The derivative industry deals with products in which one party gains what the other party loses. These are situations known as zero sum games. Hence there are bound to be large winners and large losers. The size of the gains and losses are magnified by the leverage and overbetting, leading invariably to large losses when a bad scenario occurs. This industry is now a staggering $ 370 trillion of which $ 262 trillion is in interest rate derivatives, according to the Bank for International Settlements in Basel; see Tett (2006).
Figlewski (1994) attempted to categorize derivative disasters and this chapter discusses and expands on that:
In an ordinary hedge, one loses money on one side of the transaction in an effort to reduce risk. The correct way to evaluate the performance of a hedge is to consider all aspects of the transaction. In sophisticated hedges where one delta hedges but is a net seller of options, there is volatility (gamma) risk which could lead to losses if there is a large price move up or down. Also accounting problems can lead to losses if gains and losses on all sides of a derivatives hedge are recorded in the firm's financial statements at the same time.
Credit risk is the fastest growing area of derivatives and a common hedge fund strategy is to be short overpriced credit default derivatives. There are lots of ways to lose on these shorts if they are not hedged properly, even if they have an edge.
Derivatives have many purposes including transferring risk from those who do not wish it (hedgers) to those who do (speculators). Speculators who take naked unhedged positions take the purest bet and win or lose monies related to the size of the move of the underlying security. Bets on currencies, interest rates, bonds, or stock market moves are leading examples.
Human agency problems frequently lead to larger losses for traders who are holding losing positions that if cashed out would lead to lost jobs or bonus. Some traders will increase exposure exactly when they should reduce it in the hopes that a market turnaround will allow them to cash out with a small gain before their superiors find out about the true situation and force them to liquidate. Since the job or bonus may have already been lost, the trader's interests are in conflict with objectives of the firm and huge losses may occur. Writing options, which typically gain small profits most of the time but can lead to large losses, is a common vehicle for this problem because the size of the position accelerates quickly as the underlying security moves in the wrong direction. Since trades between large institutions frequently are not collateralized mark to market large paper losses can accumulate without visible signs such as a margin call. Nick Leeson's loss betting on short puts and calls on the Nikkei is one of many such examples. The Kobe earthquake was the bad scenario that bankrupted Barings.
A proper accounting of trading success evaluates all gains and losses so that the extent of some current loss is weighed against previous gains. Derivative losses should also be compared to losses on underlying securities. For example, from January 3 to June 30, 1994, the 30-year T-bonds fell 13.6 %. Hence holders of bonds lost considerable sums as well since interest rates quickly rose significantly.
Gap moves through stops are one example of forced liquidation. Portfolio insurance strategies based on selling futures during the 18 October 1987 stock market crash were unable to keep up with the rapidly declining market whose futures fell 29 % that day. Forced liquidation due to margin problems is made more difficult when others have similar positions and predicaments. The August 1998 problems of Long Term Capital Management in bond and other markets were more difficult because others had followed their lead with similar positions. When trouble arose, buyers were scarce and sellers were everywhere. Another example is Metallgellschaft's crude oil futures hedging losses of over $ 1.3 billion. They had long term contracts to supply oil at fixed prices for several years. These commitments were hedged with long oil futures. But when spot oil prices fell rapidly, the contracts to sell oil at high prices rose in value but did not provide current cash to cover the mark to the market futures losses. A management error led to the unwinding of the hedge near the bottom of the oil market and the disaster.
Potential problems are greater in illiquid markets. Such positions are typically long term and liquidation must be done matching sales with available buyers. Hence, forced liquidation can lead to large bid-ask spreads. Askin Capital's failure in the bond market in 1994 was exacerbated because they held very sophisticated securities which were only traded by very few counterparties. Once they learned of Askin's liquidity problems and weak bargaining position, they lowered their bids even more and were then able to gain large liquidity premiums.
As derivative securities have become more complex, so has their full understanding. Our Nikkei put warrant trade (discussed in Chapter 9) was successful because we did a careful analysis to fairly price the securities. In many cases, losses are the result of trading in high-risk financial instruments by unsophisticated investors. Lawsuits have arisen brought by such investors attempting to recover some of their losses with claims that they were misled or not properly briefed on the risks of the positions taken. Since the general public and thus judges and juries find derivatives confusing and risky, even when they are used to reduce risk, such cases or their threat may be successful.
A great risk exposure is the extreme scenario which often investors assume has zero probability when in fact they have low but positive probability. Investors are frequently unprepared for interest rate, currency or stock price changes so large and so fast that they are considered to be impossible to occur. The move of some bond interest rate spreads from 3 % a year earlier to 17 % in August/September 1998 led even the savvy investor and very sophisticated Long Term Capital Management researchers and traders down this road. They had done extensive stress testing which failed as the extreme events such as the August 1998 Russian default had both the extreme low probability event plus changing correlations. Scenario dependent correlation matrices rather then simulations around the past correlations are suggested. This is implemented, for example, in the Innovest pension plan model which does not involve levered derivative positions (see Chapter 21). The key for staying out of trouble, especially with highly levered positions, is to fully consider the possible futures and have enough capital or access to capital to weather bad scenario storms so that any required liquidation can be done in an orderly manner or preferably prevented.
Figlewski (1994) mentions that the risk in mortgage backed securities is especially difficult to understand. Interest only (IO) securities, which provide only a share of the interest as part of the underlying mortgage pool's payment stream are a good example. When interest rates rise, IO's rise since payments are reduced and the stream of interest payments is larger. But when rates rise sharply, the IO falls in value like other fixed-income instruments because the future interest payments are more heavily discounted. This signal of changing interest rate exposure was one of the difficulties in Askin's losses in 1994. Similarly the sign change between stocks and bonds during stock market crashes as in 2000 to 2003 has caused other similar losses. Scenario dependent matrices are especially useful and needed in such situations. 6. Forgetting that high returns involve high risk
If investors seek high returns, then they will usually have some large losses. The Kelly criterion strategy and its variants provide a theory to achieve very high long term returns but large losses will also occur. These losses are magnified with derivative securities and especially with large derivative positions relative to the investor's available capital.
Stochastic programming models provide a good way to try to avoid problems 1-6 by carefully modeling the situation at hand and considering the possible economic futures in an organized way.
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