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Risk Management in Financial Institutions Essay

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Updated: Apr 2nd, 2020

This is a method that is used widely in financial statistics to determine the amount of risk incurred by a company wholly or a specific investment over a given period (Cortez 197). In that regard, risk and time become the critical variables that define Value at Risk.

Having been used to determine the level of risk incurred by undertaking a certain investment, the risk management personnel can use it as a crucial tool when seeking to control the level of risk incurred over the given period. Essentially, the personnel managing risks are mandated to ensure that the amount of risk, which is taken by the company, does not exceed the level of loss that can be incurred potentially in the worst-case scenario.

What Risk Does it Measure and How?

In essence, the VaR is applied to determine the maximum amount of risk that can be incurred by a company or an investment. This is determined by making a determination based on the loss that can befall a company in case of the worst-case scenario. The calculation incorporates three variables, including the probable financial loss, the probability indicating the likelihood of the loss occurring, and the amount of time in which the company may be exposed to the risk.

For instance, if a company establishes that it has 0.05 (5 percent) VaR of $120 million in a single month, then it can be implied that it has a 0.05 probability of losing 120 million dollars in a given month. Accordingly, it can be implied that the company is vulnerable of losing $120 million once within a period of every twenty months.

Importantly, the determination of VaR is conducted based on a given degree of confidence which is also referred to as the confidence interval. This determination is critical because it can help the managers to identify the level of investment that should be taken in order to ensure that the potential loss does not incapacitate the company’s solvency. Understandably, the application of probability to such situations is a critical factor that ensures financial preparedness and planning.

Computational Methods Used to calculate it

Historical Method

This method of calculation takes into consideration the past returns of the investment. In order to facilitate the calculations, the returns are re-ordered in such a manner that the arrangement ranges from the worst performance to the best case (Grinsven 176). It then introduces a critical assumption that the historical performance, which was experienced in the past, will occur in subsequent unit periods in terms of the risks incurred.

This assumption enables the risk managers to use the past data as the reflection of the future experiences. Essentially, the use of this method was preferred to the earlier re-sampling technique. The preference is informed by the fact that the historical method hardly takes into consideration the assumptions that are inherent to the returns. Instead, it is based on the repetitive nature of the history which shows how the company investments have behaved in the past.

Monte Carlo

This technique, which is also referred to as the Monte Carlo Simulation, seeks to determine the VaR using a model. This model is derived from the projected returns occasioned by the stock prices. Then, the derivation involves robust application of many hypothetical trials using the designed model. In essence, it is referred to as any technique which can produce trials in a fairly random manner.

However, the Monte Carlo ideology suggests that the model does not have to provide any information about the inherent method used. Widely, the simulation is equivalent to a black box that is considered to generate results randomly without any predetermined sequence.


This method needs the risk managers to determine the standard deviation of the investment and the inherent mean returns of the same portfolio. In essence, this implies that the returns occasioned by the stocks are distributed normally. After close consideration, it is evident that the idea of this technique is related to the essences of the historical method that was discussed in the previous paragraph.

However, the two techniques differ at a point where the variance-covariance method take into consideration the familiar curve rather than the using the actual data as used in the historical methodology of obtaining VaR. Considering that the method assumes that the parameters are distributed normally, it becomes straightforward and simple in terms of approach. This implies that the model can be used to calculate the VaR for investments that follow linearity.

Advantages and Disadvantages of Each Method

Historical Method

Essentially, the historical method incorporates only three variables which are additionally easy to determine. This renders the formula intuitive and essentially easy to apply during the calculations. Consequently, it can be used to estimate the VaR in cases of urgency in order to make critical determination over a small period of time (Wong 243).

However, the method is affected by the assumptions made in light of calculating the risk. From the previous paragraph, it was noted that it relies of the historical performances to predict the future. However, it cannot be disputed that the past is not a perfect reflection of the future. When the factors leading to the past performance are not in force, the future return may change critically to cause errors in the calculations.

Monte Carlo

The Monte Carlo simulation is one of the most effective and advanced method of calculating the VaR. In essence, it can be used to evaluate the payoffs which depend on the pathways and also portfolios with non-linear functions. As such, it can be used in cases that involve complex derivatives.

However, the complexity and advancement of this formula also lead to a critical disadvantage. Understandably, the use of this simulation in an attempt to estimate the VaR of complex functions requires a lot of time. As such, it cannot be applied during the time of urgency. On the other hand, the variance covariance and historical methods can be used in such cases because they are simple to apply and interpret the results.


With regard to the advantages of the Variance-Covariance method, it is evident that the approach is based on the fact that the parameters of investments are distributed normally. This makes the method to adopt a straightforward approach which renders it easy to apply.

However, the assumption leads to two critical disadvantages that may impede the effectiveness of the method. This condition of such ineffectiveness is most evident when the VaR is used to calculate the risk whose parameters are not stated satisfactorily. In addition to this, the method’s simplicity allows the calculation of portfolios that assume linear relationship of parameters. As a result, it becomes incapacitated when calculation non-linear portfolios especially when they include factors such as options. Understandably, options are online functions which may not behave in a similar way as the linear investment portfolios.

VaR Flaws

As stated previously, the VaR is used to measure the maximum loss that can probably befall a company in the worst-case condition. As such, it does not indicate the risks that may be encountered within the spontaneous points in time. As such, it is not a comprehensive technique that can enable the risk managers to control risk spontaneously.

Works Cited

Cortez, Annetta. Winning at risk strategies to go beyond Basel. Hoboken, N.J.: Wiley, 2011. Print.

Grinsven, Juergen H. M. van. Risk management in financial institutions formulating value propositions. Amsterdam: Delft University Press/IOS Press, 2010. Print.

Wong, Maxwell. Bubble value at risk a countercyclical risk management approach. Rev. ed. Singapore: Wiley, 2013. Print.

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