Topics: Risk management, Risk, Operational risk Pages: 30 (10336 words) Published: April 20, 2014
Chapter 2
Overview of Financial Risk Management
Risk management describes a collection of activities to identify, measure and ultimately manage a set of risks. Human enterprise in its various forms confront risks every day; the individual deciding whether to leave a relatively secure job for another with better opportunity and compensation across country; the government facing the threat of terrorist attacks on public transportation; or the bank having to determine which financial products it should offer to customers. While some risks are fairly mundane and others a matter of life or death at times, the fundamental process for assessing risk entails evaluation of tradeoffs of outcomes depending on the course of action taken. The complexity of the risk assessment is a function of the potential impact from a particular set of outcomes; the individual deciding to take a different job is likely to engage in a simpler risk assessment; perhaps drawing up a pros and cons template while a government facing terrorist threats might establish a rigorous set of quantitative and surveillance tools to gather intelligence and assign likelihoods and impacts to a range of outcomes.

Regardless of the application or circumstance, each of the assessments above has a common thread; namely the assessment of risk. But what exactly is risk and is it the same across all of these situations? Risk is fundamentally about quantifying the unknown. Uncertainty by its very nature tends to complicate our thinking about risk since we cannot touch or see it although it is all around us. As human beings have advanced in their application of technology and science to problem solving, a natural evolution to assessing risk using such capabilities has taken place over time. Quantifying uncertainty has taken the discipline of institutional risk management to a new level over the last few decades with the acceleration in computing hardware and software and analytical techniques.

Risk and statistics share common ground as uncertainty may be expressed using standard statistical concepts such as probability. As will be seen later, while statistics provides an intuitive and elegant way to define risk, it nonetheless offers an incomplete way to fully understand risk due to inherent limitations on standard statistical theory and applications. This does not imply that we should abandon statistical applications for assessing risk, but that a healthy dose of skepticism over accepting a purely analytical assessment of risk is a prerequisite to good risk management. As a starting point, basic statistical theory presents a convenient way of thinking about risk. Exhibit 1 depicts a standard normal probability distribution for some random variable x. The shape of the distribution is defined by two parameters, its mean or central tendency centered on 0 and the standard deviation, σ. If risk can be distilled to a single estimate, standard deviation is perhaps the most generalized depiction of risk as it measures the degree to which outcomes stray from the expected outcome; or mean level. More formally, standard deviation is expressed as the following:

Exhibit 1

where pj represents the probability of outcome i, and µ is the mean of the variable x. The variable x could reflect the returns from a product or service for a company, the compensation to an employee for a particular job, or the amount of collateral damage from a terrorist attack, for example. Despite the difference in the variable of interest, the one common aspect for all of these risks is that they can be measured by the standard deviation. Further, risks can be managed based on the tolerance for risky outcomes as may be represented by the distance of a specific set of outcomes from their expected level. Take the case of a company that faces whether to engage in a certain business activity or not. The firm has obtained a set of historical data...
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