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 ,

30  

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.