The Knowledge Exchange / Risk Management / Moving toward optimization

Moving toward optimization

Myron Scholes discusses value of high-performance analytics

Myron Scholes, Nobel LaureateIn the second installment of our seven-part series, we want to move forward and expand upon our research to propose a new framework geared toward optimization and the trade-offs for achieving the required returns for adequate levels of invested capital.

Risk management is not risk minimization; risk management is the optimization of a number of firmwide components. Risk management is not solely a monitoring technology; it is an entirely integrated approach with return generation and the dynamics of running an entity. And new computing and telecommunications technologies make the integrated approach feasible at a reasonable cost.

Risk management requires an understanding of necessary rewards (including reserves for adjusting holdings to shocks) for the capital needed to sustain each business activity, including how much (if any) capital should be shared among competing activities and the dynamics of changing positions as opportunity sets change. Therefore, optimization technology allocates scarce capital among competing alternatives, taking into account not only the rewards if everything goes well or along the expected path, but also the adjustment costs of altering the risks at times of shock. The fact that opportunity sets change cannot be ignored.

Risk management is not risk minimization; risk management is the optimization of a number of firmwide components.

If the opportunity set might change or does change – either with new understanding and techniques or as a result of changes in liquidity in the market or planning decisions of other market participants – the entity’s level of risk is a senior management responsibility and decision requirement.

Operating or financing flexibility decisions can’t be relegated down to divisions or to subsidiaries. For example, a firm that is less leveraged will have capital available for investment most of the time and, as a result, will experience lower returns on equity more often than would a more highly leveraged firm. The lower leveraged firm, however, will most likely suffer much lower adjustment costs (if any) at times of market shock. In fact, its opportunity set would be much greater at that time than its competitors, who would be constrained. For example, JPMorgan Chase, which ran more conservative risk positions than other banks, experienced a large inflow of capital during the crisis, because clients of weaker banks flocked to it. As a result, the bank was able to capitalize on the rich opportunity set at that time.

The level of risk selected is a business decision that is not independent of how the investment opportunity set might change. The optimization decision involves how to combine assets. Optimization requires an analytic framework to incorporate and aggregate these financial metrics to provide decision tools and monitors for traders and managers. Scenarios are used to understand the level of risk decision and how shocks might compound loss in a particular scenario. The resulting optimized scenario may be to abandon a position rather than engage in further diversification or hedging.

To accomplish these requirements, a firm needs computing power, algorithms for finding approximate solutions to these computationally difficult problems at the speed of the market, and a management philosophy that disciplines the firm to anticipate changes in the opportunity set when making optimization decisions. Optimization is no longer a quarterly or monthly reporting cycle – it is an activity of responding quickly to market, capital and risk factor changes when they happen.

In our next article, the third installment in this series, we are going to talk about the reasons financial firms failed during the crisis that began in 2008. We’ll try to explain how a narrow view of risk and return led to their failure. Read “Quantitative math or management analytics?

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