In the previous articles, we have discussed the details about the concept of value at risk (VaR) as well as the theoretical calculation of value at risk (VaR). The theoretical portion of the model is easy to understand. However, it needs to be understood that this is not how the model is actually implemented in real life. In this article, we will have a closer look at some of the complications faced while implementing the value at risk (VaR) model.

The Problem of Too Many Asset Positions

Theoretically, the value at risk (VaR) model proposes that each asset should be considered separately. This means that if a company has a hundred different types of bonds, then they should run the value at risk (VaR) model for all of them. However, in real life, this becomes a challenge.

This is because it is common for companies to have hundreds or even thousands of different types of securities. As such, computing the value at risk (VaR) by inputting values for each individual asset becomes a computational challenge. However, nowadays, computing power is available fairly cheaply. Hence, it would be possible for companies to tremendously scale up their computational power without having a proportional impact on their budget.

However, computational power is not the only challenge. If there are hundreds of assets, then the details of these hundreds of assets also have to be entered into the model. Deriving and maintaining data related to hundreds of assets and the various correlations between them can be a challenge. Firstly, a lot of time, money, and resources will have to be allocated to this task. Secondly, the complexity may be needless since spending more time and money may not necessarily improve the accuracy of the model.

Therefore, during the implementation of the value at risk (VaR) model, every company faces the same problem. They need to group assets together in order to reduce the asset types. However, they also need to ensure that the accuracy of the model is not compromised while doing so. Hence, the grouping has to be done in an intelligent manner.

Methods Used to Implement Value at Risk (VaR) Model

1. Sensitivity Based Approach: One approach commonly used is to group assets that have similar sensitivities into one group. The sensitivities are identified by conducting statistical tests. For instance, it is a known fact that debt instruments are more sensitive to interest rate changes. Similarly, some other securities may be more sensitive to changes in the forex rate.

   The concept of sensitivity is not really new to traders. They have been unofficially using this method to manage risks for a long time now. This method of grouping assets has been found to be most effective since it does not compromise the integrity and accuracy of the model.

   However, this model requires a tremendous understanding of the various types of risks that an organization faces. The initial setup of the model will be quite difficult. However, once a model has been put into place, it may be easy to add assets to or remove assets from a particular risk factor group. Using this approach hundreds or even thousands of assets can be mapped to a single group and the complexity can be greatly reduced.

2. Cash Flow Based Approach: The cash flow-based model assumes that any asset can be broken into its cash flows. This may be true for most assets such as bonds and stocks. However, it is not true for some assets such as options. However, since this approach works for a wide variety of other assets, it is commonly used in the market.

   As a part of this approach, the present value of the cash flow of the assets over a period of time is calculated. This present value is then mapped to different time horizons. Now, cash flows are all being expressed in terms of present value. Hence, they are additive! As a result, the organization does not have to maintain thousands of assets. This reduces the computational as well as the data collection complexity of the underlying model.

The Errors

Mapping the various assets to risk factors will not be an error-free process. Many times, the organization may not be sure of the mapping. In such cases, they may have to make difficult choices. They must be aware of the two types of errors and their consequences.

1. Type 1 Error is the error of commission. In this context, this means that the asset was placed in the wrong risk factor category. For instance, the asset was more sensitive to interest rates but it was placed in the forex risk category. The possible loss that may happen if this type of error takes place should be calculated.

2. Type 2 Error is the error of omission i.e. a particular asset was not added to any risk category i.e. it was missed. The possible losses from this approach should also be known.

The final decision should be taken to minimize the impact i.e. the lower value of type 1 or type 2 error must be selected.

The bottom line is that the practical implementation of value at risk (VaR) models is quite different as compared to the theoretical construct. A lot of different types of challenges may be faced while implementing the model.

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