Fannie Mae, Freddie Mac and Ginnie Mac.
Similar to a put option, a homeowner has the option to default on his/her mortgage.
Assuming all other factors remain constant, the underlying reasons for a default are linked
to: the value of the home; an income shock; increase in costs; economic climate; and
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interest payments. If the default value is higher than the current value of the house, the
borrower has a sufficient reason to default. The incentive for default is tied to the capital
loss. If the initial purchase price of the house, which determines the monthly interest
payment, and the outstanding mortgage debt, is greater that the home value, the borrower
will default on the loan. The two primary drivers of defaults have been unemployment and
a decrease in home prices. The below diagrams capture the dependence of mortgage
defaults on home prices and unemployment:
Figure 1. The Dependence of Mortgage Defaults on Home Price appreciation
Figure 2. The Dependence of Mortgage Defaults on Unemployment Rate
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As previously noted (See Appendix A), from 2000-2004 the inflow of available credit coupled
with low interest rates, and greater deregulation of credit, increased the aggregate demand
for homeownership. In turn, the demand for home ownership increased the price of homes.
Home prices increased 2.94% per annum from 1994 to 2005. From 1980 to 2005, as banks
increased their holding of MBS, rating agencies had to rate more securities. The biggest
clients of rating agencies became banks and the restrictions placed on ratings decreased
dramatically. Several factors have been attributed to the lack of proper ratings of MBS.
These factors include the increase in demand for ratings, the revenue generated from
ratings, the recycling of ratings between agencies, and the reclassification of these
securities. Concurrently, the securitization of loans was correlated with lower lending
standards, and an increase in mortgage credit (Mian and Sufi, 2009), primarily because
banks were no longer holding or reporting these loans on their balance sheets. In short,
banks simply became loan servicers, collecting money from borrowers and channeling it
into fixed income securities. Since banks no longer held the loans long terms, they had less
of an incentive to analyze the risk associated with these securities.
The Model
Based on our findings, we have developed a dynamic model that helps us understand
the interactions among the main variables that contributed to the evolution of the housing
bubble formation, subprime mortgage crisis, global financial meltdown, the credit crunch
and the sovereign debt crisis. We explore banking transactions, government sector
dynamics, housing market evolutions and main macroeconomic dynamics in depth to find
the key driving forces of the recent crisis. Our dynamic model is useful in identifying and
displaying the driving causes of these dynamics. It is also useful in examining policy
responses and their effectiveness. After modeling these sectors (in the next step) we run
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the model and explain the results of the simulation. We then study the system’s behavior
under possible future scenarios and examine the impacts of different policy packages on the
system’s behavior. We show how the availability of subprime mortgage loans leads to an
excessive building rates in housing markets and in turn an excessive inventory of housing
stock (housing bubble formation). In the next steps, we show how the bubble bursts, the
price of houses depreciates, and how this event leads to an increase in bank insolvencies.
We also explore the quality of the linkage between the financial crisis and the sovereign
debt crisis through our debt-deficit model. Later in this report, we explore the effectiveness
of policy packages in correcting the behavior of the system and in resolving the financial
crisis in the U.S. We show that stimulus packages must cross a certain threshold to
effectively correct the disastrous behavior of the system.
Figure1 (Appendix B) shows a very simple causal model that captures the main
dynamics of the financial meltdown and the debt crisis. As shown in the model, this is
indispensable to the study of the domino effect and the chain of events that occurred in
creating the financial crisis. Our model helps to portray a holistic image of the recent crisis.
Any corrective action that tends to ignore these dynamics is subject to failure, as all the
major variables in the system need to be taken into account to correct the behavior of the
entire system, not just one portion of it. We use a simplified dynamic quantitative model to
capture these dynamics. We explore banking transactions, housing markets, economic
environment and central bank transactions to model the system.
Bank Transactions
The main assets that we take into consideration in the banking sector are provided
below:
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1. Cash ( ): Cash actively interacts with all other assets. Cash is the main source of
liquidity in the banks.
2. Short-term securities ( )
3. Mortgage-backed securities ( )
4. Stock of houses owned by banks ( )
5. Asset-backed securities except for mortgages ( )
The dynamics of securitization, shadow banking practices and credit-creation are
modeled, and the model is simplified enough to capture the main dynamics. However, it is
indispensable to notice that these dynamics can further be improved to capture the real
dynamics as they interact in the real world by working more on the formulas and their
attributes and by forgoing some of the main assumptions.
Bear Stearns, Lehman Brothers, and AIG’s failures were one of the leading forces of the
recent financial crisis. Most of the policy reactions have been formed to either prevent a
further collapse in the financial market by applying some policies that impose higher
capital requirements on leading financial institutions or to mitigate the unpleasant
consequences of collapses by bailing out large financial firms. However, as our model shows,
the problem was not the collapse of one or two systematically important financial
institutions, but rather the collapse of the entire financial market and more importantly,
the collapse of the asset-backed securities market. The contraction and collapse of ABS
market was disastrous in the sense that it caused a huge contraction in the supply of credit
to customers and businesses. This in turn contracted aggregate consumption and
investment, which finally led to the contraction of aggregate output. These dynamics are
fully captured in our model. As the value of mortgages fell below their cost, households
defaulted on their loan, causing a contraction in the supply of liquidity in the banks. At the
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same time, the value of houses that banks owned (or took over through foreclosures
mechanisms) declined sharply as the bubble burst. The formulas in Appendix (C) are
explained in a way that indicates these dynamics.
The dynamics that are explained in our model are:
1. The role of ABS in the process of credit creation.
2. The mechanisms by which the market operates. We show how different types of
loans, namely mortgages, credit-card loans, student loans and car loans are designed
into ABS, and how investors (e.g, hedge funds and pension funds) acquire them.
Since we want to capture the dynamics which caused the subprime mortgage crisis
in the first place, we divide ABS into two classes:
a) Mortgage-backed securities
b) All other ABS securities that are not structured as an MBS.
3. Third, the driving causes that lead the securitization are considered in our model;
The risk sharing process and risk pricing models are not fully explored in our model,
but are mentioned partially, however, the capital requirements and their
inefficiency are explained in more depth.
4. Some of the factors that led to financial fragility and global volatility are also
partially discussed in our model.
5. Finally, policy reactions and their effectiveness are considered in both our banking
transaction model and in our credit crunch model.
All the formulas in the banking sector of Appendix C are derived from the formulas in
our international capital market course. There haven’t been major theoretical formulas
regarding bank transactions that can capture those dynamics. There has been a huge
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gap between the academic world and the practical world on these practices. Therefore,
we had to look for more practical sources. We could find main formulas as they are
really used in banks, hedge funds and pension funds, presented in our international
capital market class.
Credit Crunch In Banks
As shown in the figure 2. (Appendix B), if bank’s net worth (which is a function of bank’s
insolvency ratio) decreases, the share price will decrease as well. This dynamic creates a
panic in the market and further leads depositors to take their deposits away from banks. As
a result, bank’s liquid assets, and their net worth, further decreases. Without an external
intervention, this vicious cycle will continue to decrease banks’ liquid assets. In other words,
this vicious cycle works as a reinforcing loop and external intervention is needed to bring
back the system to equilibrium. Subprime mortgage crisis induce this potentially disastrous
cycle by increasing banking insolvency and creating a moral hazard. This panic (which later
had damaging consequences) needed an external intervention.
Real Estate Sector Dynamics
Housing market cycles are a well-known phenomenon all over the globe (Harris, 2003)
These cycles are critically important because of their effects on both investment decisions
and macroeconomic performances (Pyhrr, 2003). Furthermore, their impacts are not
restricted to the economy, they can cause major political and social dynamics (Weiss, 1991).
Providing an analytical framework to analyze these cycles is complex (ECB, 2003;
Harris, 2003), but dynamic modeling of complex systems has flourished enough to enable us
to simulate the model and analyze complex relationships (Forrester, 1991; Homer and
Olivia, 2001). Even though the traditional approach toward business cycles may help us
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understand these cycles, they don’t help to capture the big picture and thus, may cause
confusion and misunderstanding. A famous study by Kaiser (1997) sheds light on this
matter. He explored real estate cycles from different origins in different periods. His work
was challenged by Wheaton (1999) who, by applying a dynamic approach, shows that
market parameter changes can significantly change the dynamics of these cycles. Dynamic
modeling has been used in studying the real estate sector. Structures that cause the cyclical
behavior is taken into consideration in most of these researches. Speculative attacks on the
real estate sector and their contributing role to cycles are explored in a research carried out
by Malpezzi and Wachter (2003). Many articles focused on housing market dynamics and
analysis (Wheaton William C., 1999; Blank, 2009; G. Meen, 2000).
The main goal of our research is to identify cycle-producing mechanisms and cyclical
behavior of real estate markets and in turn the subprime mortgage crisis. The analysis and
modeling of this market’s behavior is so intricate that creating an analytical framework
requires major theoretical work (Blank, 2009). An example of a quantitative mathematical
framework is documented by Poterba (1984) who highlights the mechanisms of an
adjustment in the real estate sector based on demand shocks. He displays that the
appreciation of house prices results in short term profits and in the supply of houses, which
in turn increases construction rates and leads to an even more excessive supply of houses.
This is one of the main driving causes of subprime mortgage crisis. This accumulation will
cause depreciation in house values. G. Meen and M. Andrew work contributed to this topic.
They studied the housing market from 1990 to 2006 and applied this approach to the
housing markets in the U.S and the U.K. Also in a similar study, G. Meen observed the
correlation among price of houses, rate of construction, cost of construction, and interest
rates using econometrics approaches (VAR and VECM). He considered the modification
mechanism of markets and the effects of important factors on supply, demand, and price (G.
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Meen, 2000). Figure 3 (Appendix B) shows the dynamics of housing market. All the related
formulas are presented in the appendix C and derived from the aforementioned sources. We
had to simplify (and in some cases) modify these formulas based on the scope of our project
and our main objective, which is to identify the drivers of the crisis; the intersection of
banking sector; housing markets; and macroeconomic drivers. The references for our models
are mentioned in appendix C.
Debt Dynamics and Macroeconomic Variables
In this section, we present a simple model that capture debt dynamics, the interactions
among major variables and the intersection of debt dynamics and banking transactions.
The relationship between banking vulnerability against debt crisis on a national level is
also taken into account. This model helps us understand the current sovereign debt crisis in
major European countries. In order to evaluate the impact of major shocks in economic
variables, notably net international reserves and exports on the banking sector credit
crunch, we use a dynamic model. The principal model is composed of three main players:
the economic environment including government, banks and credit rating agencies. In the
model not only financial institutions, but also governments can default on their debt. The
role of probability of default is partially investigated in our research. The banks will default
when their capital stock is less than a certain threshold. That threshold is a function of
credit risk which itself is contingent on the probability of default on a national level. This
model can be helpful in explaining the emerging market debt crisis, but since nowadays
some of the advanced countries show the same signs of vulnerability to default, we can
apply this model to both developing and developed economies. Both domestic and
international capital markets play major roles in these dynamics. For this reason, we
separate domestic and international capital market in our dynamic model. The debt-deficit
cycle, for instance in Greece, affects the exposure of domestic capital markets to credit risk.
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Especially during a debt crisis, banks had to undergo haircuts to face the liquidity crunch.
This is in part due to the fact that the process of liquidation cannot be performed normally,
since all domestic banks need to liquidate when faced with a debt crisis, which simply not
possible given the credit crunch and possible sudden stops. The government identifies the
level of debt and its elements, the process and level of debt repayments, tax rates and tax
policies and level of financing from internal and external sources. Figures (7) and (8)
(Appendix B), capture the public domestic and external financing dynamics, whereas figure
(7) (Appendix B), debt dynamics, explains the debt accumulation process. The government
casual model (figure 8 (Appendix B)) describes mechanisms by which the government
collects taxes and spends. These three figures and models are used to explain the way that
debt and deficits evolve gradually. The probability of a default indicates the likelihood of
the default of the country. Credit rating agencies make these assessments. The formulas
are presented in appendix (C).
Simulation Results:
The red line in the following figures show a situation in which system operates normally.
Let’s assume that we impose a shock to the system by increasing available loans for
subprime mortgages. As shown in the following figures, the building rate increases
significantly as a result of the availability of credit for these types of mortgages.
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Figure A. An Increase In Subprime Loan Availability Increases Building Rate
Meanwhile, one of the main variables is mortgage payments, which will decline
significantly and consequently will cause an increase in foreclosures.
Figure B. Mortgage Payments
Figure C. Foreclosure value
The reasoning behind this occurrence can be explained through the domino effect
(please refer to Appendix D). At first, the building rate increases and banks have enough
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capital to inject this capital into the market. After a while, expected mortgage payments
increase and when the expected mortgage payments exceed the affordability of households
(a function of the household incomes), they start to default and the foreclosure value
increases as a result. As a result, when banks lose their liquid assets and the total asset of
the banks decline, banks insolvency increases. Following figures show how bank insolvency
increases with this shock.
Figure D. Banks insolvency
The following figure captures the output contraction and unemployment increase in one
graph.
Figure E. Output contraction and unemployment rate
Note that it takes time for the output to contract and for unemployment to increase
from the time that we impose the shock until the time that the crisis happens. The
unemployment rate increases because of the decline of investment due to the lack of bank
credits. Now, we want to examine the effect of government stimulus. Imagine that at the
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time of the crisis, government introduces the stimulus package. Figure F shows that
reactionary policy:
Figure F. Government stimulus
Mortgage payment will react with a delay, however it increases. Figure G captures this
dynamic.
Figure G. Mortgage payments increase with the introduction of stimulus package (with
delay)
Output grows as a result of this reactionary policy by government. The blue line captures
the reaction of output to this policy. The redline shows a situation in which the stimulus
package is less than that of blue line. By changing the level of stimulus package, we can
find the threshold at which the stimulus packages can actually correct the economic
behavior of the system.
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Figure H. Output growth with two different stimulus packages
Note that there is a limit for the stimulus package size, because the debt accumulation
dynamics act as a balancing loop. First, the government expenditures increase:
Figure I. Government expenditure increases
Then as a result of borrowing to finance deficit, the debt starts to pile up:
Figure J. Debt accumulation Dynamics
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To explain the debt dynamic, we have to notice that external shocks will affect the economy. A
decline in countries’ GNP and international reserve (net) can be explained as a result of those
shocks. Debt goes up through borrowing (to finance deficit) and of course, through accumulation
of interest payments (As explained in Appendix C). The increase of debt will increase principal
and interest payments. Meanwhile, government money balance goes down because the
increasing government expenditures pull it down (Explained in Appendix C). The formula at
Appendix C shows that if total expenditure exceeds total revenue, the budget deficit increases. In
order to finance the deficit, government has to borrow from internal and external sources (or
through inflation tax and seniorage). This dynamics offset the impacts of balancing loop as the
reinforcing loop takes over and create a vicious cycle. However, the downgrade of the
government bonds and also interest rate adjustments will further exacerbate this vicious dynamic
(As explained in Appendix C, when probability of default goes up, the adjusted market interest
rate will go up as well. Meanwhile, the increasing probability of default coupled with credit and
rollover risks further intensifies the debt accumulation process). The downgrade of government
bonds not only affects the adjusted interest rate and deteriorates the debt stock, but also it leads
lenders to lend short-term.
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References:
David M. Blank and Louis Winnick. The Structure of the Housing Market [Journal]- The MIT Press,
2009.
G. Meen Housing Cycles and Efficiency [Journal]- Scottish Journal Of Political Economy, 2000. - Vol.
47, No. 2.
G. Meen M. Andrew, On the Aggregate Housing Market Implications of Labor Market Change
[Journal]-Scottish Journal of Political Economy, 1998. - pp. 393-419.
James M. Poterba Tax Subsidies to Owner-occupied Housing: An Asset-Market Approach [Journal].
The Quarterly Journal of Economics, MIT Press, November, 1984. - Vol. 99(4). - pp. 729-52.
John M. Quigley and Steven Raphae Is Housing Unaffordable? Why Isn't It More Affordable?
[Journal]: The Journal of Economic Perspectives, 2004. - Vol. 18. - pp. 191-214.
DiPasquale Denise Why Don't We Know More About Housing Supply? Chicago: 1997.
ECB. Structral Factors in the EU Housing Market. - Frankfurt: European Central Bank, 2003.
Harris Ian Market failure and the London housing market. - London: Greater London Authority
(GLA), 2003.
Herring Richard and Wachter Susan Bubbles in Real Estate Markets // Asset Price Bubbles:
Implications for Monetary, Regulatory, and International Policies. - Chicago : 2002.
Kaiser Ronald W. The Long Cycle in Real Estate. : Journal of Real Estate Research, 1997. - 3 : Vol.
14.
Malpezzi Stephen and Wachter Susan M. The Role of Speculation in Real Estate Cycles // joint
meeting of the American Real Estate and Urban Economics Association and the Asian Real Estate
Society. - Seul : 2002.
Mueller Glen R. What Will the Next Real Estate Cycle Look Like? : Journal of Real Estate Portfolio
Management, 2002. - Vol. 8.
Patrik wilson John Okunev Spectral Analysis of Real Estate and Financial Asset Markets - Sydney :
Journal of Property Invesment and Finance, 1998. - Vol. 19.
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Wheaton William C. Real Estate Cycles: Some Fundamentals. - Boston: Journal of Real Estate
Economics, 1999. –Vol 27
William C. Wheaton Real Estate Cycles: Some Fundamentals [Journal]. - Boston : Journal of Real
Estate Economics, 1999. Vol. 27
Bartolini and Cottarelli (1994): “Government Ponzi Games and the sustainability of Public Deficits
under Uncertainty,” Ricerche Economiche 48, 1-22.
Blanchard, O. J. (1984), “Current and Anticipated Deficits, Interest Rates and Economic Activity,”
European Economic Review 25, 7-27.
Blanchard, O. J. and S. Fischer (1989), Lectures on Macroeconomics, (MIT Press, Cambridge,
Massachusetts).
Blanchard, O. J. and Weil, P. (1992): “Dynamic Efficiency and Debt Ponzi Games under Uncertainty,”
NBER Working Paper No. 3992.
Fedelino, A., A. Ivanova, and M. Horton (2009): “Computing Cyclically Adjusted Balances and
Automatic Stabilizers,” IMF Technical Notes and Manuals 09/05.
Girouard, N. and C. André (2005), “Measuring Cyclically adjusted Budget Balances for OECD
Countries”, OECD Economics Department Working Papers, No. 434, OECD publishing.
doi:10.1787/787626008442
Tanzi, V. (1977): “Inflation, Lags in Collection, and the Real Value of Tax Revenue,” Staff Papers,
IMF, Vol. 24 (March), pp. 154-67.
Tanzi, V., M. I. Blejer, and M. O. Teijeiro (1987): “Inflation and the Measurement of Fiscal
Deficits,” Staff Papers, IMF, Vol. 34 (December), pp. 711-38.
Eduardo Ley (2010):”Fiscal (and External) Sustainability”, Economic Policy and Debt Department,
PREM, the World Bank
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Appendix A. Time line of banking crisis from 1954
1. 1954 US stock market recovers from the Great Crash. Commercial banks are covered by
deposit insurance and barred from investment banking. Fixed exchange rates are linked to
gold under Bretton Woods. Commercial banks dominate finance and investment is
dominated by individuals. Investors cannot invest in commodities, foreign exchange, credit
default risk or emerging markets.
2. 1962 Fidelity creates the Magellan Fund, starts publicizing returns and launching large
funds with the aim of accumulating assets. League tables become the norm and fund
managers using other people’s money come to the fore.
3. 1966 The United States Congress took the highly unusual move of setting limits on savings
rates for both commercial banks and S&Ls. From 1966 to 1979, the enactment of rate
controls presented thrifts with a number of unprecedented challenges, chief of which was
finding ways to continue to expand in an economy characterized by slow growth, high
interest rates and inflation. These conditions, which came to be known as stagflation,
wreaked havoc with thrift finances for a variety of reasons. Because regulators controlled the
rates thrifts could pay on savings, when interest rates rose, depositors often withdrew their
funds and placed them in accounts that earned market rates, a process known
as disintermediation. At the same time, rising rates and a slow growth economy made it
harder for people to qualify for mortgages that in turn limited the ability to generate income
4. 1969 Launch of the first money market fund. The slow unraveling of relationship banking to
be replaced by a transaction orientation begins. The judgment of bankers is replaced by the
impersonal hand of the market and trading room. Money market mutual funds even offer
checkbooks and clients lose an important protection – deposit insurance.
5. 1971 Gold standard ends, bubbles become possible. Oil prices become the new standard.
6. 1975 First index fund launched and the benchmark as measuring stick comes into existence
encouraging herd behavior among money managers
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7. 1982 Launch of the first emerging markets fund. The World Bank rebrands Less Developed
countries as emerging markets. This new asset class is deluged by money seeking returns
causing these to become correlated with other mainstream markets s herding increased.
8. 1984 Ronald Reagan allowed investment banks to trade mortgage-backed securities, bonds
backed by pools of mortgages. This was a precursor to the shadow banking system which
started later.
9. 1990 Crash of Japan leads to the yen carry trade. Japan had a real estate bubble which
when it burst led the Bank of Japan to lower interest rates to near the zero lower bound.
This cheap money enabled equity investors to finance their investments this way allowing
the yen to become correlated with stocks.
10. 1992 When George Soros shorted the pound sterling his $3Bn profit from the trade caused it
to drop out of the tight band of currency rates in Europe known as the snake. Foreign
exchange became an asset class and other big investors began making bets on it, another
business that banks dominated came under pressure from these bets.
11. 1996 The Greenspan “put” his ability to maintain low interest rates causing the growth in
the equity markets as Baby Boomers crowd into the stock market to save for retirement,
removes more deposits from banks and into other asset classes, chasing returns.
12. 1997 Asian countries suffer a series of devaluations after following the Washington
Consensus and their citizens increased savings rates as they did not have any safety net in
case of adversity or to plan for retirement. Their governments followed moved from import
substitution industrialization policies to an export led growth policy. This increased their
stockpiles of dollars, lowering interest rates in the US, pumping more money into markets
and away from banks.
13. 1998 Global banking mega-mergers crest too big to fail banks that can only be bailed out by
the government increasing moral hazard and cause risk seeking behavior as they struggle to
remain relevant to depositors.
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14. 1998 Long Term Capital, with John Meriwether of Salomon and Myron Scholes (Nobelist)
and other luminaries melts down and has to be bailed out by the New York Fed in
conjunction with other commercial banks.
15. 2000 Dot com bubble bursts. Fed responds by lowering interest rates, feeding bubbles in
credit and housing and the formation of hedge funds.
16. 2010 The rise of the BRICS
17. 2004 Commodities become an asset class
18. 2005 Default risk becomes an asset class.
All three above are fueled by the search for uncorrelated asset classes. All three are
correlated to the whims of the huge money managers who move in and out in a herd thus
moving markets in unison on the way in and in common panic on the way out.
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Appendix B. Graphs and models
Figure 1. A simple model of financial and debt crisis
Figure 2. Main banking transaction dynamics
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Figure 3. Major banking transaction dynamics
Figure 4. Financial Meltdown and Credit crunch in banking sector
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Figure 5. stock-flow diagram of housing market (Major dynamics)
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Figure 6. Major public dynamics
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Figure 7. Debt dynamics
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Figure 8. Deficit dynamics
Figure 9. Debt-raising dynamics
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Appendix C. All formulas with explanations and resources
The changes in short-term securities are formulated as below:
min , /
This formula shows that short-term securities can easily increase only if the level of cash at
present time exceeds the cash value ( at the steady state of the system. is the processing time
for selling or buying short-term securities. The positive sign above shows the positive part of the
expression. As shown in the formula, short-terms securities conversion to cash is constrained by
their availability. Short-term securities are considered as the most liquid assets after cash.
The second asset is MBS. The changes in mortgage-backed securities can be formulated as below:
min , min , ℎ
Where p is the average price of a house unit, is houses’ buying rate, is payment rate that
households can afford, is the rate or repayment of mortgages. is the rate of change in mortgage-
backed securities because of foreclosures. is cycle time in mortgage payments, is the cumulative
periods of repaying mortgage loans before starting to default on loans and is the time we need to
process foreclosures. In other words, it is possible to write the rate of conversion from bank’s cash to
the houses that banks own as below:
/
As explained in the formula, if households get mortgage loans, this action will increase “m”. But
if they repay those loans, the value of “m” decreases and the value of cash increases. Meantime, “m”
will decrease if households fail to repay their loans and default on their loans. In this case, banks
will take over their property and assume ownership of them and as a result, this will increase the
value of “b”. One of the main assets of the bank that played a critical role in financial crisis was the
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value of the houses that banks owned. The market value of those houses changes with the following
rate:
minℎ. , /
Where “h” is the number of houses that are considered as banks’ property. The dynamic change
of the value of banks property in the form of houses is calculated below:
min ,ℎ
The net flow to other bank-assets or non-mortgage-backed securities can be formulated as below:
min ,
Where K is the bank available capital and can be formulated as below:
And L is bank’s liabilities. is aggregate output growth. In other words, we have:
/
and represents “time-to-production” and “loan request processing time” respectively. This
type of asset is used to fund businesses and other investments that are not related to housing market
directly. We will see that the crisis in banking sector will increase bank insolvency and it will cause
credit crunch which later affect the investment funding of other businesses as well and through that,
it will lead to output contraction as seen in the model.
At the end, we can formulate “rate of changes in bank’s cash” as below:
min , min ,ℎ
min ,
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As shown in the model, all inflows and outflows to the bank’s cash are formulated. Short-term
securities are assumed equivalent of bank’s cash and a source of liquidity. The subprime mortgage
crisis significantly decrease bank’s liquid asset including short-term securities and cash and causes
credit crunch as seen in our simulation results.
Real estate Sector:
In real estate sector, the dynamics are presented as below:
First of all, we want to see the supply and demand dynamics in real sector in the U.S and their
interactions with both banking sector and macroeconomic variables. We also take into account the
role of expectations in real estate sector that seems to be largely contributing to subprime mortgage
crisis. Expected purchasing rate of houses by household is a direct function of some major
macroeconomic variables, including mortgage interest rates, rate of unemployment and the
availability of capital loans. Of course it is also a function of the number of households that don’t own
a house of their own. The formula is shown below:
1 1 / 1
Where is number of households that don’t own a house of their own, is unemployment rate,
is capital availability for subprime loans and is mortgage interest rate. Expected purchasing rate
is a demand force. But on the other side, we have some supply forces that create dynamics in our
model. One of those forces is that number of houses that are for sale. The rate of those houses is
critically important. The formula for such a rate can be simplified as below:
ℎ / ℎ
ℎ represents the entire housing stock of the united states. ℎ is a non-variable time-function
that represents the house purchase processing time. Finally the purchase rate that can be financed
by banks are as below:
. min , / ℎ
31
Where represents the available capital allocated for financing house purchases. One of the
important features of housing market is “price elasticity” issue. Imagine that house values in the
steady state equilibrium is . In this situation, availability rate for on sale houses should be equal to
the expected purchasing rate. In other words:
ℎℎ 1 1 / 1
When the bubble burst, the system move from steady state and the price decline as shown with
the formula below:
Θ /
Where 0 0.
The stock of the houses that are occupied is important in identifying demand-supply mechanism. In order to identify the changes of the stock of those available houses that are not bank’s property, we have following formulas;
ℎ
Where is the rate by which selling and purchasing change the status of the houses from non-occupied to occupied and vice versa. is buying rate from market and is the rate of construction. The formulas for , , are as below:
min
ℎ Θ, min , /
min min , , ,
, , ,ℎ / ℎ
Where ℎ is the houses on which banks are assumed to have ownership and ℎ is the number of occupied houses. All the dynamics in this market can be summarized as below:
ℎ
32
ℎ/
ℎ ℎ
min min , ,
Almost all of the formulas for housing market are derived from following sources. We tried to
simplify them based on the scope of our research:
David M. Blank and Louis Winnick. The Structure of the Housing Market [Journal]- The MIT Press,
2009.
G. Meen Housing Cycles and Efficiency [Journal]- Scottish Journal Of Political Economy, 2000. - Vol.
47, No. 2.
G. Meen M. Andrew, On the Aggregate Housing Market Implications of Labor Market Change
[Journal]-Scottish Journal of Political Economy, 1998. - pp. 393-419.
James M. Poterba Tax Subsidies to Owner-occupied Housing: An Asset-Market Approach [Journal].
The Quarterly Journal of Economics, MIT Press, November, 1984. - Vol. 99(4). - pp. 729-52.
John M. Quigley and Steven Raphae Is Housing Unaffordable? Why Isn't It More Affordable?
[Journal]: The Journal of Economic Perspectives, 2004. - Vol. 18. - pp. 191-214.
DiPasquale Denise Why Don't We Know More About Housing Supply? Chicago: 1997. ECB.
Structral Factors in the EU Housing Market. - Frankfurt: European Central Bank, 2003.
33
Harris Ian Market failure and the London housing market. - London: Greater London Authority
(GLA), 2003.
Herring Richard and Wachter Susan Bubbles in Real Estate Markets // Asset Price Bubbles:
Implications for Monetary, Regulatory, and International Policies. - Chicago : 2002.
Kaiser Ronald W. The Long Cycle in Real Estate. : Journal of Real Estate Research, 1997. - 3 : Vol.
14.
Malpezzi Stephen and Wachter Susan M. The Role of Speculation in Real Estate Cycles // joint
meeting of the American Real Estate and Urban Economics Association and the Asian Real Estate
Society. - Seul : 2002.
Mueller Glen R. What Will the Next Real Estate Cycle Look Like? : Journal of Real Estate Portfolio
Management, 2002. - Vol. 8.
Patrik wilson John Okunev Spectral Analysis of Real Estate and Financial Asset Markets - Sydney :
Journal of Property Invesment and Finance, 1998. - Vol. 19.
Wheaton William C. Real Estate Cycles: Some Fundamentals. - Boston: Journal of Real Estate
Economics, 1999. –Vol 27
William C. Wheaton Real Estate Cycles: Some Fundamentals [Journal]. - Boston : Journal of Real
Estate Economics, 1999. Vol. 27
Main macroeconomic variables:
Our economy model considers output (gross production) as below:
34
Where is aggregate demand and is a constant for production (For the sake a simplicity). The
household income is modeled as below:
/
where is household income per unit time-period. For the sake of simplicity, we assume that
households allocate their income among consumption, repayment of mortgage loans and tax. We
assume that tax rate is and marginal propensity to consume is . Therefore we can calculate
(the payment rate that household can afford), as below:
1
From macro textbooks, we know that aggregate demand can be formulated as below
min ,
Other variables are explained in other sectors. We can see how housing market can simply
relates to the macroeconomic variables through this simple model. In the debt deficit dynamics, we
will extend this model as well.
Debt-deficit Dynamics
First, we look mat the stock of debt. Note that in formulas below, which differentiate
external and internal dynamics. is debt level and goes up through debt raising from internal and
external sources ( ) and also accumulation of interest payments due ( ), but it goes down
with the rate of amortization rate of debt ( ) and also debt repayment rates ( ). Therefore,
we have:
},{ iej
jD
jtac)(
jtai)(
jtam)(
jtpi)(
35
is a function of the level of debt that we want to borrow from international capital sources in order
to finance the deficit ( ). Therefore, shows the level of debt that we want to borrow from
internal capital sources in order to finance the deficit ( ). Note that where denotes
the probability of default. Accumulation of interest payments due ( ), and also debt repayment
rates ( ) are functions of the current interest rate of the market ( ) and the average interest
rate of the market ( ), and of course, level of debt stock. One of the most important mechanisms in
our model is interest rate adjustment mechanism which is captured through following formula:
Where is an indicated rate and is the period through which the adjustment take place.
Through following formula, we can identify indicated interest rate that we need for our model:
This formulas show that the adjusted rate of market interest rate depends on which is risk
premium and is a decreasing function of probability of default. It also depends on the soundness of
fiscal deficit performance which is a function of roll-over risk ( ). Indeed, shows that
ja
jjtpi
jjjtai
dfditac
d
dfdetac
jtpi
jtam
jtai
jtac
jt
iD
iD
Dp
p
Dp
Where
iej
D
)(
)(
)(
)(
)()()()(
)](1[
0)(
)(
:
},{
dfD )(1 dp
dfD 0)( dp dp
jtai)(
jtpi)(
ji
jai
adjjjj tiii /)( *
ji* adjt
0)(
0)(
)()(*
f
d
fdjj
p
p
ppii
)( dp
fp )(),( fd pp
36
interest rates of the market are increasing functions of credit risk and roll-over risk. Therefore,
interest rate increases if probability of defaults increase and the fiscal deficit performance declines.
We also can obtain level of amortization rate through following dynamics:
captures the level of investors’ willingness to invest on bonds based on bonds attractiveness
which is a decreasing function of probability of default and captures the current fiscal
performance and its relationship with the historical payoffs and therefore on amortization rate.
Therefore increase in the probability of default and fiscal accounts mismanagement will increase the
level of risk aversion of investors as captured in the formula. Note that probability of default is the
function of: .
In other words,
Debt-Deficit Dynamics
Changes in government money balance is which is a function of net flows to it. is the tax
revenue and are debt-related and not-debt related expenditures, respectively. Tax revenues
can be divided into inflation tax and normal tax. is the money stock (with respect to price level).
is inflation rate at any given time.
Our main resource for formulas in this part is:
Eduardo Ley (2010):”Fiscal (and External) Sustainability”, Economic Policy and Debt Department,
PREM, the World Bank
0)(
0)(
))()(/()(
f
d
fdd
jjtam
p
p
ppD
)( dp
)( fp
XDandRGNPGGNPD tpideficit //,/,/ )(
)/,/,/,/( )( XDRGNPGGNPDfp tpideficitd
mtG G
tT
nt
dt andee
Q
t
tttnont
isenioraget
nont
Gt
nt
dt
jtac
Gt
mt
QQtttt
eeTG
)1/()( 1
)(
37
Almost all other formulas regarding the debt-deficit dynamics are derived from following resources:
Bartolini and Cottarelli (1994): “Government Ponzi Games and the sustainability of Public Deficits
under Uncertainty,” Ricerche Economiche 48, 1-22.
Blanchard, O. J. (1984), “Current and Anticipated Deficits, Interest Rates and Economic Activity,”
European Economic Review 25, 7-27.
Blanchard, O. J. and S. Fischer (1989), Lectures on Macroeconomics, (MIT Press, Cambridge,
Massachusetts).
Blanchard, O. J. and Weil, P. (1992): “Dynamic Efficiency and Debt Ponzi Games under Uncertainty,”
NBER Working Paper No. 3992.
Fedelino, A., A. Ivanova, and M. Horton (2009): “Computing Cyclically Adjusted Balances and
Automatic Stabilizers,” IMF Technical Notes and Manuals 09/05.
Girouard, N. and C. André (2005), “Measuring Cyclically adjusted Budget Balances for OECD
Countries”, OECD Economics Department Working Papers, No. 434, OECD publishing.
doi:10.1787/787626008442
Tanzi, V. (1977): “Inflation, Lags in Collection, and the Real Value of Tax Revenue,” Staff Papers,
IMF, Vol. 24 (March), pp. 154-67.
Tanzi, V., M. I. Blejer, and M. O. Teijeiro (1987): “Inflation and the Measurement of Fiscal
Deficits,” Staff Papers, IMF, Vol. 34 (December), pp. 711-38.
Eduardo Ley (2010):”Fiscal (and External) Sustainability”, Economic Policy and Debt Department,
PREM, the World Bank
38
Appendix D. Domino-Effect
Our simulation model captures the domino-effect demonstrated in the above diagram.
As shown above, the housing market dynamics, financial market activities and government
and industry responses are all interrelated. These dynamics are all captured in our
dynamic model. Our simulation results demonstrated that an excessive housing inventory
will decrease home prices. As a result, household wealth declines causing households to be
unable to refinance their mortgages. This in turn causes a greater degree of foreclosures.
This process has both a negative impact on the economy and the banking sector. As
C(' /1(%/", D".‐ /"+/ ) /%.")+(4(4@"4<3>*+(' /"' , +.; &; /")+(4(4"
39
mortgage payments in terms of cash flow declines, bank capital levels are depleted, which
further causes a liquidity crunch in the financial market. This process has a negative
impact on the economy because banks are no longer capable of financing businesses,
decreasing the overall level of investment as well as aggregate output. The central bank
will react by easing monetary policy and injecting liquidity into the financial market.
During the recent crisis, the US government first introduced The Fiscal Stimulus Package,
followed by numerous other bailouts (providing funding for troubled institutions). In the
end, the systematic rescue was presented as a final means to stabilize the economy by
recapitalizing the banks on a global level.