Market risk: Known and unknowns

November 30, 2010 06:00 PM

Anatomize trading to its very core and therein lies the act of risk-taking. The saying goes that without risk, there is no reward. Thus, risk equates to opportunity and opportunity involves risk. It logically follows then that risk management is the determinate that separates skilled traders from the unskilled.

But what constitutes risk in an ever-changing market environment is not static. In addition to traditional risk considerations, heightened regulations and an evolving market structure driven by technological advancements have conspired to create a brave new world of known unknowns. New risk factors are constantly emerging and must be worked into risk management models. But where does a trader begin?

Central to the discussion of risk is decision theory and choice under uncertainty. It is from such ideas that the concept of unknown unknowns evolved, a term in use by the U.S. military establishment long before Defense Secretary Donald Rumsfeld waxed poetically that, "There are known knowns. There are things we know that we know. There are known unknowns. That is to say, there are things that we now know we don’t know. But there are also unknown unknowns. There are things we do not know we don’t know."

Things we know we know

A common mistake for those new to futures trading is to over-emphasize the potential for outsized gains (or losses) because of leverage, rather than the quality of returns relative to risk taken. For example, if we were to compare two traders, one with an annual return of 40% and a second who returned 10%, many would automatically assume that the first performed better. This is not necessarily true.

In futures trading, margin is a good faith deposit which by design requires a small amount of money to control the face value of a futures contract (see "Risk vs. return," below). In practice, experienced traders capitalize their trading accounts with more than the minimum margin requirement, but also less than the amount that would be needed to fully fund their nominal position exposure. The average amount of capital that is utilized to fund a position relative to the account’s liquidating value is referred to as margin-to-equity.


In our example above, if the average margin-to-equity for the first trader is 60% and 15% for the second, and assuming all other factors are alike except for the number of contracts traded, the risk-adjusted performance for both traders is in fact comparable. The key difference in this case is that the first trader is trading four contracts vs. one contract for the second. Albeit, calculating performance relative to margin utilized is a simplistic method for standardizing risk. Sharpe ratio, value-at-risk, rank correlation analysis and other measures provide better insight into quantifying risk-adjusted returns.

Another nontrivial issue facing traders is developing and maintaing the right psychological frame of mind. Trading is a discipline in response to the emotions of fear and greed. Relying on experience and intuition in response to risk events characterizes a discretionary approach, whereas systematic trading is based on a predefined decision-making framework. While seemingly disparate, these styles actually have shared qualities. Discretionary traders may not have a formalized decision-making process, but successful traders of this type embrace many of the same risk management tenets utilized by systems traders. Central is developing a replicable strategy that attempts to avoid trades with worse-than-expected return potential, while seeking trades that have better-than-expected return potential. As important are self-control and strict money management principles (see "Decision tree," below).


System traders, whether they recognize it or not, employ discretion when modeling markets and developing assumptions that inform their systematic approach. Risk management decisions include the kind and number of markets traded keeping in mind market correlation, how to best adjust trade size in order to maintain constant risk exposure, what time horizon is used for price data input, and level of accepted return variability. In addition, knowledgeable systems traders are concerned about the risk of curve fitting, and recognize that avoiding negative back-testing results implies opportunity cost.

Things we know we don’t know

Investors and traders operate in a world of constraints referred to as market structure. The naïve assumption is that we generally operate in free markets where supply and demand determine price, but economic history reveals that free markets are not the norm, but the exception. High commodity prices in particular have been cause for government intervention.

For example, in 1933 President Franklin Roosevelt prohibited ownership of gold by U.S. citizens, and it wasn’t until 1974 that gold futures were made legal in the U.S. Conversely, the spike in commodity prices around this time led Congress to become dissatisfied with regulatory oversight, contributing to the establishment of the Commodity Futures Trading Commission (CFTC). Not soon after, the CFTC imposed "liquidation-only" orders in response to the 1980 silver market corner by the Hunt brothers. More recently, there was talk of raising margin requirements when oil neared its 2008 peak, and a transaction tax has been debated in the Unitest States and United Kingdom since the credit crisis.

What becomes clear is that excessive speculation, an "eye of the beholder" standard not "black letter law," provides political will as cover for government manipulation. An interesting exception is the Commodity Futures Modernization Act of 2000 (CFMA), which was predicated on the impetus of deregulation in the belief that markets are efficient and effectively self-regulating.

In an ironic twist of ‘be careful what you wish for’ leading to a series of unintended consequences, the CFMA set off an explosion in over-the-counter derivatives trading and new products such as securitized futures funds commonly known as commodity ETFs. At the same time, various loopholes built into the CFMA, which was a critical factor in allowing innovation to flourish, also provided an asymmetric advantage for those positioned to benefit from reduced transparency and regulatory arbitrage.

Various theorists argue that the securitization of commodities changed market behavior by opening up the floodgates to an increased flow of funds into commodities, thereby altering the commodity markets’ traditional price discovery mechanism. As a result, there is an increased risk of positive feedback loops and speculative bubbles that results from imitative behavior and herding. Astute traders sense this and keep a close eye on fund activity, understanding that flow of funds potentially can override fundamentals.

Another issue related to market structure risk is so-called dark pools of liquidity. When trading futures, time-honored rules prohibited pre-execution communications in connection with pit transactions executed on the trading floor. However, CME Group rules now permit pre-execution communications in electronic exchanges for the purpose of discerning interest in a transaction prior to the exposure of the order to the market. Combine this with exchange of futures for futures (EFF), and there is the risk of fragmented liquidity in the futures market not unlike that which currently exists in the equity markets.

A related phenomenon is the rise of algorithmic and high frequency trading (HFT) that has caused a growing chorus of complaints about abnormal pricing, especially after the May 6 "flash crash." While HFT traders defend their practice as one that provides a market-making function and liquidity, detractors argue that HFT engages in systemic penny-jumping. Further, it is said that HFT bids-asks do not represent real buy or sell orders, but are designed not only to detect asymmetries in order flow, but to game the system into adverse selection. In another era, such risks would be described as front-running and churning.

Things we don’t know we don’t know

The rise of systematic trading is rooted in a combination of increased computing power and a belief that it should be possible to build predictive models of economic systems. The problem, as pointed out by Muth (1961) in a paper on rational expectations, is that to explain how expectations are formed, he advanced the hypothesis that they are the same as the predictions of the relevant economic theory.

This seemingly bizarre statement, which turns conventional logic on itself, actually relates to the problem facing investors and traders in light of Federal Reserve Chairman Bernanke’s recent decision to go ahead with another round of quantitative easing. Central to the conundrum is the debate between deflation vs. inflation, and the market’s predisposition to buy on the rumor (based on expectations) and sell on the fact. At risk is the credibility of the Federal Reserve as steward of U.S. monetary policy.

So what would happen if the central bank of the world’s de facto reserve currency lost all credibility? That is a question for another time but what is painfully clear is that the Fed (and you can add the Treasury and Congress) is willing and able to take actions that are manipulative. The hypocrisy is that often these actions are taken in the guise of preventing manipulation but in truth are factors that influence market disequilibrium. Thus, when it comes to unknown unknowns, we may have met the enemy and he is us. The bottom line is that these political realities must be considered when managing risk.

Michael "Mack" Frankfurter is Managing Director, Operations of Cervino Capital Management LLC and has worked with many successful futures traders.

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