Harry Markowitz's Broken Toy: A Journey Through Modern Portfolio Theory (Part I)

One of the most important contributes that academia brought to Wall Street is surely Harry Markowitz's modern portfolio theory.

The theory is pretty simple: create a basket of (presumably) uncorrelated securities and you can maximize return and minimize risk. The two risks the model tries to reduce are the unsystematic one along with the company's. The magic of the model lies in the diversification into stocks (or other asset classes) with different volatility and that are little or negatively correlated. Markowitz's model has a series of assumptions:

• Markets must be somewhat efficient or able to absorb new information quickly and completely;
• Investors have free access to new and correct information;
• Investors try to maximize return and minimize risk;
• Investors make their investment decisions according to the excepted return of the security and its volatility (as measured by the standard deviation);
• Investors are supposed to act rationally in order to maximize their utility given their level of income;

At time t for an investment X, we define the moments of a normal distribution as follows:

Moments of a normally distributed random variable.*

Let's assume, for the sack of simplicity, that an investor wants to invest in stocks (through the S&P 500) and government bonds (the model is also applicable to 2+ asset classes). We can create several portfolios assigning different weights to stocks and bonds (i.e. long 90% equities and 10% bonds or alternatively short -50% equity and long 150% bonds) and then use the mean-variance analysis to compute expected return and volatility: 

The model, however, has several limitations:

• Volatility, as well as correlation, is supposed to stay constant;
• Returns are assumed not to violate the assumptions of normality;
• Securities can be bought and sold in the market without encountering any liquidity squeeze;
• Investor are supposed to act rationally;
• Investors are supposed to have the same investment horizon;
• There are no transaction costs;

Several (if not each) of the model's assumptions have been violated during recent years. Correlations among asset classes broke in 2008 and tended to one as it usually happens in highly volatile markets (Robert Frey has a post on this). The normality behavior of stocks and bonds has been questioned as well as the rationality of investors. Whoever used Markowitz's model (banks, retail and institutional investors) incurred large losses as markets turned bearish. 

Correlation changes over time and with dramatic effects on the portfolio frontier. In the graph below, I plotted three portfolio frontiers according to three different correlations: the historical correlation between stocks and bonds from 1926 to 2012  (approximately 1.4%) and two hypothetical new correlations: +0.6 and -0.8. 

Any portfolio along the three frontiers is optimal considering the trade-off between risk and return, given the weights assigned to stocks and bonds. The green arrows indicate how risk and return move along the frontiers as correlation changes (I am assuming the investor is merely interested in having a portfolio with minimum risk and maximum return).

Stocks might violate the normality assumptions. I calculated daily returns for IBM from 1962 to 2012 and the frequency distribution is presented below. You may be thinking "Hey! It really looks like a bell curve. IBM returns can be normally distributed" and you would be wrong! Excess kurtosis (a measure of the "peakedness" of the probability distribution of a real-valued random variable) is over 10. Not a good sign. 

IBM Daily Returns 1962 - 2012

Excess Kurtosis: 10.42

In terms of implementation this is a big problem as numerous asset sub-classes like options and interest rate derivatives are nonlinear. The Gaussian nature of the fluctuations of the underlying assets and the non-linear dependence of the price of the derivatives are major obstacles for the implementation in Markowitz's model.

The moments of the normal distribution change: Correlation changes over time, as well as volatility, thus the moments of the normal distribution change too. As moments change over time, the shape of the distribution changes giving different estimates about the future depending on the historical sample used, with considerable impact on the location and shape of the portfolio frontier.

I created two portfolio of stocks for two different time intervals: 1926-1966 and 1967-2012. The frequency distributions of returns are presented below:

LFT Portfolio (1926-1966) - Mean Return: 12.4%, SD 23.5%, Skewness -0.2, Kurtosis -0.38
RT Portfolio (1967-2012) - Mean Return: 10.8%, SD 17.6%, Skewness -0.7 Kurtosis 0.12

Tails are also affected: a 3-standard deviation move from the mean has probability of 5% in the 1926-1966 portfolio and 0.5% in the 1967-2012 one.

Markowitz's model gives a false sense of risk control to investors. The optimization process and the normal distributions indirectly "promote" the underestimation of risk with non-optimal portfolio allocation. However, (partial) solutions for these problems have been found. More on that in the following weeks. Stay tuned!

*In reality the variance is one of the moments of a normal distribution, not the standard deviation. However, I defined the latter for reasons the reader will understand when reading the post.

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Forget the Winklevoss Twins' ETF! Investors Still Lose Money Trading Leveraged ETFs.

Exchange Traded Funds (ETFs) have been under scrutiny for quite a while now for their performances during periods of high market volatility and deviations from the underlying assets value. Leveraged ETFs are no exception and probably among the most misunderstood financial products. Leveraged ETFs were first launched in 2006 and since then the market for this kind of financial products has grown reaching a total of $50.9 billion assets, with more than 275 leveraged ETFs trading on exchanges in 2013. The Financial Industry Regulatory Authority (FINRA) has warned investors about the potential pitfalls of leveraged ETFs and in some cases it has punished banks. On the other hand, investment banks like UBS and brokerage firms like Ameriprise stopped offering leveraged ETFs to their clients.

Leveraged ETFs' goal is to delivery twice or three times the daily return on a specific underlying index. In order to do so, they have to re-balance their portfolios on a daily basis and keep the proper amount of leverage in order to make sure they deliver exactly twice or three times the daily return on the index. As a consequence, leveraged ETFs are exposed to price erosion: an investor would have to borrow more funds to buy more ETFs as the price of the index goes up. Conversely, the investor would need to buy less ETFs as the price goes down.

Let’s see this with an example. For the sake of simplicity, I assume that the price on the underlying index goes up at Tn and down at Tn+1 by 10%. An investor starts with $100 at day n and wants to invest in a 2X Leveraged ETF. It would have to borrow $100 to invest $200. Assume the market goes up by 10%. The investor makes $220, repays the $100 it borrowed and retains $120. At day Tn+1, the investor would have to borrow $120 to invest $240 in the leveraged ETF. Assume now that prices go down by 10%. The investor loses $24 but still makes $216. It repays the $120 it borrowed at day n and retains only $96. Again at day Tn+2, the investor would have to borrow $96 to invest $192 in the ETF. Prices go up by 10% and the investor makes $211.2. It repays the $96 it borrowed at day Tn+1 retaining $115.2. And so on and so forth.  

The effects of a relatively “long-term” investment in a leveraged ETF look like this:

Natural decay for a 2X and 3X Leveraged ETF - Volatility window -10%/+10%

As we increase the volatility window from ±10% to ± 20% (that is, prices go up and down by 20%), the exponential decay is even more magnified:

Natural decay for a 2X and 3X Leveraged ETF - Volatility window -20%/+20%

It seems clear that the daily compounding is amplified as markets experience periods of high volatility. The higher the volatility, the more adversely affected is the leveraged ETF. In addition, expenses are high as leveraged ETFs require active portfolio management. The most popular leveraged ETF (ProShares Ultra S&P 500) has expenses of approximately 0.9% while “vanilla” ETFs without leverage cost 0.1% or less. Price erosion is even more accentuated in the 3X leveraged ETF because of the expenses, which are even higher than in the 2X as portfolio management is more challenging.

Leveraged ETFs should be seen as extremely short-term bets on specific market moves or as a way to hedge some specific market exposure: therefore as mere investment vehicles. On the long-term, leveraged ETFs do not replicate the underlying index. Big deviations from it are expected.

The lesson here is a lesson on leverage. Passive investors should avoid building long-term strategies using leveraged ETFs. They are more suited for institutional investors who are primarily engaged on active trading. 

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Did Harvard Never Consider Swaptions? On Larry Summers' Deal That Cost Harvard $1 Billion.

It's the debate of the week! Janet Yellen or Larry Summers as next Fed Chairman?

Yellen over Summers?

Summers over Yellen?

Yellen or Summers?

Sometimes fruitful, sometimes full of hatred, sometimes rhetoric, sometimes thoughtful, the debate has seen people presenting strong cases to prefer one candidate over the other. Does the debate need to hear my opinion? I don't think so. 

As a financial engineer, one specific issue captured my attention as it was brought up by Matthew Klein a few weeks ago: Harvard's long-term swap contracts the school entered in the early 2000s to "hedge" against a rise in interest rates between 2004 and 2008. This post will not discuss responsibilities or take part in the blaming game. Instead, I would like to focus only on the trade itself.

Although we don’t know all the specific details of the deal, we can read Matthew Klein (here), the Epicurean Dealmaker (here) and Brad Delong (here) and have a fair picture of it. 

In the early 2000s, Harvard University was planning to:

• borrow some money in 2008 for some project/campus expansion;
• Harvard was concerned about a rise in interest rates between 2004 and 2008;
• Harvard wanted to hedge the risk of a rise in interest rate between 2004 and 2008;

Therefore, Harvard's Debt Management Committee entered long-term swap contracts in 2004. A decision that later proved to be catastrophic for the school as it lost almost one billion dollars. The swaps were “naked” not “covered”, that is, at that time, Harvard hadn't borrowed the money yet (it was planning to do so in 2008). Without an equivalent and opposite trade, it's hard to see how the swaps could be considered as a hedge against a rise in interest rates. Also, it's hard to see how these contracts could turn automatically into a bet as Harvard cancelled the project in 2007...

Harvard had a better alternative: using swaptions. A swaption (see the picture below) is an option to buy a swap at a future date, at the preset fixed interest rate. For example, a company can buy a 3-month swaption on an underlying 9-month $100 million at 5% fixed interest rate. This gives the company the right to take on a long position in the swap at the end of the third month. The company would make the decision based on the then-prevailing swap rates at the end of the 3-months. Upon exercise, the swap performs just like an ordinary swap. Swaptions can be: payer-swaptions, where the option holder has the right to enter into a pay-fixed swap (i.e. the right to enter into a long position in the swap, therefore hedging a floating interest rate), or receiver-swaptions, where the option holder has the right to enter into a receive-fixed swap (i.e. the right to enter into a short position in the swap).

Swaptions are preferable because:

• Allow the investor to "wait and see", that is to postpone the decision to enter swap contracts to a later point in time. It also minimizes the costs, as the investor is not committed in any way and it has only the option to enter swaps contracts if and only if the conditions are met;
• If at expiration (at the end of the third month) conditions are not met, contracts can be rolled over, that is, contracts can be renewed;
• Allow the investor to focus on the short-term and see how market conditions change. Long-term interest rate swaps are risky (see the intrinsic cash-flow risk). Swaptions give the investor lots of flexibility.

Harvard had a better alternative. Did it never consider it?

The school chose to enter swaps contracts that required a high degree of confidence in the occurrence of specific market conditions. The price the school paid was one billion dollars.

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Stop Investing In Hedge Funds Now! Oh No, Wait A Minute...

“Don't Invest in Hedge Funds” is probably the refrain you have been hearing and reading over and over again recently. The so-called “smart money” has been under attack as it has failed to beat the market and deliver juicy returns. Some hedge fund managers were harshly criticized because of their attacks on the FED and the monetary policies it has executed in recent years. While the reasons for their hatred towards Bernanke could originate from their trading positions (as Brad DeLong explained in this post) we should bear in mind that one size does not fit all. Blaming the hedge fund category as a whole is not only wrong, but silly. First of all, accurately quantifying the returns of hedge funds is very hard, if not impossible. For the simple reason that, the large majority of them do not disclose their performances to the public. Matthew O’Brien, in his latest piece for the Atlantic, used a performance chart looking over only ten years and the HFRX index as if this is an index of hedge funds (like the S&P 500 for stocks for example). But that’s wrong. The HFRX shows the return of an index that attempts to track broad hedge-fund performances through the most diffused hedge fund strategies. Several indices which track hedge funds strategies have been constructed to try to replicate their performances. But this is not an easy task! With a lot of limitations. Robert Frey (Professor at Stony Brook University) and myself, find the EDHEC index much more representative than the HFRX. The EDHEC index takes into account the high fees (1% fixed fee of all assets, 10% of all profits), which have been heavily criticized. This finds Matthew and myself in agreement. If we take a look at the cumulative return over the 2000-2013 time window we can see that:

Chart 1 - Courtesy of Robert Frey

And if we enlarge a little bit the time horizon, here is how the hedge fund performances look like:

Chart 2 - KPMG, AIMA, Centre for Hedge Funds Research

It seems like hedge funds have performed pretty well over a longer period of time. The two charts emphasize the big losses hedge funds incurred during the financial crisis. From Chart 1, we can also see that hedge funds have recently struggled a little bit to generate "abnormal" returns against the S&P 500. One could say “but it does not tell me anything about the risk taken by the industry, performances are not risk-adjusted” and this is of course a reasonable objection. However, it is almost impossible to measure the risk-adjusted returns of hedge funds because any measure of risk-adjusted returns is hocus pocus. Hedge funds have quantitative and qualitative risks that make them unique to evaluate and analyze. Someone else could say “An equally split (50%-50%) stock/bond hedge fund would have done better than the 60%-40% stock/bond portfolio”. Reasonable too. Objections can be many! But let's focus for a moment on the recent performances. Hedge funds lost a lot of money during the crisis and let's admit that they haven't been able to beat the market recently. Are those good excuses not to invest in hedge funds? In my opinion no, and here is why. 

We are missing three very important points here:

1. Hedge Funds provide diversification. Investment strategies of hedge funds vary vastly. To list a few: directional strategies, market-neutral strategies, equity long/short strategies, event driven funds, macro strategies, fixed income arbitrage, short bias etc. A lot of hedge funds try to achieve positive returns using strategies that move in the opposite direction of the major market indices. They therefore suit perfectly for diversification purposes. Risk, the premier journal for risk managers, found that 10 to 15 hedge funds are needed for optimum portfolio diversification: 

2. Hedge funds can improve an investor's overall return. It’s pretty common to think of hedge funds as risky bets. But hedge funds can be used for several purposes. There are in fact, low-volatility hedge funds that can enhance returns at limited costs. Or alternatively, returns can be boosted by using hedge funds that particularly focus on high-return strategies by trading volatile products. Generally speaking, investors look at hedge funds as dynamic and flexible investment vehicles able to be traded under several market circumstances. And when implemented in a portfolio with proper due diligence and awareness of the risks involved, benefits may be several. 

KPMG, AIMA, Centre for Hedge Funds Research

3. Continued interest in hedge funds has never diminished, regardless of the recent negative performances and the adverse articles on the media. The asset under management stands at around $2.375 trillion. Compared to the $100,000 first investment in the first hedge fund under Alfred Winslow Jones in 1949, that should equate to a 30.39% annualized growth rate in AUM. If hedge funds have had such a high growth rate, it would indicate much better performance over time (see Chart 2), especially given the fact that they haven't been able to, historically speaking, advertise. Prequin data shows that 60% of institutional investors, primary source of capital for hedge funds stated in 2013 that they are looking to increase their hedge fund allocation. And this should not be surprising, given the current macroeconomic environment and how hysterically investors have looked for yield in the last year or so. Investors, in fact, are getting prepared for both growth and volatility and use hedge funds as a way to access "dynamic and interconnected markets and to mitigate the risk inherent in these exposures". Like in point 2, the flexibility of these instruments is something that investors demand.

One big problem for the industry as whole, now that some hedge funds will be able to advertise themselves, may be the pollution coming from people who are largely incompetent, crooks, or whoever does not use the most up-to-date financial tools. An investor should be able to pick the good hedge funds. Investing in hedge funds because something is called "hedge fund" should be strongly avoided. And this is easier said than done.  An investor carries together with the trade its own risk attitude as well as its own grade of financial sophistication, with a trade-off between the two. 

Dreaming double figures returns is easy. Finding ways to systematically trade is harder. 

Taking Matthew O'Brien's paradox to the extreme, what’s worse? Rich people blowing money in hedge funds that are highly inaccessible to retail investors or the same retail investors being harmed by mutual funds that take fees from them but don’t deliver alpha?

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The Other Side of Asymmetric Information. A Few Additions On P. Krugman's Recent Post

"Fight Between Jacob And The Angel" - Pier Francesco Mazzucchelli (1610- 1620)

Paul Krugman recently wrote a post explaining how real resources in finance are being devoted to getting an "economic number" before everyone else, taking advantage of asymmetric information. The example Krugman brings is the one where Thomson-Reuters pays the University of Michigan a million dollars a year for early access to the monthly Michigan consumer sentiment survey. In the era of High Frequency Trading (HFT), this of course can be a great advantage over the other market participants. However, this practice is completely legal as explained in this post.

Profiting (illegally) by taking advantage of information asymmetries and insider trading shouldn't sound new to financial insiders. Insider trading is a long-dated illegal activity with the most striking and famous cases concentrated in the stock market (see here, here and here). However, this post will not focus on them. Although stocks are the most heavily traded asset class in insider trading information, there is little to add as these cases are very popular and a lot has been written. 

Other financial products which are very vulnerable to information asymmetry will be at the center of discussion in this post. The first and perhaps, most important example are Credit Default Swaps (CDS). A credit default swap is a credit insurance contract that pays a specified payout on default of the underlying asset. Investor A wants to buy protection against a loan it has issued to company Y. Investor A enters a credit default swap on a specific underlying asset with counterpart B. Investor A pays a premium to the provider B of the credit default swap. If the credit event occurs, B has to repay A in full in a pre-specified way (missed coupon, total default, etc.). What makes CDS so vulnerable to asymmetric information is their intrinsic nature and the lack of regulation and oversight. CDS are vulnerable to insider trading for four specific reasons:

1. Most of the players in the CDS market are financial institutions and insurance companies (retail investors are largely absent); 
2. The majority of CDS are held by the same financial institutions and insurance companies that dominate the CDS market;
3. CDS are opaque products and only recently they have been tried to be regulated with the Dodd-Frank Act;
4. CDS incentives the moral hazard which arises from insider trading information.

As A lends to company Y, A collects several information about Y in order to make sure that company Y will be able to repay the loan. As A wants to buy protection against the default of company Y, it uses the information it has about Y in order to price the credit default swap. The problem with asymmetric information lies in A potentially exploiting the information it has gathered from Y and then indirectly transmitting it to the public markets. As an example, A is aware that company Y is not in a good shape (financially speaking) and may exploit the information it has at the expenses of the sellers of the CDS protection. Company A may profit by betraying company Y and potentially destroying its client with the insider information it has. And here is another problem with CDS: the non-public information which lies within the borrower and the lender.

As for today, the only case of insider trading on CDS we have seen is the one between Deutsche Bank (DB) and the hedge fund Millennium Partners. A bond trader at DB, Mr. Rorechat passed insider information to Mr. Negrin (portfolio manager at Millennium) about a Dutch company that DB was supervising, VNU. The insider information was about a change in the bond offering at VNU. Mr. Negrin consequently bought CDS on VNU’s bonds that increased their value by $1.2 million. I also want to emphasize that whoever has insider trading information on macro news (like  the example given by Paul Krugman) may use CDS indices to profit instead of trading on the stock market. Why? For the same reasons I mentioned above: lack of transparency and regulation and because it’s easier to get caught in the stock market. There are in fact several CDS indices which are suitable for that trade. And of course, the same applies to companies. Whoever holds insider trading information can profit by betting against it on specific tranches of CDS indices based on companies.

Another good example of securities vulnerable to information asymmetry can be found in structured finance. The infamous CDO (Collateralized Debt Obligation) called "ABACUS 2007-AC1" issued by Goldman Sachs in 2007 is probably the best case in point (for a brief explanation on how a CDO is created, packed and sold, please see here). The CDO was put together by John Paulson and made of the worst possible assets at that time (adjustable rate mortgages, low borrower FICO scores, mortgages in states that had experienced high rates of home price appreciation such as Arizona, California, Florida and Nevada). John Paulson was rightly betting on the collapse of the housing market in 2007. Paulson's goal was to profit by betting against ABACUS by using Credit Default Swaps (see how the moral hazard arises from the asymmetric information?). However, GS failed to disclose to its investors how the portfolio underlying the CDO was put together. Investors were unconsciously underestimating the risk they were taking. After all, "the two main indicators of risk available to investors -- credit ratings and the role of the collateral manager -- were both misleading". They thought the portfolio was made of 90 bonds derived from subprime mortgage loans. In one year, the 99% of assets underlying ABACUS defaulted and Paulson personally made $1 billion out of the deal. Even in this case, it should be pretty evident that the seller-side of these products are privileged as they have an asymmetric information advantage over the buy-side. Furthermore, buyers are not in a position to carefully examine how good the investment is or regularly check how it is doing as loan-level disclosure reports are due once per month or even less (while the sell-side gets information on the loan-level performances on a daily basis).

And of course there are then the numerous cases of market manipulations such as the LIBOR, High-Frequency Trading in equities market, in the swap market (IsdaFix) and recently in the FX market too.  But maybe that's the topic for another post...

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* The above panting represents the eternal conflict between good and evil (with no allusion from my side)

Hurricane (Weather) Derivatives: They Are Already The Future!!

Hurricane 'Fran', 1996

Introduction

Hurricanes are the high impact low probability events lurking in the extreme ends of a distribution. Although, they occur on a regular basis with a yearly cycle and past data is plentiful (see Chart 1 below), predicting the expected number and impact of hurricanes remains a challenge. It is indeed impossible to predict a hurricane damage. As showed in Chart 2, there is no trend at all (hurricane damages were adjusted for inflation, wealth growth and population). Financial markets have been attempting to find a way to allocate this risk with the help of hurricane derivatives products.

Chart 1 

Chart 2

Hurricanes are complex and chaotic systems. It is difficult to predict their paths even after their formation. Hurricanes developing over the Atlantic usually start out as a tropical wave from the west coast of Africa and if the circumstances are ideal (e.g. high sea surface temperature and vorticity) the system starts to develop. Over the vast surface of hot water the system gathers strength, organizes and after making landfall it leaves destruction in its wake. The joined effect of strong wind, heavy rain fall and costal surge following the hurricane are responsible for death and damage in the magnitude of dollar billion. On the other hand hurricanes have an important role in the polar heat transfer. The financial impact of these storms is critical for individuals and for big corporations as well: a family can lose its home, a cruise ship company can lose its fleet and Electricity Company may have to change the entire infrastructure due to a great storm.

The most vulnerable regions are the regions on east coast of the United States. There are two distinct trends that make the landfall of a hurricane more damaging. The first trend is in the population trend. An increasing amount of people chose to live on the coastal regions of the United States. The surge in populations has brought increasing wealth, and resulted in higher wealth at risk of hurricane landfall. The second trend is that over half of the US industry is found at the coastal regions. 

What are Hurricane Derivatives?

Hurricane Derivatives are contracts that provide the buyer financial protection against the negative effects of a hurricane. Contracts were first introduced at the Chicago Mercantile Exchange in 2007 and are now mainly traded on the over-the-counter market. The counterpart is usually a financial institution such as insurance and reinsurance companies, hedge funds, energy and utility companies, pension funds.

The pay-off of the contract varies according to the occurrence of certain conditions such as the landfall of the hurricane in a specific geographical area, or the index (upon which the contract is based, called CHI) reaching a certain maximum amount. If conditions are met, the pay-off is computed according to the number of contracts the buyer hold and the amount of the CME Hurricane Index (CHI) at the time the conditions are met. The contract is quoted on CHI points, with tick size of 0.1 point. The total number of contracts outstanding (open interest) was 2,700 in 2011 (an increase of 68.75% compared to the previous year) with trading volume of 4,000. 

Liquidity

Hurricane derivatives are among the least liquid of all weather products. Open interest and trading volume are very low compared to other derivatives products. The reasons for the lack of liquidity must be researched in the characteristic seasonality of the hurricane phenomena: liquidity increases as the storm begins to develop and decreases as the probability/frequency of an event decreases. Consequently, most of the trades happen at the beginning of the hurricane season with trading volume decreasing as the season comes to an end. Furthermore, institutional buyers use hurricane derivatives as substitutes for insurance, as they are highly customizable, further dragging down liquidity. Individuals can in fact chose to customize their contract according to the name of the storm, landfall location, location boundaries (once these are  crossed, payment triggers) the CHI threshold beyond which the  payment triggers, etc. Last but not least, the minimum contract size makes these contracts highly attractive to retail investors, although they haven't traded much these contracts so far. 

Underlying

The underlying is the CME Hurricane Index (CHI). The index is driven by two factors: maximum sustained winds (V, in mph) and the radius of hurricane-force winds (R). The formula to compute CHI at a certain point in time is:

With V0 = 74 statute miles per hour and R0 = 60 statute miles set as threshold. The NOAA National Hurricane Center declares that a tropical cyclone turn into a hurricane (in the Atlantic and East Pacific basins) if these two values are reached. If V is less than 74 mph, then CHI is set equal to zero. A storm with sustained winds of 80 miles per hour and a radius of 30 miles scored 2.14 on the scale. To put this into perspective, 20 hours before landfall Katrina had scored 27 on the scale (see graph below). The track of the storm is closely monitored and in case it makes landfall, the CHI index number is multiplied by the notional amount of each contract ($1000, in case of standard futures and options). 

Explaining The Subprime Mortgage Meltdown (And Its Causes) Through Morgan Stanley's 9 billion loss.

Explaining (or at least trying to explain) the subprime mortgage meltdown with a single post is not an easy task. Furthermore, the reasons behind the crisis are still being studied, its interconnections analyzed, the role played by banks, rating agencies and federal regulators scrutinized. Meanwhile, 6 years later, CDOs are back and from here we should begin our analysis...

...After all, it all began with securitization...

In the securitization process, bank X creates a legal entity called SPV which issues the asset-backed securities. These are issued in several tranches (made of several loans), each tranche having a rating assigned by a rating agency according to the probability of default. Bank X sells the loans to the SPV and receives cash. The issuer (SPV) is structured for remote bankruptcy, that is bank X cannot be sued by the investors if something goes wrong. In a few words, bank X gets rid of the liabilities (and the risk associated with them). The underlying value of the asset-backed securities depends directly on the cash flow generated by the underlying assets (interest and principal). If Carl, John or Aron experience difficulty in repaying the loans, the value of the asset-backed security goes down. Hypothetically, the asset-backed security can be worth zero, if borrowers default in series. The picture above shows the type of contract Morgan Stanley was trading: an hybrid between a CDO (Collateralized Debt Obligation) and a CBO (Collateralized Bond Obligation). 

Now, we are in 2004, and the market for these kind of financial instruments was flourishing. Morgan Stanley was creating asset-backed bonds at a faster rate than the loans borrowers were purchasing. So it was de facto exposed to counter party credit risk: that is, in the time between the purchase of the loans and the selling of the bonds, borrowers could experience difficulty in meeting their mortgage commitments and the value of the bonds could go down. Morgan Stanley decided to buy insurance against this kind of event by entering credit default swaps for $2 billion. 

These credit default swaps were based on the riskiest loans of triple-B tranches of asset-backed bonds. Morgan Stanley purposely decided to hedge with riskier triple-B tranches as these were more likely to pay off in the future. In 2006, Howie Hubler and the other traders at Morgan Stanley were certain borrowers would have defaulted one after the other, sooner or later. Yes, they had realized that the mortgage-backed securities market was a broken toy and were clearly betting on the collapse of the housing market. Morgan Stanley paid the counterpart 2.5% per year and, in case of default, it would have been entitled to the entire pie ($2 billion). 

You may be wondering: why did the counterpart take the other side of the bet? In other words: why the counterpart was unconsciously accepting to pay $2 billion to Morgan Stanley if only 4% of losses on the underlying loans were needed to trigger a credit event? (Note: 4% of losses on loans underlying mortgage-backed securities are experienced, on average, in good times). We don't know. However, we shouldn't be surprised to see this kind of perverse reasoning in the financial markets (see here and here). 

Howie Hubler and the other traders at Morgan Stanley bought insurance and entered credit default swaps for $2 billion. Morgan Stanley had to make regular payments to the counterpart. The premium Morgan Stanley was paying was high as the tranches were triple-B rated, so Morgan Stanley was experiencing short-term losses in a order of magnitude of $200 million. In order to hedge this exposure, Howie Hubler decided to enter triple-A rated credit default swaps for $16 billion and take the other side of the bet. The infamous triple-A hedge. In 2007, Howie Hubler was selling insurance to institutional investors in order to pocket the tiny premium on triple-A tranches to compensate for the short-term losses on the $2 billion credit default swap. In order to do so, Howie Hubler had to sell a lot of CDS. $16 billion to be precise.

Hubler considered this trade as risk-free as the rating was excellent (AAA). He thought the correlation of the prices of the subprime mortgage bonds inside CDO was low. Morgan Stanley estimated it approximately around 30%. Instead it was very close to 100%. As the loans underlying the asset-backed securities experienced the first losses, it all fell down. The mortgage-backed securities market proved to be a house of cards with a domino effect that severely affected the real economy. Morgan Stanley turn out to lose $9.2 billion.

How did Securitization get its start?

It all began after the 1980s...

It all goes back to the CRA (Community Reinvestment Act) that enhanced the availability of “affordable housing” via the use of “creative financing techniques”.

It all goes back to the favorable accounting and capital regulations that enabled Wall Street to create an assembly line for CDOs, under direct approval of Alan Greenspan.

It all goes back to the active encouragement by federal regulators of OTC derivatives by all types of financial institutions.

It all goes back to the 'decimalization' introduced in March 2001 and the Reg FD (October 2000) that hit major Wall Street firms hard, so they focused on new OTC assets instead of exchange traded ones (with a much wider bid-ask spreads). 

It all goes back to the rating agencies that gave OTC products investment grade ratings, basing their judgment on no credit history and consequently reducing capital requirements. 

Finally, it all goes back to the housing prices that in aggregate were supposed to always go up...

Paul Volcker: "Vision without execution is hallucination."

Reference:

Michael Lewis (2010) - The Big Short

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