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Contrarian investor, Armel is passionate about investment risk management and outside the box investing and thinking. My investment philosophy : 1- Integrate Black swan: possibility of tail/extreme event that can wip out the entire profit. 2-Reach optimum diversification: marginal/incremental... More
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  • PART III: Putting the piece together: Why systemic risk regulation is needed?
    I won’t get into the ideological battle of government regulating the financial market or the debate over the increase of the FED role. But, based on factual evidence in my previous articles, I support the creation of the systemic risk authority provided this authority utilizes a holistic approach to systemic risk analysis. There is a systemic risk out there that needs to be properly monitored as any type of risk. In fact I support the creation of a systemic risk authority that will play a similar role as a risk manager.
     
    As our series of articles shows assessing the systemic risk should be holistic task. It should not be limited to a single factor or methodology: statistical analysis and qualitative assessment should be couple with macro-economic analysis to detect the early signals of increase systemic risk in the economy. As part II of our study showed, many variables identified trend that should have been warning signals for regulatory action. Our reviewed showed clearly the current financial crisis caused by endogenous systemic factors has been anticipated by some experts. But it looks like the signals were not heard or were ignored by the FED and market participants. This tells us the importance of a supra national and national authority that coordinate the systemic risk monitoring. Similar as a risk manager of a company the systemic risk manager will be in charge of tracking and monitoring signals and indicators that increase the systemic risk in the economy.
     
    While panel of economists warned against the crisis in 2005 as demonstrated by the economist letter of June 2005, two eminent researchers of the FRBNY reserve Jonathan McCarthy and Richard W. Peach argued that there was no housing price bubble. Their conclusion was that the U.S. housing market had risen in line with increases in personal income and declines in nominal interest rates. Despite the warning of house-price inflation, most economists thought the housing price would flatten rather than collapse. It is true that, unlike share prices, house prices often come back to fundamentals. Some even disagreed on the housing price inflation, Robert Shiller, a Yale economist, who has just updated his book “Irrational Exuberance” (first published on the eve of the stock market collapse in 2000), disagrees. He estimates that house prices in America rose by an annual average of only 0.4% in real terms between 1890 and 2004. According to Robert Shiller, if the current boom is stripped out of the figures, along with the period after the second world war when the government offered subsidies for returning soldiers, artificially inflating prices, real house prices have been flat or falling most of the time. In this same here 2005, Alan Greenspan former chairman of Federal Reserve, recognized local bubbles, but dismissed the idea of a national housing bubble that could harm the whole economy if it bursts. The diverging view demonstrated the importance of a systemic risk manager that will have the authority to supervise systemic risk. He should have the power to supervise the systemic risk and reconciliate and gauge different economist’s, statisticians, traders, bankers and market participants’ views and interpret indicators to make the proper recommendations. Much like the FDIC, the systemic risk manager should also have the ability to mitigate the systemic risk in the system by warning market participants or breaking down factors, banks; financial institutions  that threaten the entire system.
     
    Our final thought is that a proper assessment of systemic risk assessment needs to be holistic
    And rely these non exhaustive variables:
     
    Macro-economic Level:
    o   Monitoring of macro-economic fundamentals
    o   Monitoring of regulatory and policy change impacts
     
    Micro-economic level:
    o   Monitored individual market participants behaviors
    o   Monitor market innovations and competitions
    o   Monitored widely used models or product that may  pose systemic risk
    o   Understanding risk concentration, transfer, diversification, exotic products,
    o   Understanding of break-down of statistics and finance assumptions and consequences
    o   Analysis of extreme value dependencies
    o   Stress-Testing at the industry level or aggregate level
     
    Extraneous factors:
    o   Prepare the financial system for unexpected factors : natural catastrophe, terrorism attack and others
     
     
    This was my contribution on the need for systemic risk regulation
    Tags: FED, FDIC, Alan Greespan, Robert Shiller
    Sep 19 08:59 pm | Link | Comment!
  • PART II: Could the subprime crisis been averted? Yes, provided there was a systemic risk manager.
     
    MACRO-ECONOMIC APPROACH TO SYSTEMIC RISK
    Systemic risk with regard to the financial sector can be defined as the potential for an economic shock to induce substantial volatility in asset prices, significant reductions in corporate liquidity, potential bankruptcies and efficiency losses. A key feature in the propagation of such a systemic shock is acute uncertainty regarding institution’s ability to satisfy its immediate payment obligations and simultaneously inability of counterparties to hedge such risk. Macroeconomic shock effects are visible first through macroeconomic variables before its propagation to the financial system. Macroeconomic developments and the financial sectors are linked; some macroeconomic variables can serve as indicators of signal of increasing systemic risk for the banking system. In this part of the study and based on previous study conducted mainly by AV Felice Marlor on Macroeconomic indicators of systemic risk published in 1997, the economic literature and my own observations assessment. Change in the economy alone is not sufficient to generate systemic risk. Financial institutions have enough information about the business cycle to take changes in the economic activity into consideration during their activities. For systemic risk to arise several financial institutions must have made loans with correlated risk at the same time, the economic environment must be disturbed such that loss occur. From previous part I of this series of articles we pointed out that individual risk can be managed by risk management techniques provided that the overall economic environment stays stable. Increasing systemic risk (market risk) can increase the total sum of risk. The chain can break at the weakest part and result in massive losses in one sector and spread to another sector via counterparty risk or via pure lack of confidence.
     
     
    Empirical observations of macro-economic indicators that signaled the systemic risk arisen
     
    The root cause of the current financial crisis is: Unsustainable price level fueled by low interest rate, herd behavior, historically low interest rates encouraged home buyers to borrow more money; and customer flying from stock markets to housing, regarded as safest and more attractive asset after the plunge of the stock market, moral hazard, credit risk spill over in the general economy, etc. Overspending, over consumption in certain area: real estate. These are symptoms that could have been observed in macro-economic indicators.
     
    Observation 1: Increase delinquency and charge-offs rates in the economy
    The change in mortgage quality could have been seen in the delinquency performance. Having being predicted for several years credit default based on delinquency trend, I have hands on experience on this fact. Since nearly a decade, delinquency rates have steadily gone up. According to the Mortgage Bankers Association, at 6.35 %, the delinquency rate, i.e., the share of mortgages with payments outstanding 60 days or more, in the first quarter of 2008 was the highest (on a seasonally adjusted basis) since they began collecting the data in 1979. At 2.47 %, the foreclosure rate, i.e., the share of outstanding mortgages in foreclosure proceedings has more than doubled since the end of 2006. As a function of the number of months since the conclusion of the mortgage contract, delinquency rates on mortgages issued in 2006 have been rising more steeply and have been higher than delinquency rates in any previous year in this decade; delinquency rates on mortgages issued in 2007 are even worse. The delinquency rates in the mortgage industry spill over in other parts of the economy. Consumer credit delinquency rates increased as well as credit card delinquency rates.
      
     
    Observation 2: FED fund rates
    Research has shown how moral hazard and coordination problems arising from uncertainty or informational asymmetries can lead to banks runs and sub-optimal resource allocations. See Diamond and Dybvig (1983), Caballer and Krishnamurthy (2006). The subprime crisis, high risky lending in real estate area, credit risk spread out to the all economy through securitization and the Federal reserve may have precipitating the crisis with its aggressive monetary policy from late since 1990, drastically lowering interest rates. This contributed to create moral hazard in the financial market. This is certainly not new. But isn’t this tells us that a systemic risk manager should be independent from the Federal Reserve as the FED itself can fuel systemic risk?
     
    Observation 3: Housing Valuation versus GDP
    First of all the housing valuation is highly correlated with the GDP and they both followed an exponential growth: started around in 1965 for GDP and a year later in 1966 for Housing Valuation. GDP and Housing valuation very closely correlated due to the fact that rising home values have made Americans feel wealthy. And tapping into the Home Equity has been the source of purchasing power. Fuelling consumption and GDP growth. Since 1980 the Housing Valuation has exceeded GDP. Since 2000, it also appears that The Housing Bubble has not been pushing up the GDP as much, and the Gap between the two has been rising. The correlation was consistently above 0.97. Other analysis suggests or inferred that a decrease of housing will have a significant impact on the GDP.
     
    Observation 4: Home Equity
    Home equity is the current market value of a home minus the remaining mortgage balance. It is essentially the amount of the home that you own outright. Home Equity has fallen from 84% in 1945 to 53% in 2005.
     
    Observation 5: Overvaluation of residential property
    In 2005 that the total value of residential property in developed
    Economies rose by more than $30 trillion over the past five years, to over $70 trillion, an increase equivalent to 100% of those countries' combined GDPs. Not only does this dwarf any previous house-price boom, it is larger than the global stock market bubble in the late 1990s (an increase over five years of 80% of GDP) or America's stock market bubble in the late 1920s (55% of GDP). The panel of economist predicted that in their own word in June 2005 “….it looks like the biggest bubble in history”.
     
    Observation 6: Housing Price Index
    US saw one of the biggest increases in house-price inflation over in 205, with the average price of homes jumping by 12.5%.
     
    Observation 7: Ratio of housing price to rent
    This is an indicator of the overvaluation of the housing value. John Krainer and Chishen Wei economist at FED, popularized the use of the ratio of price to rent. As an asset has a fundamental value equals the sum of its discounted future payoffs. The fundamental value of a house is the present value of the future housing service flows that it provides to the marginal buyer. A house yields a dividend in the form of the roof over the head of the occupant. In a well-functioning market, the value of the housing service flow should be approximated by the rental value of the house. Just as the price of a share should equal the discounted present value of future dividends, so the price of a house should reflect the future benefits of ownership, either as rental income for an investor or the rent saved by an owner-occupier. Hence the ratio of prices to rents can be used for fundamental analyze similar to the price/earnings ratio for the housing market. When the ratio diverges too much from its fair values (rents) there is a concern of bubble. Looking at the price to rent ratio some critical observations:
     
    • USA’s ratio of prices to rents is 35% above its average level during 1975-2000
    • UK’s property was “overvalued” by 50%. Rental yields have fallen to well below current
    mortgage rates, making it impossible for many landlords to make money. More precisely,
    house prices grown faster than implied rental values for long time.
     
    Observation 8: Speculative behavior and mispricing
    It was well known in 2005, and based on Fed study that prices were driven by speculative demand. According to the National Association of Realtors (NAR) 23% of all American houses
    bought in 2004 were for investment, not owner-occupation. Another 13% were bought as second homes. Investors are prepared to buy houses they will rent out at discount in hope to make large gain by selling few times later. The economist June 2005 letter warned about all of these. Speculative loans or Interest-only mortgages, along with so-called “negative amortization loans” (the buyer pays less than the interest due and the unpaid principal and interest is added on to the loan) were popular. After an initial period, payments surge as principal repayment kicks in. The new loans are essentially a gamble that prices will continue to rise rapidly, allowing the borrower to sell the home at a profit or refinance before any principal has to be repaid. Such loans are usually adjustable-rate mortgages (ARMs), which leave the borrower additionally exposed to interest rates risk. In 2004, ARMs raised to 50% of all mortgages.
     
    Observation 9: Price-to-Income
    In the summer of 2005, when easy-money mortgages were readily available and helping to drive up home prices, the national median sales price of a home was almost eight times as much as the average per capita after-tax income of Americans.
     
    Observation 10: Bank competition for margin lead to riskier mortgage
    Many economists and expert saw the new riskier loans as potential factor of systemic risk. Loans with no down payment increased the risk of bubble. According to the National Association of Realtors (NAR), 42% of all first-time buyers and 25% of all buyers made no down-payment on their home purchase in 2004. Indeed, homebuyers can get 105% loans to cover buying costs. At the same time new type of loans NINJA “No Income, No Job” were booming. Banks requested increasingly no or little documentation of a borrower's assets, employment and income.
     
    Observation 10: National average housing price
    In 200/2005 the national average house Prices fallen for a full year since modern statistics began. Based on experience in Netherlands even a mere leveling-off of house prices can trigger a sharp slowdown in consumer spending triggering a recession.
     
    Observation 11: Housing-equity withdrawal
    When house prices had been rising, borrowing against capital gains on homes to finance other
    spending had surged. Although house prices did not fall, this housing-equity withdrawal plunged
    after 2001, removing a powerful stimulus to spending.  Sensitivity analysis conducted by economists demonstrated that even a modest weakening of house prices in US would substantially hurt consumer spending, because homeowners have been cashing out their capital gains at a record pace. Goldman Sachs estimates that total housing-equity withdrawal rose to 7.4% of personal disposable income in 2004. If prices stop rising, this “income” from capital gains will vanish.
     
     
    YES THE CURRENT CREDIT CRUNCH CRISIS WAS PREDICTED?
    My intention was to clearly demonstrate that endogenous systemic risk (arisen from inside the financial system) as describe in part I of my series on systemic risk can be sometimes anticipated.  As showed by the different signals above, the systemic risk increase in the financial system was identified and in June 2005, several economists wrote a letter in the economist were they made a definitive prediction of the current crisis, based? The article I refer to is: “The global housing boom, In come the waves Jun 16th 2005, From The Economist print edition: The worldwide rise in house prices is the biggest bubble in history”. But the market participants did not really pay attention to the warning. This fact demonstrates the need of a systemic manager or regulatory authority whose role would be to track all those signals. The June 2005 letter warned against against an unprecedented recession coming soon.
     
     
    To be continued: Part III and Final: The need of systemic risk manager?
    Tags: Federal Reserve, Housing, Real Estate
    Sep 19 08:12 pm | Link | Comment!
  • PART I: SYSTEMIC RISK? LET’S UNDERSTAND THE FOUNDATION TO GAUGE THE NEED OF SYSTEMIC RISK REGULATION.
    This is part I of my series on systemic risk. Hold on to Part 3 for my final take on systemic risk regulation. The series analyzes the systemic risk; try to understand its current assessment through statistical models and dynamic with regard to history and the current financial crisis. List and propose methods to assess and anticipated the increase of the systemic risk. Another objective in this series of articles is to demonstrate that there are tools that can help assess, anticipate or signal some of the systemic risk and anticipate its consequence through regulation, provided there is an authority to analyze this risk. The systemic risk has become at the center of the current financial crisis, after the collapse of Lehman brothers and the near collapse of AIG, no financial industry sector is immune from the systemic risk. Regulation of the financial system is a critical issue. The question today is it possible to create a regulatory mechanism that is successful in reducing systemic risk, but not too costly?
     
    DEFINITION OF SYSTEMIC RISK
    I believe it is important to set first the foundation:
     
    "Systemic risk" refers to the likelihood and degree of negative consequences to the larger body. With respect to federal financial regulation, the systemic risk of a financial institution is the likelihood and the degree that the institution's activities will negatively affect the larger economy such that unusual and extreme federal intervention would be required to ameliorate the effects. Property Casualty Insurers Association Of America
     
    Systemic risk arises when a disturbance occurs which can lead to credit losses and result in the failure of a group of financial firms. Such disturbances can threaten the functioning of the financial markets as a whole. ….In order for systemic risk to arise, many financial institutions must have made loans the risks of which are correlated and at the same time, the economic environment must be such that losses actually occur. AV Felice Marlor, Payment Systems Department, Sverige Riksbank.
     
    Systemic risk is modeled as the endogenously chosen correlation of returns on assets held by banks. The limited liability of banks and the presence of a negative externality of one bank's failure on the health of other banks give rise to a "systemic risk-shifting" incentive where all banks undertake correlated investments, thereby increasing aggregate risk. …….A financial crisis is “systemic” in nature if many banks fail together, or if one bank’s failure propagates as a contagion causing the failure of many banks. A Theory of Systemic Risk and Design of Prudential Bank Regulation, Viral V. Acharya, London Business School, NYU-Stern and CEPR.
     
    CLASSIFICATION OF SYSTEMIC RISKS.
    I have not found clear classification of systemic risk, but it seems intuitive for me to classify the systemic risk as following:
     
    Intern to the financial system (endogenous)
    (1)    Too Big to Fail: The institution may be too big and has enormous exposure such that its failure may negatively affect the larger economy such that unusual and extreme federal intervention would be required. This characterization focus on the size of operations and less on the number of interconnection of the institutions.
     
    (2) Too Interconnected to Fail: risk that the interconnection of a financial institution which default negatively impact to the larger economy. The institution may not be big but the leverage and multiplier effect can diffuse the effect (domino effect) to the larger economy.
     
    (3) Endogenous cycle with euphoric and panic behavior in the financial sector
     
    External to the financial system (extraneous)
    (4) Macro economical and political risk. A macroeconomic shock, induced by domestic imbalances or an external impulse. Disturbance in the economy or the market which can lead to credit losses and result in the failure of a group of financial firms. For instance unexpected changes in the economic policy or regulation can alter economic fundamentals and thereby substantially change the outcomes of financial institutions’ lending decisions. Regulatory changes have contributes to banking crises in many countries.
     
    (5) Natural or human made catastrophes that can disturb the entire financial system. Example of Katrina or September 11.
     
    SYSTEMIC RISK ASSESSMENT THROUGH STATISTICAL MODELS
    There are several flaws in current risk conceptualization. Before we review the limits of today systemic risk assessment, let’s recognize the important role statistical risk conceptualization or the lack of risk modeling has played in past crisis. The growing importance of risk management is seen in regulatory design where risk regulations are more and more model based. Risk modeling brought tremendous progress in the understanding and the quality of risk assessment. Risk management helped understand the dynamic of previous crisis and the complexity of banks exposures and off/on-balance sheet derivatives trades. It was the misunderstandings of quantitative risk and bank exposures that have contributed to amplify the crises in the nineties and eighties. For instances:
     
    • In many countries banking crises in the late eighties, early nineties and today had resulted from a failure to properly recognize risks and correlations of risks in real-estate, small-business lending and among financial institutions. In many case it is the proper use of risk modeling and the ability to think outside the general framework that were at question
     
    • The absence of risk modeling resulted in a failure to understand the maturity mismatch between assets and liabilities and its implications played an important role in banks defaults in early eighties.
     
    • The development of Value at Risk (VaR) models significantly enhanced banks ability to measure and hedge their trading book risks. An important consequence of VaR is that it has been used by regulators to set rules on prudential capital and avoid the distortionary impact of this capital.
     
    But as someone said the devil is in the details, risk models work well as far as the condition are normal, stationary, the models reliability are questionable or the models collapse in specifics and extreme cases. Statisticians know very well the realities do not often exhibit the Gaussian, stationary and independence properties that the statistical theory assumes. And as regulatory environment (Basel II, Solvency II) are based on imperfect models, it is not surprising that those limitations in risk modeling technology, coupled with imperfect regulatory design, may increase rather than prevent systemic failure. To me this, intrinsic human/model/economist imperfection makes the need of systemic risk surveillance important.
     
    Assumption of independent Stochastic Process
    To understand the limit of current risk approach to measure systemic risk it is interesting to realize some flaws in their initial design. An explicit assumption of many risk models is that the market data or the considered variables follow a stochastic process. Statistical models and efficient market theory usually made the assumptions that past observations predict the futures or all information is embedded in the in the market price. We know market participants’ behavior is not always rational and can be driven by many behavioral and outside considerations that the statistical models don’t incorporate. It is therefore more difficult to incorporate a systemic variable in the modeling. An independent stochastic process assumption break down in time of crisis, when systemic risk arisen. At time of crisis market participants could behave in a similar fashion leadings to collapse of all models. This correlated behavior or systemic risk is not properly modeled or it is omitted in number of risk models. Also unfortunately, regulatory action rather than limiting the risk can be source of systemic risk increase. When identical model based risk capital, constraints are imposed; regulatory demands may perversely lead to the amplification of the crisis by reducing liquidity.
     
    Judgment and subjectivity of modeler
    Risk assessment is not only based on statistical or economical models but also on the modeler ability to represent the real world and its judgment. This imperfection may be source of systemic risk, if models are used blindly. This observation should lead any risk manager, financial institution regulators to be humble in its modeling ability and put internal and external safeguard to monitor discrepancy. Similarly the regulator should put in place mechanism to observe unusual behavior in the markets. For instance a price that is removed from the fair value or real value of the asset.
     
    Correlation as measure of systemic risk
    Jon Danielson, of the Financial Markets Group, London School of Economics, www.RiskResearch.org, wrote an excellent paper on the subject that deserve to be read. Some of ideas I shared: correlation in times of stability does not provide the correct measure of assets and financial institutions relationships risk in times of crisis. Correlations models rely on normal distribution assumption and oversee the left tail or extreme risk. Empirical studies and literature review show that correlations and other risk metrics are not robust in time of crisis and are too volatile. And even the wrong comfort these risk metrics gave lead to misinformation and even risk metrics can participate in increasing the systemic risk. When many of the market participants execute the same trading strategies during crisis, they change the distributional properties of risk. As a result, the distribution of risk is different during crisis than in other periods, and standard risk model become not only useless but may amplify the crisis, by leading to large price swings and lack of liquidity. This was the case in the 1987 crash, the Russia crisis of 1998 and the subprime crisis of 2007. The elaboration of a systemic risk regulations could not relied uniquely on those metrics. In particular, correlations are subject to change over time. Even with the best of data, correlations are therefore hard to model. The recent tentative to model correlation such as the copula function showed worrying limitations that participated in the collapse of the CDs market: the mortgages showed non predicted common dependence unanticipated by the most sophisticated risk models. This common factor or systemic factor has been overlooked for too long because of its complexity of modeling.
     
    "Risk comes from not knowing what you're doing". Warren Buffett
     
    Understand the copula function to understand one of the factors of the CDS collapse: What is copulas function? Copula function played a significant part important part in the acceleration of the credit default swap collapse. As statistician, I love copula because of its elegance and ability to aggregate and simplify the multidimensional correlation concept, I recognize it difficult to model under extreme scenario. But the most important part of a model is not its ”elegance” or “beauty” but assumptions underlying this model that many statisticians and modelers know very well, some are unrealistic. The Gaussian copula is a statistical function introduced by David X. Li in 1998, a New York banker that was initially praised to simplify the complex concept of correlation to a simple number. With copulas no more need to pay close attention to the underlying assets and simulate myriads of scenarios and covariance matrix to capture the correlation: It was a gift as the “le Chaînon manquant or missing link” for statistician, traders, rating agencies, banks and regulators trying to price CDS tranches. As a single number was now almost enough to derived the CDS price, the model was immediately used by practitioners with less considerations for its underlying flaws as the CDS market grow exponentially and there were so many money to be made. Unfortunately copula function fuels the CDS market, contributing to give false sentiment of fair pricing and understanding of risk. Millions of CDS tranches deemed rated AAA, based on their default correlation derived from copulas were created. Without being modelers there can be certain uncomfort to think that a single number so remote from the underlying assets could tell the full story of the default probability of a CDS tranche. The copulas also implicitly made the assumption that CDS market price the default risk appropriately. But as expected when the market freeze and the copulas functions become irrelevant as the CDS tranches correlation exploded, which I called correlation break-down. Li himself certainly clearly understand the limits of the models, but we all buy in the euphoria of increasing home price,   risk diversification and profit underlying the growth of the CDS market.
     
    Some comments of renowned experts:
    • Paul Wilmott: “the correlations between financial quantities are notoriously unstable.” The copula model may have give false confidence of little risk, and push to overlook the tail risk or extreme event.
     
    • Nassim Nicholas Taled (Black Swan is one of my favorite book) added "Anything that relies on correlation is charlatanism."  
     
    • And Goodhart (1974) “Any statistical relationship will break down when used for policy purpose “
     
    Is Value at Risk an endogenous systemic risk factor?
    Value–at–Risk (VaR) is a fundamental component of the current regulatory environment, and financial institutions in most countries are reporting VaR to their supervisory authorities. The simplicity of calculation is the strongest advantage of the VaR. Under some assumptions VaR, provides an adequate representation of risk; however again the Gaussian and independence assumptions underlying VaR are not realistic and may result in misrepresentation of risk especially at time of crisis. There are couple of evident flaws in the VaR, I won’t develop in this article, note that  VaR is not a coherent risk measure as it lacks the subadditivity property and the reliance of VaR on a single quantile of the profit and loss distribution implies it is easy to manipulate reported VaR with clever trading strategies. Also, VaR overlook tail risk or extreme event.
     
    SOME STATISTICAL ASSESSMENT OF SYSTEMIC RISK
     
    • Extreme Value dependence to measure systemic risk
    Most current risk models did not performed well during crisis, however new models grounded in the extreme value, microeconomic and macroeconomic theories may be useful to explore. Extreme Value Theory (EVT) or the analysis of variables behavior under extreme or stress scenario, works provided we have the probability distribution of the variables under consideration. Extreme value dependence focuses on the relation of variables in the tails of their distributions. Existence of extreme realization of variables together or not irrespective of how strongly they might be correlated in their more normal range. Extreme value dependence behaves differently from the basic notion of dependence often refers as correlation
     
    • Stress-testing
    To make up for the lack of knowledge of extreme losses and insufficient of data, it can be useful to artificially generate extreme scenarios of the main factors driving returns and then assess the resulting outcome. Stress tests are more accurate when it is possible to calculate the probability of the extreme scenarios, which often is not the case.  Geithner conducted stress-test on the biggest banks to evaluate the systemic risk the system may face.
     
    • Leverage in the system
    Because of leverage, financial institution positions are often considerably larger than the collateral against those positions. Leverage has the effect of a magnifying glass, expanding small profit opportunities into larger ones but also expanding small losses into larger losses. And when adverse changes in market prices reduce the market value of collateral, credit is withdrawn quickly, and the subsequent forced liquidation of large positions over short periods of time can lead to widespread financial panic, as in the aftermath of the default of Russian government debt in August 1998 and Lehman Brothers collapse in September 2008.
     
    • Liquidity exposure
    Generalize illiquidity play an important part in general market collapse. The more illiquid the portfolio, the larger the price impact of a forced liquidation or fire sale, which erodes the bank’s risk capital that much more quickly. Now if many hedge funds or financial institutions face the same “death spiral” at a given point in time—that is, if they become more highly correlated during times of distress and as financial institutions are interrelated for instance obligors of other financial institutions the illiquidity crisis can cascade quickly into a global financial crisis. This is systemic risk. Along with the many benefits of a truly global financial system is the cost that a financial crisis in one country can have dramatic repercussions in several others—that is, contagion. The subprime mortgage and CDS market collapse have spilled over. A method for assessing the illiquidity risk exposure of a given financial institution can be examined through the autocorrelation coefficients of the institution returns.  Samuelson’s (1965) in his paper—“Proof that Properly Anticipated Prices Fluctuate Randomly”—provides a succinct summary for the motivation of the martingale property: In an informationally efficient market, price changes must be unforecastable if they are properly anticipated, that is, if they fully incorporate the expectations and information of all market participants. Measure of aggregate serial correlation can be used as a proxy of illiquidity in the context of systemic risk.
     
    • Performance smoothing
    A more prosaic channel by which serial correlation may arise in the reported returns of hedge funds or financial institution is through “performance smoothing,” the unsavory practice of reporting only part of the gains in months when a fund has positive returns so as to partially offset potential future losses and thereby reduce volatility and improve risk adjusted performance measures such as the Sharpe ratio. For funds containing liquid securities that can be easily marked to market, performance smoothing is more difficult and, as a result, less of a concern. Indeed, it is only for portfolios of illiquid securities that managers and brokers have any discretion in marking their positions. Such practices are generally prohibited by various securities laws and accounting principles and great care must be exercised in interpreting smoothed returns as deliberate attempts to manipulate performance statistics. Managers do have certain degrees of freedom in valuing illiquid securities
     
    • Liquidation of a financial institution
    Since the collapse of LTCM in 1998 and Lehman Brothers in September 2008, it has become clear that hedge fund liquidations can be a significant source of systemic risk. By analyzing factors driving financial institutions liquidations, regulators may develop a better understanding of the likely triggers of systemic risk in the financial system.
     
    • Regime switching model
    Another measure of systemic risk is motivated by the phase locking example of Lo (1999), where the return-generating process exhibits apparent changes in expected returns and volatility that are discrete and sudden—for example, the Mexican peso crisis of 1994–95, the Asian crisis of 1997, and the global flight to quality precipitated by the default of Russian GKO debt in August 1998. Linear models are generally incapable of capturing such discrete shifts; hence, more sophisticated methods are required. A regime-switching process in which two states of the world are hypothesized and the data are allowed to determine the parameters of these states and the likelihood of transitioning from one to the other. Regime-switching models have been used in a number of contexts, ranging from Hamilton’s (1989) model of the business cycle to Ang and Bekaert’s (2004) regime-switching asset allocation model, and NICHOLAS CHAN and al proposed to apply it to the CSFB/Tremont indexes to obtain another measure of systemic risk, that is, the possibility of switching from a normal to a distressed regime.
     
    • Expected shortfall
    From a regulator’s perspective it may not be relevant just to look at VaR and other metrics, but also at expected shortfall, which is the present value of the amount of debt that cannot be covered by the assets of the bank in case of default. In the simple Merton (1977) framework, this is given by the value of a put option. Because of this variation, the regulator might not only be concerned about the level of the expected shortfall but also about its dynamics. In an economy with uncorrelated bank portfolios a shock to the assets of one bank will increase the regulator’s liability towards this bank directly but it will not affect the values of the guarantees the regulator has given to other banks’ depositors. With highly correlated asset portfolios a shock will again hit the regulator directly but will also adversely affect the liabilities towards other banks. It is thus important to look at the liability of the deposit insurance agency and at the potential future shortfall in a banking system from a portfolio perspective and not just at the level of individual banks. In a low correlation banking system, in which the shocks to the bank asset portfolios are mainly idiosyncratic, the volatility in the regulator’s portfolio of guarantees should be low whereas high systemic risk will imply high volatility.
     
    The take away of this part s the complexity of the systemic risk concepts that goes beyond modelers and market participant’s expertise. By definition as the individual institution behave to maximize individual profit, someone one needs to be in between to identify friction and systemic risk arisen.
     
     
    "Risk comes from not knowing what you're doing". Warren Buffett
     
     
    TO BE CONTINUED………………………PART II: How the current systemic crisis could have been averted should we had a systemic risk manager (I do prefer systemic risk manager versus regulatory authority)?
     
    References:
     
    1-       Many of the ideas developed in this part result by empirical study conducted by Jon Danielson, of the Financial Markets Group, London School of Economics, RiskResearch.org
    2-       Based on article Recipe for Disaster: The Formula That Killed Wall Street By Felix Salmon,
    3-       Why VAR Fails: Long Memory and Extreme Events in Financial Markets, Cornelis A. Los Kent State University, Department of Finance, BSA 430, Kent, OH 44242-0001. Email: los500@cs.com
    4-       For complete analysis of subadditivity of VaR see Coherence of VaR as risk measure: PRM Handbook–Volume III
    5-        The Emperor has no Clothes: Limits to Risk Modeling, Jon Danielson, Financial Markets Group, London School of Economics, www.RiskResearch.org.
    6-       NICHOLAS CHAN, MILA GETMANSKY, SHANE M. HAAS, AND
    ANDREW W. LO “hedge role in increasing systemic risk”
     
    Tags: AIG, LEH
    Sep 19 05:16 pm | Link | Comment!
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