Critical Evaluation
Chincarini's analysis of the Amaranth collapse contains five key lessons for portfolio managers and regulators with respect to managing market risk. Of the ones applicable to firms, the first is that liquidity risk must be accounted for. Liquidity risk in Amaranth's case compounded an already-bad situation. The second recommendation is that liquidity risk measures should be developed that are common -- the way that VaR is common -- so that communication of liquidity risk is consistent throughout the firm and throughout the industry.
The third is that internal risk management practices must be upheld. The best strategies for managing market risk are irrelevant if the firm is not in a position to implement them. In the case of Amaranth, the energy traders were in Calgary while the risk managers were in New York. This made it more difficult to control the firm's exposure in the natural gas futures market. As well internal incentives should reflect the firm's outlook with respect to risk management. Often, successful trading strategies are handsomely rewarded, while those same players are subject to limited downside risk.
When the nature of capital market distortions is analyzed, irrational behavior appears to be present as evidenced by the disconnect between real world market performance and capital market volatility. This irrational behavior may result from situations such as that at Amaranth, where the risk-return tradeoff for the energy traders was not in line with the actual risk-return tradeoff in the market. That the traders increased their bets in the face of steep losses is evidence that such distortions can not only increase the disconnect between the markets and reality but can severely distort risk and return for the company. If market risk is successfully managed, the firm should not be subject to these distortions, so the incentive programs should reflect rational expectations of risk and return on an enterprise-wide level. The last lesson is that spread positions are not arbitrage positions, and must therefore by factored into any evaluation of market risk.
Of these insights, the only one that addresses the issue of market risk on a systemic level is the call for a common measure of liquidity risk. The remainder are largely focused on managing risk at the firm-specific level -- other companies did not suffer the same fate as Amaranth because they did not have the same trading strategies and risk management style. Some of those same strategies were designed to address market risk, at least under normal circumstances, but were misused by Amaranth. As a hedge fun, Amaranth was not expressly intending to eliminate market risk, however. But hedge funds do provide an opportunity to study the issue of market risk further, because many are designed to perform with negative betas, acting as a counter to market movements.
What the Amaranth case highlights is that the same strategies utilized to counter market risk can increase a portfolio's level of firm-specific risk. Amaranth's energy desk was focused on specific natural gas futures plays, a portfolio that can be described as decidedly lacking in diversification. Yet, such a portfolio could have a role to play in countering market risk. If the concept of diversification is applied beyond the stock markets, greater diversification of investment types and investment strategies could result in a portfolio being hedged against stock market risk, if not total market risk. Commodities, currencies, real estate and other tangible goods, emerging markets, fixed income, hedge funds and other investments when put together can reduce a portfolio's total volatility vs. The market, especially when it is considered that some elements often move opposite the market.
Blankfein (no date) argues this point, claiming that market risk is subject to the same principles of quantification as other forms of risk. He outlines a number of different models that can be used to understand market risk. The exponentially weighted moving average model is useful because it measures volatility against the total market while taking into account the reality that high volatility days occur in clusters. This helps account for those 5% incidents with abnormal returns, the same 5% events that threaten firms that lack adequate insulation from market risk. Building on this model is the GARCH3 model, which assumes that variance rates are mean reverting, trending towards a long-run average. These models are used to help firms understand the rate of market risk to which they are subject.
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