Risk is an integral part of virtually every decision we make. In a modern portfolio theory framework, risk and return are two required inputs as we seek to maximize returns at a given level of risk. This task is further simplified by the assumption that an asset providing a higher rate of return is riskier than an asset providing a lower rate of return. In this process, risk is assumed to be known and quantified. Standard deviation, variance, and volatility offer simple and tangible metrics to quantify the amount of risk at play.
Because risk is quantifiable, it should be easily predictable and readily manageable. Using various statistical and nonstatistical approaches, risk measures can be calculated and used to predict the impact risks may have on the performance of the portfolio. These methods allow for managing the risks that we know that we know, such as small price and yield changes. For this task, we can use the various financial tools that have developed over the years to manage the effects of these types of risks.
How to manage the risks that we know that we do not know remains a challenge, even though reoccurring financial crises generously generate ample data to analyze, observe, and extrapolate.
But the real challenge in managing risks in investment management is managing and measuring the impact of risks that we do not know that we do not know. These risks, such as extreme tail risks or black swan events, are risks that we cannot fully comprehend, imagine, ...

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