In the subject of finance and economics, it is very crucial to choose an appropriate risk measurement technique achieving desired results. Among various risk measurement techniques, the most commonly used was Value at Risk (VaR) measure. But, due to the failure of this method to consider extreme events, it became imperative to develop an effective method. This is the reason marketers came up with the method of Coherent Risk Measure. Here you will learn about all the aspects of this technique educating you to tackle some of the most extreme challenges.
Before elucidating the technique of coherent risk measure, you need to understand the significance of this subject in financial economics. A coherent risk measure is a method used to estimate the profitability of a business depending on the risk taken. In this discipline, the amount of risk involved in a business is directly proportioning to the profits earned. The demand for this technique emerged after encountering the shortcomings and drawbacks of the method of Value at Risk. Correctly, in the discipline of actuarial science and financial economics, the method of coherent risk measure plays a vital role. It considers all the extreme events lying under the shortcomings of VaR measure making it suitable while analyzing risk.
The method of coherent risk measure works on the principle of Expected Tail Loss helping marketers to estimate the risks accurately. Also, for a risk measure to be coherent, it should satisfy specific mathematical properties possessing a strong significance in risk measurement. Thus, this method satisfies the properties of monotonicity, sub-additivity, positive homogeneity, and translation invariance.
Coherence risk measure follows a set of relevant criteria needed to measure the risk involved in earning profits of a business. To estimate the profitability of a business, you need to consider the factors involved in risk measurement like; p(X) where X is a set of outcomes.
If X < Y for each scenario, p(X) > p(Y).
p(X + c) = p(X) - c
The method of Value at Risk got nullified as it failed to satisfy the condition of sub-additivity. Suppose, you have two portfolios X and Y, then the benefits can be given as:
p(X) + p(Y) - p(X + Y)
Therefore, risk measurement can be considered as coherent if it satisfies all the four factors described above.
Value at Risk (VaR) is an old technique of actuarial science and financial economics used to estimate the risks involved in the business. But, the reason that this theory lost popularity in the market was due to its failure to satisfy the conditions of the sub-additivity axiom. Moreover, the technique of VaR failed in considering extreme events making it more challenging for a business to estimate the risks. It is imperative for any organization to evaluate the risks involved to provide it with an adequate measure contributing to its promotion. Therefore, the primary reason for the failure of this method was its incapacity to consider all the crucial factors.
Expected shortfall is calculated as an average of the total losses having a value greater than or equal to VaR. It estimates the future value of an investment in the worst possibilities of any case. Therefore, the technique of expected shortfall is considered as an effective, coherent risk measure widely known by marketers globally. It is also known as the conditional value at risk (CVaR), average value at risk (AVaR), and expected tail loss (ETL).
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