Long Term Capital Management. Note Quote.

Long Term Capital Management, or LTCM, was a hedge fund founded in 1994 by John Meriwether, the former head of Salomon Brothers’s domestic fixed-income arbitrage group. Meriwether had grown the arbitrage group to become Salomon’s most profitable group by 1991, when it was revealed that one of the traders under his purview had astonishingly submitted a false bid in a U.S. Treasury bond auction. Despite reporting the trade immediately to CEO John Gutfreund, the outcry from the scandal forced Meriwether to resign.

Meriwether revived his career several years later with the founding of LTCM. Amidst the beginning of one of the greatest bull markets the global markets had ever seen, Meriwether assembled a team of some of the world’s most respected economic theorists to join other refugees from the arbitrage group at Salomon. The board of directors included Myron Scholes, a coauthor of the famous Black-Scholes formula used to price option contracts, and MIT Sloan professor Robert Merton, both of whom would later share the 1997 Nobel Prize for Economics. The firm’s impressive brain trust, collectively considered geniuses by most of the financial world, set out to raise a $1 billion fund by explaining to investors that their profoundly complex computer models allowed them to price securities according to risk more accurately than the rest of the market, in effect “vacuuming up nickels that others couldn’t see.”

One typical LTCM trade concerned the divergence in price between long-term U.S. Treasury bonds. Despite offering fundamentally the same (minimal) default risk, those issued more recently – known as “on-the-run” securities – traded more heavily than those “off-the-run” securities issued just months previously. Heavier trading meant greater liquidity, which in turn resulted in ever-so-slightly higher prices. As “on-the-run” securities become “off-the-run” upon the issuance of a new tranche of Treasury bonds, the price discrepancy generally disappears with time. LTCM sought to exploit that price convergence by shorting the more expensive “on-the-run” bond while purchasing the “off- the-run” security.

By early 1998 the intellectual firepower of its board members and the aggressive trading practices that had made the arbitrage group at Salomon so successful had allowed LTCM to flourish, growing its initial $1 billion of investor equity to $4.72 billion. However, the miniscule spreads earned on arbitrage trades could not provide the type of returns sought by hedge fund investors. In order to make transactions such as these worth their while, LTCM had to employ massive leverage in order to magnify its returns. Ultimately, the fund’s equity component sat atop more than $124.5 billion in borrowings for total assets of more than $129 billion. These borrowings were merely the tip of the ice-berg; LTCM also held off-balance-sheet derivative positions with a notional value of more than $1.25 trillion.


The fund’s success began to pose its own problems. The market lacked sufficient capacity to absorb LTCM’s bloated size, as trades that had been profitable initially became impossible to conduct on a massive scale. Moreover, a flood of arbitrage imitators tightened the spreads on LTCM’s “bread-and-butter” trades even further. The pressure to continue delivering returns forced LTCM to find new arbitrage opportunities, and the fund diversified into areas where it could not pair its theoretical insights with trading experience. Soon LTCM had made large bets in Russia and in other emerging markets, on S&P futures, and in yield curve, junk bond, merger, and dual-listed securities arbitrage.

Combined with its style drift, the fund’s more than 26 leverage put LTCM in an increasingly precarious bubble, which was eventually burst by a combination of factors that forced the fund into a liquidity crisis. In contrast to Scholes’s comments about plucking invisible, riskless nickels from the sky, financial theorist Nassim Taleb later compared the fund’s aggressive risk taking to “picking up pennies in front of a steamroller,” a steamroller that finally came in the form of 1998’s market panic. The departure of frequent LTCM counterparty Salomon Brothers from the arbitrage market that summer put downward pressure on many of the fund’s positions, and Russia’s default on its government-issued bonds threw international credit markets into a downward spiral. Panicked investors around the globe demonstrated a “flight to quality,” selling the risky securities in which LTCM traded and purchasing U.S. Treasury securities, further driving up their price and preventing a price convergence upon which the fund had bet so heavily.

None of LTCM’s sophisticated theoretical models had contemplated such an internationally correlated credit market collapse, and the fund began hemorrhaging money, losing nearly 20% of its equity in May and June alone. Day after day, every market in which LTCM traded turned against it. Its powerless brain trust watched in horror as its equity shrank to $600 million in early September without any reduction in borrowing, resulting in an unfathomable 200 leverage ratio. Sensing the fund’s liquidity crunch, Bear Stearns refused to continue acting as a clearinghouse for the fund’s trades, throwing LTCM into a panic. Without the short-term credit that enabled its entire trading operations, the fund could not continue and its longer-term securities grew more illiquid by the day.

Obstinate in their refusal to unwind what they still considered profitable trades hammered by short-term market irrationality, LTCM’s partners refused a buyout offer of $250 million by Goldman Sachs, ING Barings, and Warren Buffet’s Berkshire Hathaway. However, LTCM’s role as a counterparty in thousands of derivatives trades that touched investment firms around the world threatened to provoke a wider collapse in international securities markets if the fund went under, so the U.S. Federal Reserve stepped in to maintain order. Wishing to avoid the precedent of a government bailout of a hedge fund and the moral hazard it could subsequently encourage, the Fed invited every major investment bank on Wall Street to an emergency meeting in New York and dictated the terms of the $3.625 billion bailout that would preserve market liquidity. The Fed convinced Bankers Trust, Barclays, Chase, Credit Suisse First Boston, Deutsche Bank, Goldman Sachs, Merrill Lynch, J.P. Morgan, Morgan Stanley, Salomon Smith Barney, and UBS – many of whom were investors in the fund – to contribute $300 million apiece, with $125 million coming from Société Générale and $100 million from Lehman Brothers and Paribas. Eventually the market crisis passed, and each bank managed to liquidate its position at a slight profit. Only one bank contacted by the Fed refused to join the syndicate and share the burden in the name of preserving market integrity.

That bank was Bear Stearns.

Bear’s dominant trading position in bonds and derivatives had won it the profitable business of acting as a settlement house for nearly all of LTCM’s trading in those markets. On September 22, 1998, just days before the Fed-organized bailout, Bear put the final nail in the LTCM coffin by calling in a short-term debt in the amount of $500 million in an attempt to limit its own exposure to the failing hedge fund, rendering it insolvent in the process. Ever the maverick in investment banking circles, Bear stubbornly refused to contribute to the eventual buyout, even in the face of a potentially apocalyptic market crash and despite the millions in profits it had earned as LTCM’s prime broker. In typical Bear fashion, James Cayne ignored the howls from other banks that failure to preserve confidence in the markets through a bailout would bring them all down in flames, famously growling through a chewed cigar as the Fed solicited contributions for the emergency financing, “Don’t go alphabetically if you want this to work.”

Market analysts were nearly unanimous in describing the lessons learned from LTCM’s implosion; in effect, the fund’s profound leverage had placed it in such a precarious position that it could not wait for its positions to turn profitable. While its trades were sound in principal, LTCM’s predicted price convergence was not realized until long after its equity had been wiped out completely. A less leveraged firm, they explained, might have realized lower profits than the 40% annual return LTCM had offered investors up until the 1998 crisis, but could have weathered the storm once the market turned against it. In the words of economist John Maynard Keynes, the market had remained irrational longer than LTCM could remain solvent. The crisis further illustrated the importance not merely of liquidity but of perception in the less regulated derivatives markets. Once LTCM’s ability to meet its obligations was called into question, its demise became inevitable, as it could no longer find counterparties with whom to trade and from whom it could borrow to continue operating.

The thornier question of the Fed’s role in bailing out an overly aggressive investment fund in the name of market stability remained unresolved, despite the Fed’s insistence on private funding for the actual buyout. Though impossible to foresee at the time, the issue would be revisited anew less than ten years later, and it would haunt Bear Stearns. With negative publicity from Bear’s $38.5 million settlement with the SEC regarding charges that it had ignored fraudulent behavior by a client for whom it cleared trades and LTCM’s collapse behind it, Bear Stearns continued to grow under Cayne’s leadership, with its stock price appreciating some 600% from his assumption of control in 1993 until 2008. However, a rapid-fire sequence of negative events began to unfurl in the summer of 2007 that would push Bear into a liquidity crunch eerily similar to the one that felled LTCM.


Credit Risk Portfolio. Note Quote.


The recent development in credit markets is characterized by a flood of innovative credit risky structures. State-of-the-art portfolios contain derivative instruments ranging from simple, nearly commoditized contracts such as credit default swap (CDS), to first- generation portfolio derivatives such as first-to-default (FTD) baskets and collateralized debt obligation (CDO) tranches, up to complex structures involving spread options and different asset classes (hybrids). These new structures allow portfolio managers to implement multidimensional investment strategies, which seamlessly conform to their market view. Moreover, the exploding liquidity in credit markets makes tactical (short-term) overlay management very cost efficient. While the outperformance potential of an active portfolio management will put old-school investment strategies (such as buy-and-hold) under enormous pressure, managing a highly complex credit portfolio requires the introduction of new optimization technologies.

New derivatives allow the decoupling of business processes in the risk management industry (in banking, as well as in asset management), since credit treasury units are now able to manage specific parts of credit risk actively and independently. The traditional feedback loop between risk management and sales, which was needed to structure the desired portfolio characteristics only by selective business acquisition, is now outdated. Strategic cross asset management will gain in importance, as a cost-efficient overlay management can now be implemented by combining liquid instruments from the credit universe.

In any case, all these developments force portfolio managers to adopt an integrated approach. All involved risk factors (spread term structures including curve effects, spread correlations, implied default correlations, and implied spread volatilities) have to be captured and integrated into appropriate risk figures. We have a look on constant proportion debt obligations (CPDOs) as a leveraged exposure on credit indices, constant proportion portfolio insurance (CPPI) as a capital guaranteed instrument, CDO tranches to tap the correlation market, and equity futures to include exposure to stock markets in the portfolio.

For an integrated credit portfolio management approach, it is of central importance to aggregate risks over various instruments with different payoff characteristics. In this chapter, we will see that a state-of-the-art credit portfolio contains not only linear risks (CDS and CDS index contracts) but also nonlinear risks (such as FTD baskets, CDO tranches, or credit default swaptions). From a practitioner’s point of view there is a simple solution for this risk aggregation problem, namely delta-gamma management. In such a framework, one approximates the risks of all instruments in a portfolio by its first- and second-order sensitivities and aggregates these sensitivities to the portfolio level. Apparently, for a proper aggregation of risk factors, one has to take the correlation of these risk factors into account. However, for credit risky portfolios, a simplistic sensitivity approach will be inappropriate, as can be seen by the characteristics of credit portfolio risks shows:

  • Credit risky portfolios usually involve a larger number of reference entities. Hence, one has to take a large number of sensitivities into account. However, this is a phenomenon that is already well known from the management of stock portfolios. The solution is to split the risk for each constituent into a systematic risk (e.g., a beta with a portfolio hedging tool) and an alpha component which reflects the idiosyncratic part of the risk.

  • However, in contrast to equities, credit risk is not one dimensional (i.e., one risky security per issuer) but at least two dimensional (i.e., a set of instruments with different maturities). This is reflected in the fact that there is a whole term structure of credit spreads. Moreover, taking also different subordination levels (with different average recovery rates) into account, credit risk becomes a multidimensional object for each reference entity.
  • While most market risks can be satisfactorily approximated by diffusion processes, for credit risk the consideration of events (i.e., jumps) is imperative. The most apparent reason for this is that the dominating element of credit risk is event risk. However, in a market perspective, there are more events than the ultimate default event that have to be captured. Since one of the main drivers of credit spreads is the structure of the underlying balance sheet, a change (or the risk of a change) in this structure usually triggers a large movement in credit spreads. The best-known example for such an event is a leveraged buyout (LBO).
  • For credit market players, correlation is a very special topic, as a central pricing parameter is named implied correlation. However, there are two kinds of correlation parameters that impact a credit portfolio: price correlation and event correlation. While the former simply deals with the dependency between two price (i.e., spread) time series under normal market conditions, the latter aims at describing the dependency between two price time series in case of an event. In its simplest form, event correlation can be seen as default correlation: what is the risk that company B defaults given that company A has defaulted? While it is already very difficult to model this default correlation, for practitioners event correlation is even more complex, since there are other events than just the default event, as already mentioned above. Hence, we can modify the question above: what is the risk that spreads of company B blow out given that spreads of company A have blown out? In addition, the notion of event correlation can also be used to capture the risk in capital structure arbitrage trades (i.e., trading stock versus bonds of one company). In this example, the question might be: what is the risk that the stock price of company A jumps given that its bond spreads have blown out? The complicated task in this respect is that we do not only have to model the joint event probability but also the direction of the jumps. A brief example highlights why this is important. In case of a default event, spreads will blow out accompanied by a significant drop in the stock price. This means that there is a negative correlation between spreads and stock prices. However, in case of an LBO event, spreads will blow out (reflecting the deteriorated credit quality because of the higher leverage), while stock prices rally (because of the fact that the acquirer usually pays a premium to buy a majority of outstanding shares).

These show that a simple sensitivity approach – e.g., calculate and tabulate all deltas and gammas and let a portfolio manager play with – is not appropriate. Further risk aggregation (e.g., beta management) and risk factors that capture the event risk are needed. For the latter, a quick solution is the so-called instantaneous default loss (IDL). The IDL expresses the loss incurred in a credit risk instrument in case of a credit event. For single-name CDS, this is simply the loss given default (LGD). However, for a portfolio derivative such as a mezzanine tranche, this figure does not directly refer to the LGD of the defaulted item, but to the changed subordination of the tranche because of the default. Hence, this figure allows one to aggregate various instruments with respect to credit events.

Asset Backed Securities. Drunken Risibility.

Asset Backed Securities (ABS) are freely traded financial instruments that represent packages of loans issued by the commercial banks. The majority are created from mortgages, but credit card debt, commercial real estate loans, student loans, and hedge fund loans are also known to have been securitized. The earliest form of ABS within the American banking system appears to stem from the creation of the Federal National Mortgage Association (Fannie Mae) in 1938 as part of amendments to the US National Housing Act, a Great Depression measure aimed at creating loan liquidity. Fannie Mae, and the other Government Sponsored Enterprises buy loans from approved mortgage sellers, typically banks, and create guaranteed financial debt instruments from them, to be sold on the credit markets. The resulting bonds, backed as they are by loan insurance, are widely used in pension funds and insurance companies, as a secure, financial instrument providing a predictable, low risk return.

The creation of a more general form of Mortgage Backed Security is credited to Bob Dall and the trading desk of Salmon brothers in 1977 by Michael Lewis (Liar’s Poker Rising Through the Wreckage on Wall Street). Lewis also describes a rapid expansion in their sale beginning in 1981 as a side effect of the United States savings and loans crisis. The concept was extended in 1987 by bankers at Drexel Burnham Lambert Inc. to corporate bonds and loans in the form of Collateralized Debt Obligations (CDOs), which eventually came to include mortgage backed securities, and in the form of CDO-Squared instruments, pools of CDO.

Analysis of the systemic effects of Asset Backed Security has concentrated chiefly on their ability to improve the quantity of loans, or loan liquidity, which has been treated as a positive feature by Greenspan. It has also been noted that securitization allowed banks to increase their return on capital by transforming their operations into a credit generating pipeline process. Hyun Song Shin has also analysed their effect on bank leverage and the stability of the larger financial system within an accounting framework. He highlights the significance of loan supply factors in causing the sub-prime crisis. Although his model appears not to completely incorporate the full implications of the process operating within the capital reserve regulated banking system, it presents an alternate, matrix based analysis of the uncontrolled debt expansion that these instruments permit.

The systemic problem introduced by asset backed securities, or any form of sale that transfers loans made by commercial banking institutions outside the regulatory framework is that they allow banks to escape the explicit reserve and regulatory capital based regulation on the total amount of loans being issued against customer deposits. This has the effect of steadily increasing the ratio of bank originated loans to money on deposit within the banking system.

The following example demonstrates the problem using two banks, A and B. For simplicity fees related to loans and ABS sales are excluded. It is assumed that the deposit accounts are Net Transaction accounts carry a 10% reserve requirement, and that both banks are ”well capitalised” and that the risk weighted multiplier for the capital reserve for these loans is also 10.

The example proceeds as a series of interactions as money flows between the two banks. The liabilities (deposits) and assets (loans) are shown, with loans being separated into bank loans, and Mortgage Backed Securities (MBS), depending on their state.

Initial Conditions: To simplify Bank B is shown as having made no loans, and has excess reserves at the central bank to maintain the balance sheet. The normal inter-bank and central bank lending mechanisms would enable the bank to compensate for temporary imbalances during the process under normal conditions. All deposit money used within the example remains on deposit at either Bank A or Bank B. On the right hand side of the table the total amount of deposits and loans for both banks is shown.


Step 1: Bank A creates a $1000 Mortgage Backed Security from the loan on its balance sheet.


Step 2: The securitized loan is sold to the depositor at Bank B. $1000 is paid to Bank A from that depositor in payment for the loan. Bank A now has no loans outstanding against its deposits, and the securitized loan has been moved outside of banking system regulation. Note that total deposits at the two banks have temporarily shrunk due to the repayment of the loan capital at A. The actual transfer of the deposits between the banks is facilitated through the reserve holdings which also function as clearing funds.


Step 3: As Bank A now has no loans against its deposits, and is within its regulatory capital ratios, it can make a new $1000 loan. The funds from this loan are deposited at Bank B. The sum of the deposits rises as a result as does the quantity of loans. Note that the transfer of the loan money from Bank A to Bank B again goes through the reserve holdings in the clearing system and restores the original balance at Bank B.


Step 4: Bank A securitizes the loan made in Step 3 repeating Step 1.


Step 5: Bank A sells the MBS to the depositor at Bank B, repeating Step 2.


Step 6: Bank A makes a new loan which is deposited at Bank B, repeating Step 3.


Step 7: Bank A securitizes the loan made in Step 6, repeating Step 4.


Since the Deposit and Loan positions of the two banks are identical in all respects in Steps (1,4), (2,5), (3,6) and (4,7) the process can continue indefinitely, resulting in expansion of the total commercial bank originated loan supply independent of central bank control.

This is a simplified version of the flows between loans, deposits, and asset backed securities that occur daily in the banking system. At no point has either bank needed recourse to central bank funds, or broken any of their statutory requirements with respect to capitalisation or reserve requirements where they apply.

The problem is the implicit assumption with reserve based banking systems that bank originated loans remain within the banking system. Allowing the sale of loans to holders outside of the regulated banking system (i.e. to entities other than regulated banks) removes these loans from that control and thus creates a systemic loophole in the regulation of the commercial bank loan supply.

The introduction of loans sales has consequently created a novel situation in those modern economies that allow them, not only in causing a significant expansion in total lending from the banking sector, but also in changing the systemic relationship between the quantity of money in the system to the quantity of bank originated debt, and thereby considerably diluting the influence the central bank can exert over the loan supply. The requirement that no individual bank should lend more than their deposits has been enforced by required reserves at the central bank since the 19th century in Europe, and the early 20th century in the USA. Serendipitously, this also created a systemic limit on the ratio of money to bank originated lending within the monetary system. While the sale of Asset Backed Securities does not allow any individual bank to exceed this ratio at any given point in time, as the process evolves the banking system itself exceeds it as loans are moved outside the constraints provided by regulatory capital or reserve regulation, thereby creating a mechanism for unconstrained growth in commercial bank originated lending.

While the asset backed security problem explains the dramatic growth in banking sector debt that has occurred over the last three decades, it does not explain the accompanying growth in the money supply. Somewhat uniquely of the many regulatory challenges that the banking system has created down the centuries, the asset backed security problem, in and of itself does not cause the banks, or the banking system to ”print money”.

The question of what exactly constitutes money in modern banking systems is a non-trivial one. As the examples above show, bank loans create money in the form of bank deposits, and bank deposits can be used directly for monetary purposes either through cheques or more usually now direct electronic transfer. For economic purposes then, bank deposits can be regarded as directly equivalent to physical money. The reality within the banking system however is somewhat more complex, in that transfers between bank deposits must be performed using deposits in the clearing mechanisms, either through the reserves at the central bank, or the bank’s own asset deposits at other banks. Nominally limits on the total quantity of central bank reserves should in turn limit the growth in bank deposits from bank lending, but it is clear from the monetary statistics that this is not the case.

Individually commercial banks are limited in the amount of money they can lend. They are limited by any reserve requirements for their deposits, by the accounting framework that surrounds the precise classification of assets and liabilities within their locale, and by the ratio of their equity or regulatory capital to their outstanding, risk weighted loans as recommended by the Basel Accords. However none of these limits is sufficient to prevent uncontrolled expansion.

Reserve requirements at the central bank can only effectively limit bank deposits if they apply to all accounts in the system, and the central bank maintains control over any mechanisms that allow individual banks to increase their reserve holdings. This appears not to be the case. Basel capital restrictions can also limit bank lending. Assets (loans) held by banks are classified by type, and have accordingly different percentage capital requirements. Regulatory capital requirements are divided into two tiers of capital with different provisions and risk categorisation applying to instruments held in them. To be adequately capitalised under the Basel accords, a bank must maintain a ratio of at least 8% between its Tier 1 and Tier 2 capital reserves, and its loans. Equity capital reserves are provided by a bank’s owners and shareholders when the bank is created, and exist to provide a buffer protecting the bank’s depositors against loan defaults.

Under Basel regulation, regulatory capital can be held in a variety of instruments, depending on Tier 1 or Tier 2 status. It appears that some of those instruments, in particular subordinated debt and hybrid debt capital instruments, can represent debt issued from within the commercial banking system.

Annex A – Basel Capital Accords, Capital Elements Tier 1

(a) Paid-up share capital/common stock

(b) Disclosed reserves

Tier 2

(a) Undisclosed reserves

(b) Asset revaluation reserves

(c) General provisions/general loan-loss reserves

(d) Hybrid (debt/equity) capital instruments

(e) Subordinated debt

Subordinated debt is defined in Annex A of the Basel treaty as:

(e) Subordinated term debt: includes conventional unsecured subordinated debt capital instruments with a minimum original fixed term to maturity of over five years and limited life redeemable preference shares. During the last five years to maturity, a cumulative discount (or amortisation) factor of 20% per year will be applied to reflect the diminishing value of these instruments as a continuing source of strength. Unlike instruments included in item (d), these instruments are not normally available to participate in the losses of a bank which continues trading. For this reason these instruments will be limited to a maximum of 50% of tier 1.

This is debt issued by the bank, in various forms, but of guaranteed long duration, and controlled repayment. In effect, it allows Banks to hold borrowed money in regulatory capital. It is subordinate to the claims of depositors in the event of Bank failure. The inclusion of subordinated debt in Tier 2 allows financial instruments created from lending to become part of the regulatory control on further lending, creating a classic feedback loop. This can also occur as a second order effect if any form of regulatory capital can be purchased with money borrowed from within the banking system