Learning From Financial Disasters

After completing this reading, you should be able to:

Analyze the key factors that led to and derive the lessons learned from case studies involving the following risk factors:

• Interest rate risk, including the 1980s savings and loan crisis in the US
• Funding liquidity risk, including Lehman Brothers, Continental Illinois, and Northern Rock
• Implementing hedging strategies, including the Metallgesellschaft case
• Model risk, including the Niederhoffer case, Long Term Capital Management, and the London Whale case
• Rogue trading and misleading reporting, including the Barings case
• Financial engineering and complex derivatives, including Bankers Trust, the Orange County case, and Sachsen Landesbank
• Reputational risk, including the Volkswagen case
• Corporate governance, including the Enron case
• Cyber risk, including the SWIFT case

In this chapter, we look at famous financial disasters that have been witnessed over the years. Although each case study has its distinctive elements, they all have something in common: Certain risk factors were ignored, resulting in major financial loss. We are going to look at how each of these disasters came up, identify the warning signs that were ignored, and attempt to draw relevant lessons that can help avert similar disasters in the future.

Interest Rate Risk

Interest rate risk is the danger that a change in interest rates will cause the value of assets to decline and that of liabilities to increase. Over the last century, thousands of firms have failed as a result of interest rate risk. Between 1986 and 1995, for example, nearly a third of the 3,234 savings and loan associations in the United States failed.

The Savings and Loan Crisis

In the 1980s, the savings and loans industry in the United States suffered through a period of distress.

Savings and Loans (S&Ls) associations were founded in the 18^th century with the sole purpose of funding homeownership. At the time, banks did not lend money for residential mortgages. S&L members would pool their savings and lend the money to a few members to finance their home purchases. After repaying the funds, other members would also get a chance to borrow.

Notably, S&Ls were governed by the so-called “Regulation Q,” which set their minimum capital requirements and capital adequacy standards. Under regulation Q, S&Ls were required to pay depositors a rate of interest that was significantly lower than that offered elsewhere. Furthermore, S&Ls were not allowed to offer commercial loans to avoid risky lending. The overriding goal among policymakers and the government was to make thrifts focus solely on promoting housing and homeownership.

For a long period, these regulations worked well for S&Ls as it meant they could pay low rates on short-term deposits, pool the funds, and then provide mortgage loans at a higher interest rate. To their advantage, the demand for homes continued to rise, especially in the first half of the 19^thcentury.

In the 1970s, however, there was a dramatic increase in both interest rates and inflation. This had two main implications:

• Depositors trooped into S&Ls to withdraw their money, eying higher rates elsewhere. This meant that the amount available for lending reduced significantly.
• Funding costs for S&Ls increased significantly, wiping out the interest rate spread they depended on to make a profit. In other words, short-term deposits became costlier, and therefore the margin between the cost of funds and profit from long-term fixed-rate mortgages decreased.

A high rate of inflation also meant that the number of mortgage applications reduced, further reducing revenue for S&Ls. The low demand for mortgages combined with higher interest rates elsewhere resulted in an unprecedented loss in the value of outstanding mortgages. As a result, the net worth of most S&Ls was essentially wiped out. And because the existing regulations severely restricted alternative profit-making investments, S&Ls had to stick with a dwindling portfolio of low-interest mortgages as their only income source. While all this was happening, alternative investments were increasingly gaining popularity, especially money market funds, which offered higher returns.

In an attempt to stem the tide and restore some financial stability among S&Ls, the US government relaxed the regulations that had been in place for decades.
Several changes were introduced to allow S&Ls to “grow” out of their problems. For the first time, the government was explicitly seeking to influence S&L profits as opposed to promoting housing and homeownership. For instance, interest rate caps were removed, and S&Ls were allowed to offer commercial loans. What’s more, S&Ls could choose to be under either a state or a Federal charter. Federally-chartered thrifts took full advantage of the deregulation and rushed to become federally chartered, because of the advantages associated with a federal charter. Deposit insurance was also increased from $40,000 to $100,000 in an attempt to restore some confidence among depositors.

These regulatory changes did not quite generate the intended effect. For instance, the availability of deposit insurance led to a moral hazard. S&Ls engaged in even riskier lending activities. Ultimately, it is estimated that S&Ls suffered a combined loss of more than $160 billion. To bail them out, taxpayers paid $132 billion. The Federal Savings and Loan Insurance Corporation paid $20 billion to depositors of failed S&Ls before it went bankrupt. The S&Ls paid the remaining amount.

Lessons Learned

1. Regulation is good, but overregulation can be dangerous!

One of the root causes of the S&L industry’s woes was overregulation. Federal regulation had some very strict and precise conditions under which all S&Ls operated. Initially, for example, S&Ls were barred from offering commercial loans; they were only allowed to offer mortgages to facilitate homeownership. That prevented them from experimenting with different ways to adapt to changing market conditions. Regulators charged with defining “acceptable assets” in insurance and banking should take heed.

2. Deposit insurance can result in increased market indiscipline

The introduction of federal insurance guarantees can inadvertently trigger greater risk-taking among banks and insurance firms. It may create a situation where both lenders and depositors feel they have nothing to lose.

• To mitigate interest rate risk, there’s a need for banks to have assets that are highly correlated with liabilities. That can be achieved by having a diversified portfolio of assets and making use of interest rates derivative products such as caps, floors, and swaps.

Funding Liquidity Risk

Funding liquidity risk refers to the possibility that a bank could find itself unable to settle obligations with immediacy. It has much to do with:

• The risk that bank will be unable to pay its debts when they fall due
• The risk that the bank cannot meet the demand of customers wishing to withdraw their deposits
• The risk that a bank will be unable to roll over short-term credit, e.g., commercial paper.

There are two main sources of funding liquidity risk:

1. External market conditions, such as changes in supply and demand.
2. Structural balance sheet risks (Balance sheet risk is uncertainty about future values of balance sheet items not directly related to business or financing activities. Examples are interest rate risk and liquidity risk).

Let’s look at a few case studies where funding liquidity risk played a starring role:

Lehman Brothers

The collapse of Lehman Brothers presents the most spectacular and perhaps the most documented event during the 2007/2009 financial crisis. Here’s how the crisis unfolded.

One Henry Lehman founded Lehman Brothers in 1884 as a general and dry goods store. Soon afterward, Mr. Henry was joined by his brothers Emanuel and Mayer, and that’s how the name “Lehman Brothers” came about. For many years, the company conducted business as a private institution until the year 1994 when it opened its ownership to the public through an IPO that generated well over $3.3 billion. At this point, the company ventured into commercial and investment banking activities.

Lehman Brothers’ entry into the commercial and investment banking market coincided with the change from the originate-to-keep business model to the originate-to-distribute model. Most banks were increasingly offering securitized assets built upon mortgages sold to residential customers. Lehman Brothers became one of the pioneers of securitization, and its fortunes greatly improved. Between 2003 and 2004, for example, the company acquired five mortgage lenders in an attempt to consolidate its grip on the securitization market further. For a while, Lehman Brothers recorded fast growth fueled by the house price bubble. In early 2007, the firm surpassed Bear Sterns and became the largest underwriter for mortgage-backed securities.

It wasn’t until the second half of 2007 when cracks started to appear in the originate-to-distribute business model. It became evident that the US housing bubble had burst and that the subprime mortgage market was in deep trouble. As a result, investor confidence began to erode, and firms heavily invested in subprime securities all of a sudden found themselves unable to borrow at similar terms as before. In July of that year, the conditions were so bad that Bear Stearns (Lehman’s Brothers’ top competitor) had to support two of its hedge funds following steep losses caused by their subprime mortgage exposure.

Banks are naturally leveraged institutions that prefer debt to equity, and Lehman Brothers followed the script. In the run-up to the crisis, however, Lehman pursued leverage to levels not seen before. To put things in perspective, the bank had an assets-to-equity ratio of approximately 31:1 by mid-2007. Critically, the bank turned to short-term debt to fund its day-to-day operations, particularly the repo market.

As it turned out, the bank’s overreliance on the repo market exposed it to serious funding problems because it had to keep investors (counterparties) happy at a time when the industry was witnessing dwindling fortunes. That meant the bank had to offer guarantees continually and sometimes above-market returns to stay in business. The fact that the borrowed funds were used to fund relatively illiquid long-term real estate assets made the situation even worse.

All hell broke loose in 2008. First to go down was Bear Sterns after its repo lenders and bank counterparties lost confidence in the firm’s ability to repay its debts. As a sign of just how low Bear Sterns had sunk, J.P. Morgan bought the collapsed firm at just 10% of its prior market value. After this, the focus shifted to Lehman Brothers, who had so far avoided large-scale eye-catching losses through a combination of short-term borrowing and corporate restructuring strategies aimed at cutting costs. Lehman’s share price declined sharply by more than 48% following the collapse of Bear Stearns.

For a while, Lehman was able to restore some consumer confidence by announcing better than expected profits. Lehman also watered-down concerns that it was too leveraged by announcing that $4 billion in preferred stock had been raised, and the whole amount could be converted to common stock at a 32% premium to its current value.

The upturn turned out to be short-lived because soon after, news broke alleging that the firm had overvalued its real estate-based assets. At this point, Lehman could no longer cling to market confidence, so critical to the firm’s funding strategy (and therefore its liquidity). As the crisis mounted, many of Lehman’s major counterparties began to demand even more collateral to fund its operations. Others began reducing their exposure, and some institutions flatly refused to do business with the firm. Attempts were made to merge the firm or to sell it to another large bank, but none of them materialized.

In the early hours of 15th September 2008, Lehman was forced to file for bankruptcy, triggering a global financial crisis that saw a virtual meltdown of financial markets.

Lessons Learned

Firms (and investors), in general, should never resort to extreme leverage that far surpasses the capacity to repay. Lehman Brothers took on huge amounts of short-term debt to fund long-term assets, exposing itself to serious liquidity problems. Too much debt means that a firm cannot absorb a major loss.

Lehman’s failure has also highlighted the need to have tougher regulations in the securitization market, particularly because mortgage-backed securities and related instruments such as credit default swaps result in a highly interconnected financial market that is highly vulnerable to a total collapse in case one or two “big names” fail.

Continental Illinois National Bank and Trust Company

The failure of Continental Illinois National Bank and Trust Company in 1984 presents the biggest US liquidity debacle in the banking sector before the 2007/2009 financial crisis. Its subsequent rescue gave rise to the term “too big to fail.”

At its prime, the Chicago-based lender was the seventh-largest bank in the US, with an asset pool of approximately $40 billion. Its roots go back in time to 1910 through a merger, but what especially stood out was the management’s aggressive growth strategy. At the time, banks were not allowed to open branches across state lines. Any bank intending to lend outside its state of origin could only purchase loans from other banks. In line with its fast growth strategy, Continental Illinois took up the task head-on.

The bank developed a network of contacts across the country and positioned itself as a willing buyer of some of the most complex and riskiest loans. Initially, the bank’s strategy seemed to bear fruit, and this served as further evidence for the management that the plan was working. In the 5 years before 1981, the bank’s commercial and industrial lending jumped from USD 5 billion to over USD 14 billion. During that time, the bank’s total assets grew from USD 21.5 billion to USD 45 billion. What the management didn’t know was that things would soon head south.

Continental Illinois had developed an informal business partnership with Oklahoma-based Penn Square Bank. This smaller bank had issued loans to oil and natural gas companies in Oklahoma during the boom of the late 1970s. If a loan was too large for it to service, PennSquare Bank would pass it over to Continental Illinois. Through this arrangement, Continental Illinois purchased $1 billion in speculative energy-related loans. In July 1982, Penn Square Bank collapsed after a large number of borrowers failed to honor their contracts following an unprecedented decline in the price of oil. This put Continental Illinois firmly in the spotlight.

Over the next few months, defaults continued to mount. At the same time, Continental found itself increasingly unable to fund its operations from the US markets. As a result, it began to raise money at much higher rates in foreign wholesale money markets (e.g., Japan).

In the first quarter of 1984, the bank announced that its nonperforming loans had suddenly increased by $400 million to a total of $2.3 billion. This heightened anxiety among investors and the general public; most analysts and industry experts were of the view that it was just a matter of time before Continental Illinois suffered the same fate as Penn Square Bank. By 10th May 1984, the rumors about the bank’s insolvency had spread far and wide, sparking a crippling run. Before the trouble, the bank held $28.3 billion in deposits. Out of fear, depositors trooped into the bank to withdraw their funds, most of them wiping their accounts clean. Foreign investors also turned their back on the bank. In the end, a total of $10.8 billion was withdrawn in the space of a few days.

In the second half of May 1984, Continental Illinois attempted to project stability by maintaining its operations. At the same time, the bank borrowed from the Federal Reserve Bank of Chicago as well as several other big banks across the country in an attempt to cope with the ongoing run. However, the run did not subside, and regulators realized they were now staring at a full-blown liquidity crisis that would spill over to other banks. It is estimated that nearly 2,300 banks had some exposure to Continental Illinois, with a majority holding at least $100,000.

Regulatory authorities eventually stepped in to prevent a domino effect on other banks.

Lessons Learned

• Classifying institutions as “too big to fail” may lead to greater risk-taking. Banks that expect government support may take greater risks, safe in the knowledge that if the going gets tough, neither the bank nor its depositors will absorb most of the loss. The US congress later attempted to limit rescues of “too big to fail” institutions by passing the Federal Deposit Insurance Corporation Improvement Act (1991).
• Reliance on the so-called hot money (short-term loans from the money market) is perilous. A large number of depositors at Continental Illinois were local and international institutional investors. A run on these deposits led to Continental Illinois’s failure.
• Rumors alone can bring down even the biggest of banks. Although there was significant mismanagement and financial strain at Continental Illinois, the end was not nigh, and post-crisis analysis suggests the bank would have carried on despite the losses arising from its relationship with Penn Square Bank. Unfounded rumors that the bank was on the verge of bankruptcy proved to be the stroke that broke the camel’s back.

Northern Rock

 The 2007 failure of mortgage bank Northern Rock in the UK presents a more recent illustration of liquidity risk arising from structural weaknesses in a bank’s business model. The bank’s failure can be traced down to two key things: (I) excessive funding of long-term assets using short-term finance and (II) a sudden loss of market confidence. It was the first run on a UK bank in 140 years.

Northern Rock was a fast-growing lender based in the North East of the United Kingdom. The bank had forged a success story enviable by any other bank within and outside the UK For example, assets had been growing at around 20% per year for several years thanks to specialization in residential mortgages.  The bank continued to expand aggressively in the marketplace into the first quarter of 2007. Things were going so well that the bank had reached a multimillion sponsorship deal with Newcastle United, one of the biggest and most successful football clubs in the country.

The bank’s growth was strongly anchored in the originate-to-distribute business model, where it raised money through securitizing mortgages and selling covered bonds. Unlike many of its peers, the bank did not rely on customer deposits for funding. Instead, it borrowed heavily in the international money markets, particularly within the interbank market.

To mitigate possible weaknesses in its funding strategy, Northern Rock tapped markets across the globe – Europe, the Americas, as well as in the United Kingdom. In early 2007, concerns about mortgage-related assets began to surface among investors. Of significance was the rising number of defaults in the US subprime mortgage market, which eventually spread globally.

When the interbank funding market froze in early August 2007, all of Northern Rock’s global funding channels dried up simultaneously. Interestingly, the bank had announced increased interim dividends just a few weeks prior, after UK regulators approved a Basel II waiver that allowed the bank to adopt so-called “advanced approaches” for calculating credit risk that looked likely to reduce its minimum required regulatory capital.

After getting wind of Northern Rock’s inability to fund itself through the interbank market, UK authorities started exploring discussed a range of rescue alternatives. But these plans leaked, immediately setting in motion a run on deposits between 14th September and 17th September. Calm only (slowly) returned after UK authorities came out publicly to reassure everyone that deposits would be repaid. Eventually, Northern Rock accepted emergency capital injection from the government and then public ownership.

Funding Liquidity Risk: General Lessons

Following the 2007/2009 financial crisis, guidelines by the US Federal Reserve require large banks to put in place liquidity testing programs.  These programs aim to ensure that banks have liquidity and funding strategies that will survive system-wide stress scenarios. To manage funding liquidity risk, a bank should optimize its borrowing sources and their composition.

Trade-offs drive decisions regarding the composition of assets and liabilities as discussed below:

1. The trade-off between funding liquidity and interest rate risk

When funding liabilities have a shorter duration than loan assets, the bank is exposed to less interest rate risk and more funding liquidity risk. But when funding liabilities have a longer duration than loan assets, the bank is exposed to more interest rate risk and less funding liquidity risk.

2. The trade-off between cost and risk mitigation

To mitigate funding liquidity risk in a positively sloped yield curve environment, institutions can increase the maturity of their funding liabilities to push them farther away into the future. However, this will cost more than cheaper shorter-duration funding.

To a limited extent, banks can also mitigate funding liquidity risk by reducing the maturity of their assets. However, this is usually not possible because asset maturity is driven by borrower demand, and reducing the term to maturity may force the bank to settle for a smaller risk premium.

It is also important to have a standby emergency liquidity cushion to ensure that the bank can meet unforeseen commitments. The larger and better quality of the cushion, the lower the risk. However, such a cushion may require the bank to invest in short-term highly liquid assets that will often earn lower returns compared to less longer-term, less liquid assets.

Constructing and Implementing Hedging Strategies

For both financial and non-financial institutions, the development and implementation of effective hedging strategies come with benefits as well as challenges. Nonetheless, certain constants must be present in any strategy that an institution comes up with:

• The function or individual(s) responsible for developing hedging strategies should have access to relevant information and tools, including market data, corporate information, and advanced statistical tools. This will help them to choose the appropriate models to use for both pricing and hedging.
• The firm must decide whether to use static hedging or dynamic hedging

A static hedge is one that does not need constant re-balancing as the price and other characteristics (such as volatility) of the securities it hedges change. A static hedge usually involves the purchase of a hedging instrument that very closely matches the position to be hedged. The hedging instrument is typically held for as long as the underlying position is kept.

A dynamic hedge, on the other hand, involves adjusting the hedge through a series of ongoing trades to continuously (or frequently) calibrate the hedge position to the (changing) underlying exposure. As expected, this strategy demands greater managerial input and may come with higher transaction costs.

• The firm must decide on the time horizon over which a hedging strategy will be implemented. Horizons can be fixed (e.g., quarter-end or year-end) or rolling. No matter the choice of the horizon, performance evaluations, and investment horizons should be aligned.
• Accounting issues and potential tax implications need to be considered when devising a hedging strategy. For example, derivatives come with complex accounting requirements that may be subject to change. A derivative and the corresponding position it is intended to hedge must be perfectly matched (e.g., in terms of dates and quantities) for them to be reported together in operational profit without the need to report an accounting profit or loss.

Tax can have implications on the cash flows of a firm, and therefore getting competent professional guidance on tax matters is critical when developing and implementing a hedging strategy.

Finally, the success of any hedging strategy depends on how effective the implementation process is. This is especially true because markets are in constant movement, and prices keep on changing. As such, what appears to be an attractive hedging opportunity can suddenly become unattractive.

Metallgesellschaft: A Case of Dynamic Hedging Gone Wrong

Metallgesellschaft Refining and Marketing (MGRM) was an American subsidiary of Metallgesellschaft (MG), an international conglomerate with interests in trading, engineering, and chemicals. In 1991, MGRM designed a marketing strategy to insulate from the volatility associated with the price of petroleum.

MGRM committed to selling, at prices fixed in 1992, certain amounts of petroleum every month for up to 10 years. The contracts initially proved to be masterstrokes since they guaranteed a price over the current spot. The profit margin was between $3 and $5. By Sept 1992, MGRM had sold forward contracts amounting to the equivalent of around 160 million barrels. The contracts were attractive, particularly because they gave customers the option to exit if the spot price rose above the fixed price in the contract.

If a customer chose to exit a contract, MGRM would pay in cash one-half of the difference between the futures price and the fixed price times the total volume remaining to be delivered on the contract. A customer had the choice to exercise this option if they did not need the product or in the face of financial difficulties.

In effect, the contracts gave MGRM a short position in long-term forward contracts. To hedge these positions, MGRM turned to long positions in near-term futures using a stack-and-roll hedging strategy. A stack-and-roll hedge involves purchasing futures contracts for a nearby delivery date and, on that date, rolling the position forward by purchasing a fewer number of contracts. The process continues for future delivery dates until the exposure at each maturity date is hedged.

MGRM used short-term futures to hedge because of a lack of alternatives. Besides, the long-term futures contracts available were highly illiquid. As it turned out, MGRM’s open interest in unleaded gasoline contracts was 55 million barrels in the fall of 1993, compared to an average trading volume of 15-30 million barrels per day.

MGRM encountered problems in the timing of cash flows required to maintain the hedge. Over the entire life of the hedge, these cash flows would have canceled out. MG’s problem was a lack of necessary funds needed to maintain its position. The fundamental problem manifested in the form of inadequate funds to mark positions to market and meet margin requirements. In December 1993, MGRM was forced to cash out its positions, incurring a loss of $1.5 billion in the process.

Model Risk

Model risk is the risk of loss resulting from the use of insufficiently accurate models to make decisions when valuing financial securities. Model risk can stem from using an incorrect model, incorrectly specifying a model, and using insufficient data and incorrect estimators.

A major pitfall when using a model to value security is the use of flawed assumptions. For example, a stock pricing model might assume an upward sloping yield curve when it is, in fact, downward sloping or even flat. This type of risk is both common and dangerous and can be among the most difficult risks to detect.

We now look at well-known cases where model risk plays a prominent role:

Wrong Assumptions—The Niederhoffer Put Options

Victor Niederhoffer was a trading guru who had set up a very successful hedge fund in the 1990s. The fund had come up with a strategy it considered low risk: writing uncovered, deep out-of-the-money put options on the S&P 500 index. In other words, the fund sold a very large number of options on the S. & P. index, taking millions of dollars from other traders (in the form of premiums). In exchange, the fund was promising to buy a basket of stocks from them at current prices, if the market ever fell. And because these options were deep OTM, the premium received was relatively smaller than that of at-the-money options sold at the time.

In essence, therefore, Mr. Niederhoffer was betting in favor of a large probability of making a small amount of money, and betting against the small probability of losing a large amount of money.The overriding assumption underlying this strategy was that a one-day market decline of more than 5% would be extremely rare. If market returns were normally distributed; a fall of this magnitude was next to impossible. As it turned out, this assumption was wrong.

On 27th October 1997, the market plummeted 7%. The sharp drop in US equity prices was a spillover effect following a large overnight plummeting of the Hang Seng Index in Asia. Immediately after this, the holders of the many put options Mr. Nierderhoffer had written came calling all at once, intent on exercising their right to sell their stocks to the fund at the pre-crash prices. The fund struggled to meet the demands of all option holders, forcing Mr. Nierderhoffer to wipe out all his cash reserves, including his savings.

Besides the put options, the fund had several outstanding derivatives. Ultimately, the fund was unable to meet over USD 50 million in margin calls. The fund’s brokers had no choice but to liquidate Neiderhoffer’s positions for pennies on the dollar, a move that effectively wiped out the fund’s equity.

What lesson do we learn from Mr. Nierderhoffer’s failed strategy?

The lesson here is that there is nothing like a sure bet in today’s competitive financial markets. A strategy designed to make small profits while betting against a large market move can unravel literally in the blink of an eye, however small the probability of loss is.

Long Term Capital Management and Model Risk: When “Normal” Relationships Breakdown

Long Term Capital Management (LTCM) was a multi-billion hedge fund founded by John Meriwether, a Salomon Brothers trader. The principal shareholders were Nobel-prize-winning economists Myron Scholes and Robert Merton. All the three were experts in derivatives and had carved out a reputation for unrivaled market analysis.

To join the fund, investors were required to part with a whopping $10 million each. Despite this huge outlay, LTCM gave away very little in terms of the nature of its investments. What’s more, investors were not allowed to liquidate their positions during the first three years of their investment. This allowed the fund to lock in the funds in long-term investments. The founders and major shareholders went as far as investing a large portion of their net worth in the fund, which demonstrates just how convinced they were that the fund would succeed.

At first, the fund recorded a stellar performance unheard of before. LTCM boasted annual returns of 42.8 percent in 1995 and 40.8 percent in 1996. This was even after the management set aside about 27% of the proceeds for their compensation and other fees. In 1997, LTCM successfully hedged most of the risk from the Asian currency crisis. That year, the fund earned a return of 17.1% for investors. By 1998, however, the fund was on the brink of bankruptcy as a result of its trading strategies.

Like many hedge funds at the time, LTCM adopted a hedging strategy hinged upon a predictable range of volatility in foreign currencies and bonds. The management believed that the probability of market moves larger than the fund’s hedges was very small. To estimate future volatility, LTCM’s models relied heavily on historical data. However, all historical models are only reliable in the absence of large economic shocks, especially the ones that haven’t been experienced in history. External shocks make correlations that are historically low to increase sharply. And so, it proved to be.

In mid-1998, Russia declared its intention to devalue its currency and followed that up by defaulting on its bonds. That event was beyond the normal range of volatility predicted by LTCM’s models, which means the existing hedges proved insufficient. The US stock market dropped by 20 percent, while European markets fell by 35 percent. Most investors turned to Treasury bonds for refuge, triggering a significant drop in long-term interest rates.

 LTCM’s highly leveraged positions took a strong hit and started to crumble. A multitude of banks and pension funds had heavily invested in LTCM. So, when trouble rocked LTCM, the solvency of all these institutions was at stake. In September, Bear Stearns landed the knock-out punch. The bank managed all of LTCM’s bond and derivatives settlements. Bear Stearns called in half a billion dollars payment, out of fear of losing all its considerable investments.

To save the US banking system, the Federal Reserve Bank of New York convinced 15 banks to save LTCM by pumping in some $3.5 billion.

In summary, LTCM’s crisis could be attributed to the following:

• Overreliance on historical models that did not simulate the occurrence of large economic shocks.
• LTCM’s models further assumed that low-frequency/high-severity events were uncorrelated over time. As it turned out, one economic shock triggered another, such that extremely low probability events were occurring several times per week.
• All of LTCM’s trading strategies were hinged on the assumption that risk premiums and market volatility would ultimately decline. As a result, the firm had failed to diversify its investments wide enough.

Several suggestions have been put forth to avoid a recurrence of a similar crisis:

• There’s a need for large-scale stress testing using not just historical data but also simulated stress scenarios, even if such scenarios haven’t yet played out on the market.
• The initial margin in derivative contracts should always be enforced. In many cases, LTCM had to mark its positions to market, but the initial margin was waived.
• There’s a need to incorporate potential liquidation costs into prices to recognize the possibility of adverse market movements.

The VaR of Hedge Funds

LTCM made heavy use of a Value-at-Risk (VaR) model as part of its risk control. VaR is a measure of the worst-case loss for investment or set of investments, given normal market conditions, over a specific time horizon, and at a given confidence level. It is the maximum expected loss given certain assumptions (to do with volatility) and a level of confidence.

The management at LTCM felt that it had structured the fund’s portfolio such that there was an extremely small chance of the fund’s risk exceeding that of the S&P 500. But the problems encountered, later on, show that hedge funds are not necessarily subject to the same set of assumptions as other firms when calculating regulatory VaR. In particular,

• A 10-day horizon is too short to determine a hedge fund’s VaR. The time horizon for economic capital should be the time it takes to raise new capital, liquidate positions without duress, or the period over which a crisis scenario will unfold.
• Traditional VaR models fail to capture liquidity risk. These models incorrectly assume that liquidity will remain fairly constant throughout all market conditions.
• It is nearly impossible to capture correlation and volatility risks (i.e., the risk that the realized correlations and volatilities significantly deviate from expectations) without stress testing.

Model Risk and Governance—The London Whale

In 2012, J.P. Morgan Chase lost more than 6.2 billion dollars from exposure to a massive credit derivatives portfolio in its London office. The main culprit in the whole saga was one Bruno Iksil, a synthetic credit portfolio trader.  Bruno Iksil was given the title of the “London Whale” by media outlets in the aftermath of the scandal.

Here’s a summary of the London whale debacle:

JPM set up the Chief Investment Office (CIO) with the sole purpose of investing the excess cash (deposits) of the bank. Initially, most of the money was channeled into high-quality securities such as loans, mortgage-backed securities, corporate and sovereign securities. At the height of the 2007/2009 financial crisis, the bank constructed a synthetic credit portfolio (SCP) motivated by the need to protect the bank against adverse credit scenarios such as widening credit spreads. The bank cited the need to make financial bets that would offset risks the bank took elsewhere, such as by loaning money to homeowners or trade engagements with other banks that could fail. This begs the question: what exactly was a synthetic credit portfolio?

The bank’s synthetic credit portfolio (SCP) was essentially a basket of credit default swaps featured in standardized credit default swap indices. The bank took both buyer and seller positions in these swaps. As a protection buyer (short risk position holder), the bank would pay premiums and, in turn, receive the promise of compensation in the event of default. As a protection seller (long risk position holder), the bank would receive premiums and, in turn, promise to compensate the buyer in the event of default.

In the first few years, the SCP performed well. In 2009, for example, the SCP netted the CIO about $1 billion. At that point, the notional size of the SCP was $4 billion. By 2011, the notional size of the SCP had risen to about $51 billion – a more than tenfold increase. For a while, the SCP continued to perform well, with 2011 trading (bets) producing a gain of $400 million.

In December 2011, the management at JPM directed the CIO to reduce the exposure of the SCP and its risk-weighted assets following a more positive outlook of the economy. By so doing, the bank wanted to reduce its regulatory requirements. To achieve this, the CIO would have had to unwind SCP positions by selling them off. In the CIO’s estimates, such a move would have led to an estimated loss of $500 million – in the form of loss of premiums and trade execution costs. The CIO decided not to take that route and instead came up with a different strategy – one that would prove “fatal” in financial terms.

The CIO launched a trading strategy that focused on purchasing additional long credit derivatives to offset its short derivatives positions and lower the CIO’s RWA. That strategy ended up increasing the portfolio’s size, risk, and RWA. Besides, the strategy took the portfolio into a net long position, thereby eliminating the hedging protections the SCP was originally supposed to provide. Notably, the strategy’s assumptions about the market environment and correlation between positions did not play out as expected. What followed were trading losses that continued accumulating with each passing trading day.

As losses mounted, CIO traders tried to defend their existing positions by further growing their portfolios with huge trades to support market prices. But the markets proved rather illiquid, and CIO traders became significant market movers in these securities. That reduced their ability to exit the markets without suffering losses in the process.

Operational Risk

In the first three months of 2012, the number of days reporting losses exceeded the number of days reporting profits. In an attempt to conceal these losses, the CIO came up with a new valuation system. The CIO had hitherto valued credit derivatives by marking them at or near the midpoint price in the daily range of prices (bid-ask spread) offered in the market. By using midpoint values, the resulting prices were considered to be the “most representative of fair value.”

The new valuation system set marks that were at significant variance to the midpoints of dealer quotes in the market. The end goal was to paint a rosier picture of the outstanding derivative positions and, therefore, a better than the actual marking-to-market picture on the books. In particular, the new system resulted in smaller losses being reported in the daily profit/loss reports.
Despite the new valuation system, the CIO continued to make losses. As of 16th March 2012, the SCP had reported year-to-date losses of $161 million. If the old system making use of midpoint prices had been used, those losses would have been $593 million – a whopping $432 million more.

Absence of Regulatory and Corporate Oversight

The London whale case exposed a culture of poor regulatory oversight in which risk limits were repeatedly breached, risk metrics disregarded, and risk models manipulated without any concrete steps being taken by the management to correct these anomalies. Since the CIO wasn’t a client-facing unit of the bank, it was not subject to the same regulatory scrutiny as other portfolios.

Besides, SCP traders did not have to prepare daily reports for senior management. What’s more, risk committee meetings were rare, and in the few instances the committee happened to meet, there appeared to be no specific charter, and only CIO personnel would attend.

In the absence of oversight, CIO traders were able to engage in speculative and risky trades that were not in line with the CIO’s traditional investment strategy, which had hitherto prioritized long-term investments, limiting the use of credit derivatives to hedging purposes only.

Fudged VaR Models

CIO traders, risk personnel, and quantitative analysts frequently attacked the accuracy of the risk metrics used, including the VaR. The riskiness of credit derivatives was downplayed, and new risk measurement and models were proposed to lower risk results for the SCP.

Traders argued that the existing models were too conservative and therefore overstated risk, resulting in limit breaches. Senior management approved the migration to a new VaR model that had been researched and built by CIO traders themselves. Crucially, the bank did not obtain approval from the Office of the Comptroller of Currency. That means there had been little room for checks and balances in the process of developing the model.

The updated VaR model resulted in risk numbers that were 50% lower than prior numbers, paving the way for even more speculative trading and high-risk strategies. Months later, the bank’s model Risk and Development Office determined that the model had mathematical and operational flaws. Some of the issues that came to light include:

• EXCEL spreadsheets used required manual updates
• There were coding errors in the calculation of hazard rates and correlation estimates
• Unrealistically low volatility was attached to illiquid securities, built upon the assumption that prices for days on which trades did not occur would be the same as the price when last traded.
• Instead of using the Gaussian Copula model in the built-in analytics suite as required under Basel 2.5, the model used a Uniform Rate option.

On 10th May, the bank backtracked, revoking the new VaR model due to the above inaccuracies, and the prior model was immediately reinstated.

Rogue Trading and Misleading Reporting at Barings Bank

The Barings case revolves around Nick Leeson, a British trader. Barings PLC of London was the oldest merchant bank in England. After making a reputation for hard work and a unique understanding of the market while serving in other posts outside Barings bank, Leeson was appointed the general manager and head trader of Barings Futures Singapore. In his new post, Nick Leeson quickly became a renowned operator of the derivative product’s market on the SIMEX (Singapore International Monetary Exchange).

As a reward from his bosses, Leeson was given some “discretion” in his trades: He could place orders on his own (speculative or “proprietary” trading). He was also in charge of accounting and settlements, and there was no direct oversight over his trading book. This allowed him to create a dummy account – 88888 – where he’d dump all losing trades. As far as the London office was concerned, Leeson was reporting profits after profits on his trades. His seniors never questioned his constant requests for Margin calls

Leeson took on huge positions as the market seemed to “go his way.” He also sold options, taking-on huge market risk, which stems from unexpected major events that, while not directly related to markets, can adversely affect markets. He would also record trades that were never executed on SIMEX.

On 16^th January 1995, Leeson placed a short straddle on the Singapore Stock Exchange and Nikkei Stock Exchange. That means he simultaneously sold put options (conferring a right to sell) and call options (a right to buy) on Nikkei-225 futures. Such a strategy is aimed at making profits in the form of premiums received and works only if the market proves less volatile than the option prices predicted.

Mr. Leeson is said to have sold up to 40,000 such option contracts and earned the bank an estimated $150m. His underlying conviction was that the Nikkei would stay in the 18,500-19,500 range, and even in the worst-case scenario, it would not drop below 19 000 points.  In an astonishing turn of events soon afterward, a huge earthquake hit Japan, sending its financial markets tumbling. In the space of a week, the Nikkei had lost more than 7%.

Nick Leeson took a futures position valued at $7 billion in Japanese equities and interest rates linked to the variation of Nikkei. He was “long” on Nikkei. In the three days following the earthquake, he bought more than 20 000 futures, each worth $180 000.

Unfortunately, the Nikkei never recovered. By the time his dealings came to light, Barings had lost approx. $1.25 billion. The bank could not withstand this loss and ultimately filed for bankruptcy. In summary, Leeson’s phony transactions went unchecked for long periods because of the following reasons:

• There was little management oversight of the settlement process. Of note, Leeson reported to multiple managers. There wasn’t a clear demarcation of roles and responsibilities.
• Apart from being Baring’s Floor manager, Leeson was in charge of settlement operations. This allowed him to influence back-office employees to hide his trading losses from the London office.

To a smaller extent, some blame can be apportioned to the Singapore Stock Exchange and the Nikkei Stock Exchange. The two exchanges failed to flag the unusually large positions racked up by Barings bank. It has been reported that the exchanges did ask for information, but their concerns were watered down, with the bank forwarding a few fictitious client names. The exchanges could also have sensed danger if there had been an information-sharing mechanism between them.

Lessons Learned

• Reporting and monitoring of positions and risks (i.e., back-office operations) must be separated from trading (i.e., front-office operations)
• Outsized or strangely consistent profits should be independently investigated and rigorously monitored to verify that they are real, generated following the firm’s policies and procedures, and not the result of nefarious or unacceptably risky activities.
• It is the responsibility of risk managers to analyze reported business profits and determine if they seem logical in light of the positions held.

It is, however, important to note that Barings’ downfall could have been averted under regulations that were implemented by the Basel Committee just a few years later. For starters, the committee set capital adequacy requirements and set limits on concentration risk. Under the 1996 amendment, banks must report risks that exceed 10% of their capital and cannot take positions that exceed 25% of their capital. Had these rules been in effect in 1994, Barings would have been prohibited from racking up such large positions.

Financial Engineering

Financial engineering is all about the creation of complex financial structures that meet the needs of the investor. It involves the use of derivatives such as forwards, swaps, and options. Derivatives allow investors and institutions to break apart (i.e., segment) risks. Conversely, derivatives can be used to manage risks on a joint basis.

For illustration, consider a UK fund manager holding a bond denominated in US dollars. The manager is exposed to interest rate risk in the US fixed income market and the currency risk from changes in the euro/dollar exchange rate. In these circumstances, there are two options for the manager:

• Use a currency swap which effectively hedges both risks
• Hedge the foreign exchange rate exposure separately through a currency forward or option, and then hedge the interest rate risk through a quanto swap. Under the quanto swap, they would receive the coupon in euros at a pre-arranged rate and pay the UK LIBOR floating rate.

In practice, financial engineering is often exploited by investors in speculative ways in an attempt to earn immediate portfolio returns. However, such speculative tendencies require the taking of more risk in some form or the other. This risk may come in the form of an unlikely but potentially very severe future loss. Too often, the embedded risk is not fully understood by firms entering into complex derivatives.

The Risk of Complex Derivatives at Bankers Trust (BT)

Procter & Gamble (P&G) and Gibson Greetings sought the assistance of Bankers Trust (BT) in an attempt to reduce funding costs. BT used derivatives trades which promised P&G and GG a high probability, a small reduction in funding costs in exchange for a low probability, large loss. As it turned out, derivative trades only churned out significant losses for both P&G and GG.

BT’s derivatives were designed to be intentionally complex to stop P&G and GG from understanding their risks and overall implications. The trades were quite differentiated in form and structure, making them incomparable to derivative trades of other companies. BT duped P&G and GG into thinking that the trades were tailored to meet their individual needs. In the end, P&G and GG came to the painful realization that they had been misled after taking in huge losses. The two sued BT.

During the suit, BT’s taped conversations between its marketers and customers played a key role. The tapes exposed just the tools BT staff used to fool customers, particularly through the use of complex terminology and pricing structures. In some tapes, BT staff could be heard openly bragging about their unethical behavior.

The scandal dealt a huge blow to BT’s reputation and forced senior managers to resign, including the CEO. Eventually, BT was acquired by Deutsche Bank and dismantled.

Excess Leverage and Complex Financial Instruments at Orange County

The Orange County case illustrates how complex financial products characterized by large amounts of leverage can create significant losses. In December 1994, the use of complex structured products by Orange County treasurer, Robert Citron, resulted in a loss of $1.5 billion. This was the largest loss ever recorded by a local government investment pool. At the root cause of the downfall was Robert Citron’s decision to borrow heavily in the repo market.

Repos allow investors to finance a significant portion of their investments with borrowed money (i.e., leverage). But the use of leverage has a multiplicative effect on the profit or loss on any position; even a small change in market prices can have a significant impact on the investor.

Robert Citron had been entrusted with a $7.5 billion portfolio belonging to county schools, cities, districts, and the county itself. To many investors, Citron was a financial management guru who had, for a long time, managed to deliver consistently higher returns. Indeed, his returns were about 2% higher than the comparable State pool.

The fund had only USD 7.7 billion in equity, but Citron managed to borrow USD 12.9 billion through the repo market, creating a USD 20.6 billion portfolio. Citron used the funds to purchase complex inverse floating-rate notes. But here’s the interesting bit; the coupon payments of inverse floating-rate notes decline when interest rates rise as opposed to conventional floaters, whose payments increase in such a situation. In effect, therefore, Mr. Citron was betting in favor of interest rates falling or generally staying low.

For a while, interest rates went down, and his bet seemed to be paying off. It was in these circumstances when Citron was able to record higher than average returns. However, throughout 1994, the Federal Reserve announced a hike in interest rates by 250-basis points. As expected in this scenario, the increase in interest rates reduced the value of Citron’s portfolio substantially, generating a loss of USD 1.5 billion by December 1994. At the same time, Citron struggled to roll over maturing repo agreements, with most lenders tabling stringent demands, including the provision of more collateral before giving a single coin. Ultimately, Orange County was forced to file for bankruptcy. Citron later admitted he understood neither the position he took nor the risk exposure of the fund.

In summary, therefore, this debacle was caused by two key things:

• Excessive use of leverage
• A highly risky interest rate bet that did not take into account the Federal Reserve’s possible changes in monetary policy.

Lessons Learned

• Every firm needs to have more than a basic understanding of the risks that are inherent in their business models. Senior management then needs to take these risks into account when coming up with a risk management framework.
• Senior management needs to stick to the portfolio strategy communicated to stakeholders at the onset. Robust policies and risk measures should be adopted as specified in the risk appetite statement and the risk management framework.
• Management, and boards, should endeavor to establish areas of the business where risks may hide and also seek to establish the circumstances which can result in a loss.

The Case of Investing in AAA Tranches of Subprime CDOs: Sachsen

Subprime securities were some of the most popular assets in the run-up to the 2007–2009 financial crisis. But while subprime securities offered an attractive risk premium, they also required understanding and pricing expertise. European banks were some of the biggest buyers of US subprime securities. Among these institutions were publicly-owned banks in Germany called the Landesbanken.

The Landesbanken traditionally specialized in lending to regional small- and medium-sized companies. However, in the run-up to the crisis, a thriving industry pushed some of the banks to open overseas branches and develop investment banking businesses. One of the most notorious examples was the Landesbank Girozentrale Sachsen –a Leipzig-based bank.

Sachsen opened a network of units (called conduits), which is used to raise money through the sale of short-term debt. The money would subsequently be invested in the subprime securities market. Sachsen opened a branch in Dublin tasked with setting up the units to hold large volumes of highly rated US mortgage-backed securities. While these units were technically off the parent bank’s balance sheet, they benefited from the guarantee of Sachsen itself. That means Sachsen would promise to lend the units extra money if they ever needed it.

In the run-up to the crisis, the size of Sachsen’s off-the-balance-sheet operation was simply too large compared to Sachsen’s balance sheet.

When the subprime crisis struck in 2007, Sachsen’s attempts to rescue the units it had set up ended up wiping out the bank’s capital. Eventually, the bank had to be sold to Landesbank Baden-Württemberg (LBBW).

Reputation Risk

Two key things determine a firm’s reputation:

• The ability and willingness to fulfill its promises to counterparties and creditors
• The ability to deal fairly and follow ethical practices

In recent years, however, firms have become increasingly concerned about their reputation due to the rapid growth of public and social networks. A rumor can spread like a bushfire and cause untold reputational damage in just a few hours. As a result, companies are under growing pressure to demonstrate their commitment to environmental, social, and governance-related best practices. The reputational damage caused by unethical conduct, whether rumored or real, can be very severe.

Volkswagen Emission Cheating Scandal

The Volkswagen emissions scandal, also known as Dieselgate or Emissionsgate, burst onto the public scene in September 2015, but its origin can be traced back to 2009.

In model years 2009 through 2015, the carmaker had been installing in its diesel engines software that had been intentionally programmed to reduce emissions during testing. This meant that the cars would pass emission tests with “flying colors” only to emit up to 40 times more Nitrogen Oxide during real-world driving. This software had been installed in over ten million cars, most of which had already been shipped to various dealers and direct consumers around the world.

In 2014, engineers in the United States carried out live road tests, and that’s when the whole scheme was unearthed. Reached out for comment, Volkswagen executives in Germany and the United States formally acknowledged the deception on a conference call with officials from the United States Environmental Protection Agency (EPA). As soon as irrefutable evidence had been gathered, the EPA made the information public.

What followed was untold damage to the Volkswagen brand. The company’s share price fell by over a third, and the firm faced billions of dollars in potential fines and penalties. Multiple parties filed lawsuits, most of them emphasizing the health hazards faced by consumers. Volkswagen’s reputation took a severe hit around the world, with most of the damage happening in the US. The impact was so great that the German government expressed fears that the scandal would diminish the value of the imprimatur “Made in Germany.”

Corporate Governance: Enron

Enron was formed in 1985 following a merger of InterNorth and Houston Natural Gas. The firm was originally involved in the regulated transportation of natural gas. But following the deregulation of energy markets, the firm lost the exclusive rights to its pipelines. As a result, the management was forced back to the drawing board to devise new ways to remain in business. The management came up with an innovative business strategy that involved buying gas from various suppliers and selling it to a network of consumers at guaranteed amounts and prices. In return for assuming the associated risks, Enron charged fees for these transactions. As part of this process, Enron created a market for energy derivatives where one had not previously existed.

The new strategy turned out to be a huge success; so much so that up until late 2001, nearly all observers — including Wall Street professionals – spoke highly of this new strategy and considered it a business masterstroke. And true to their assessment, Enron’s financial position changed dramatically. The firm’s reported annual revenues grew from under $10 billion in the early 1990s to $139 billion in 2001, a transformation that firmly placed the firm among the top five Fortune 500 companies. Enron’s shares peaked at USD 90.56 in August 2000. That year, the firm had more than 20,000 employees on its payroll and revenues of nearly USD 101 billion.

Interestingly, Enron became a major proponent of the deregulation of the energy market. In the firm’s assessment, deregulation would come with greater flexibility to pursue its business model. Top managers at the firm took actions that prioritized profit over consumer welfare. For example, the firm was a prominent player in the 2000-2001 California electricity crisis. Enron created artificial power shortages enabling it to raise power prices by up to 2,000%. The crisis ultimately forced the state’s Democratic governor, Gray Davis, out of office with Arnold Schwarzenegger eventually coming in as his replacement. Meanwhile, the shortages helped Enron to make USD 1.6 billion.

Despite these shady deals, Enron still went down in December 2001, but why?

Thanks to its large-scale involvement in energy markets, Enron traded large amounts of oil futures contracts. However, the contracts didn’t involve any stake in oil price movements.Instead, Enron was collecting cash by selling oil for future delivery, promising to buy back the delivered oil at a fixed price.

As a result, no oil was delivered. This was a strategy of a loan where Enron paid cash at a later date to receive cash at the beginning of the contract. This way, the company did not have to reveal these transactions as loans in financial statements. The result was ill financial health disguised in impressive financial statements that didn’t portray the real financial situation.

JPMorgan Chase and Citigroup were the main counterparties in Enron’s trades. When the scandal blew open, the two had to pay $126 million in fines for assisting and abetting fraud against Enron shareholders.

Key Points

• Many in Enron’s senior management went against the key tenets underlying the principal-agent relationship. Most managers acted in their self-interest at the expense of shareholders. After the scandal came to light, for example, Enron chairman and CEO Ken Lay was charged with “falsifying Enron’s publicly reported financial results and making false and misleading public representations about Enron’s business performance and financial condition.”
• Enron’s board failed to fulfill its fiduciary duties to shareholders. For example, the board allowed the chief financial officer to be the sole manager of a private equity fund that did business with Enron. Private equity lacked economic substance
• Enron outsourced its audit function to Arthur Andersen, formerly one of the Big Five accounting firms. Andersen either failed to catch or explicitly approved many of the fraudulent accounting practices that led to Enron’s collapse. After the scandal came to light, the Securities and Exchange Commission (SEC) was forced Andersen to surrender its accounting licenses. This was effectively a death sentence for the firm.

Cyber Risk

The rapid rise of the internet as the preferred method to transact and share information has exposed individuals and institutions to cyber risk. There are cases where bank systems have been hacked, and ATMs breached, leading to not just loss of cash but also exposure and theft of client information. Such information can be used to inflict serious damage to clients and institutions.

As a result, financial institutions have had to spend billions of dollars every year to boost the security of their systems. The goal is to rebuff both external attacks as well as internal attacks perpetrated by individuals within the institution. Threats to the banking system from cyber-attacks are also a major concern to international regulatory bodies, such as the Bank for International Settlements (BIS) and the International Monetary Fund (IMF), as well as to local regulators.

The SWIFT Case

The Society for Worldwide Interbank Financial Telecommunication, also known as SWIFT, is a secure electronic platform used to transfer funds among more than 11,000 financial institutions worldwide. Thanks to SWIFT, transactions that would take days are completed in a matter of seconds. For the longest time, SWIFT was considered a super-secure system nearly impossible to hack. But that notion changed in April 2016.

An article published in the New York Times revealed that hackers had used the

SWIFT network to steal USD 81 million from Bangladesh Bank (the central bank

of Bangladesh). The money was transferred through the SWIFT network to accounts in the Philippines controlled by hackers.

The hackers unleashed malware that sent unauthorized messages instructing the transfer of funds to the account. The attack had been planned so meticulously that details of the transfers were immediately erased from the system. Confirmatory messages sent to designated individuals were not sent.

Though the SWIFT network was itself not compromised, the management moved with speed to reassure clients that weaknesses in the system would no longer be tolerated. A Customer Security Program (CSP) was also set up, consisting of mandatory security controls, information-sharing mechanisms, and sophisticated security features. As of December 2018, 94% of clients complied with CSP requirements.

Question

During a seminar, a risk expert is discussing the implications of risk management lapses leading to significant financial setbacks. The discussion highlights the insights gained from analyzing notable financial catastrophes both within the US and internationally. From the instances mentioned, which accurately delineates a lesson drawn from the specific case?

A. The Northern Rock incident underscores the significance of implementing a robust cybersecurity protocol.

B. The LTCM debacle underlines the cruciality of adhering to regulatory capital norms.

C. The Orange County fiasco accentuates the necessity of comprehensively grasping intricate derivative contracts prior to committing to them.

D. The London Whale situation underscores the need to discern that correlations might intensify abruptly amidst a worldwide financial downturn.

Solution

The correct answer is C.

The downfall of Orange County occurred when Robert Citron heavily wagered on inverse floating swaps, a complexity that wasn’t entirely grasped by the county’s governing body. This strategy became catastrophic when interest rates surged. Subsequently, Citron conceded his limited understanding of the stance he adopted and the associated risk exposure.

A is incorrect. This is reminiscent of the SWIFT scenario. The downfall of Northern Rock was due to a banking panic that partly arose from an excessive dependence on repurchase agreements, leading to liquidity risk when such financing avenues evaporated.

B is incorrect. The predicament with LTCM revolved around faulty correlation modeling and subpar stress testing. Being a hedge fund, LTCM wasn’t subjected to the prevailing regulatory capital norms during that era.

D is incorrect. While deficient correlation modeling was central to issues like the subprime crisis and LTCM, the London Whale event, which unraveled in 2012 post the crisis, primarily revolved around inadequate corporate governance concerning risk concentration limits, position constraints, and VaR modeling techniques.