Risk management is a critical part of an organization that has prosperity as its key priority. For successful operation, an organization must deal with any loophole that allows its operations to be vulnerable to financial and other types of risks. It is thus sensible to use effective frontier for any business. The efficient frontier is the “set of optimal portfolios that offers the highest expected return for a defined level of risk or the lowest risk for a given level of expected return”(Abu-Alkheil, Khartabiel& Dali, 2018).
Currently, Enterprise Risk Management (ERM) needs an analytical risk management framework that is dynamic and comprehensive that covers all aspect of the enterprise. The use of efficient frontier offers precisely what the enterprise needs to manage its risks. With many derivative techniques, the efficient frontier provides an efficient portfolio approach to market risks, hazards, and reputational risk domain. It is thus, different from other complex risk assessment techniques in many ways. First, it analyzes the risk versus return for an investment portfolio. The technique uses the principle of the ultimate return while projecting the financial aspect of an asset ( Shokrollahpour, Lotfi & Zandieh, 2016). Other complex risk assessment techniques usually assess a single element of risk analysis, and this makes them narrow with regards to their estimations. Other than that, the efficient frontier has the ability to identify the risk within the organization boundary. The aspect is sometimes called the risk sensing. The other forms of risk assessment techniques usually have a narrow estimation of risk in an organization. Other than this, the efficient frontier also offers a conscious effort that can define advantage or exploit any risk profile that can make a company or organization outstanding in a competitive environment. Lastly, the ability of the efficient frontier to represent the highest level of a portfolio's return for any given level of risk is a unique aspect of this type of risk assessment that makes it different from other complex risk assessment techniques.
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There are a lot of limitations the user may face while using efficient frontier. Most of the flaws are with regards to many assumptions that do not reflect the real situations in the business environment. For instance, there is an assumption that asset returns follow a normal distribution. This may not be a true reflection of the reality since in some occasions, " securities may experience returns that are more than three standard deviations away from the mean in more than 0.03% of the observed values” (Abu-Alkheil, Khartabiel& Dali, 2018). Other than this assumption, the implementation of the model is sometimes an issue in reality. During implementation, an assumption is usually made that there is normally a way of modeling the insurance or risk in an appropriate manner. This may not reflect the reality in the business environment since , plain modeling foibles, information asymmetry, data limitations, and internal disputes, can easily put a barrier to the framework’s best intentions.
The best aspect of the efficient frontier model is its ability to be put into use non-mathematically by the decision makers. For instance, the management usually has a pool of data concerning business and it usually their duty to use such data to manage the risk in the business environment. The efficient frontier thus makes the managers understand the risk versus the cost to efficiently manage the uncertainty in the business. Further, the organization management can also reduce their earnings volatility on risks by examining the portfolio of risk deeply through the efficient frontier ( Hamdan, Rogers & Hamdan, 2016). This puts them in a position that enables them to make a data-oriented decision and thus, reduce volatility.
References
Abu-Alkheil, A., Khartabiel, G., & Dali, N. R. S. M. (2018). A two-stage parametric stochastic frontier analysis (SFA) of the effective performance of Shari'ah compliant banks: global evidence. American Journal of Finance and Accounting , 5 (2), 85-110.
Hamdan, M., Rogers, J., & Hamdan, A. (2016, September). Build to Order supply chain efficiency using Stochastic Frontier Analysis (SFA). In 2016 Portland International Conference on Management of Engineering and Technology (PICMET) (pp. 2205-2215). IEEE.
Shokrollahpour, E., Lotfi, F. H., & Zandieh, M. (2016). An integrated data envelopment analysis–artificial neural network approach for benchmarking of bank branches. Journal of Industrial Engineering International , 12 (2), 137-143.
