Introduction
Like other businesses, Banks face multiple risks. However, due to the strategic importance of the banking sector to the economy and the government’s involvement in risk control, risk management in banking is heavier than in other industries. Understanding the real risk is critical to effective risk management. The main types of risks that banks face include credit risk, market risk, operational risk, reputational risk, and liquidity risk (Vyas & Singh, 2011). Understanding the specific risks facing a particular bank allows the management to apply various tools to effectively manage them. According to Kanchu & Kumar (2013), various models and tools are used to manage the different types of risks in the banking sector. For example, the Value at Risk Model and Stress Testing Model are widely used tools to minimize the impact of unfavorable events in the banking sector. However, each of these models has its own merits and drawbacks. This discussion analyzes the problems associated with the use of the Value at Risk Model and Stress Testing Model in managing risks in the banking sector. In addition, the discussion covers the problems associated with credit risk in measuring risk. The discussion further recommends the various ways of eliminating or reducing problems associated with the Value at Risk Model, Stress Testing Model, and Credit Risk.
Problems and Issues Associated with the Use of Value at Risk Model
By definition, the Value at Risk Model is a metric used to estimate the risk of an investment in the financial sector. According to Krause (2003), it is a statistical tool that measures the amount of potential loss that could occur over a specific period of time. The model specifically calculates the probability of losing more than a specified amount in a portfolio. This model is widely used by banks due to its simplicity. Although this model is a useful risk management metric particularly when applied appropriately, it is prone to significant measurement errors (Krause, 2003). Some of the main problems and issues associated with this model include difficulties in calculating risk for large portfolios, differences in approaches, the inability to measure worst-case loss, and giving a false sense of security (Berkowitz & O’Brien, 2002). In addition, the outcome of the Value at Risk Model is as good as the assumptions and inputs used.
Difficulties in Calculating for Large Portfolios
Calculating the Value at Risk of a portfolio not only requires the estimation of the return and volatility of individual securities but also the correlation coefficient between them (Krause, 2003). Given the growing demand for large and diversified portfolios, using the Value at Risk model to measure risk becomes extremely difficult. Thus, the higher the number of securities in a portfolio, the more difficult it is to estimate the Value at Risk.
Differences in Approaches
There are generally three methods of calculating Value at Risk of a portfolio. These methods include the historical approach, variance-covariance approach, and the Monte Carlo Simulation approach (Hendricks, 1996). Using these different methods to calculate Value at Risk of the same portfolio always leads to different results.
Inability to Measure Worst Case Loss
It is impossible to know the maximum possible loss by simply looking at the Value at Risk. For example, the worst-case loss may only be just a small percentage slightly higher than the Value at Risk, but it could be significant enough to affect the investment (Krause, 2003). Simply put, Value at Risk does not reveal anything about the maximum possible loss.
It Gives Some False Sense of Security
Using Value at Risk to assess risk exposure can be misleading. The vast majority consider Value at Risk to be the most they can lose, particularly when it is calculated with a confidence level of 99% (Berkowitz & O’Brien, 2002). The 1% loss can be so significant and this is what causes misunderstanding. The 99% confidence level in the Value at Risk model can consciously or unconsciously give people some false sense of security.
The Results are as Good as the Assumptions and Inputs Used
Like other quantitative techniques used in finance, the results of Value at Risk are as effective as the inputs and assumptions used (Berkowitz & O’Brien, 2002). For example, a common mistake when using the variance-covariance approach is the assumption of normal distribution for assets and returns from portfolios. Inputting unrealistic return distributions can result in the underestimation of risk.
Overall, Value at Risk is not always a good tool for risk measurement because it is vulnerable to significant measurement errors. However, it can be an effective risk management tool when applied properly with a clear understanding of its underlying assumptions.
Problems and Issues Associated with the Use of Stress Testing Model
The Stress Testing Model is a risk management tool that involves computer-generated and highly complicated simulation models that analyze the impacts of extreme scenarios (Stein, 2012). Simply put, the stress testing model analyzes how a financial institution’s balance sheet responds to certain situations. For instance, in times of financial uncertainties, banks and other financial institutions deploy stress testing models to study the market and analyze portfolio risk to help these institutions make an informed decision based on the results. These models rely on high-quality data to help organizations effectively identify potential risks and mitigate them. According to Basel II regulations, the purpose of stress testing in the banking sector is to establish whether a bank has sufficient capital and liquid assets to withstand stressful times (Stein, 2012). It is carried out for internal risk management as well as for regulatory purposes. UK, USA, and EU regulators, for instance, require banks to perform specific stress tests. Financial institutions are required to provide a capita plan justifying the models used as well as the results of their stress testing. If a bank does not meet the stress test requirements because of insufficient capital, then it must raise more capital by limiting the payment of dividends. Although stress tests are effective risk management tools, banks face various challenges when implementing stress testing models. According to Thun (2012), these challenges include determining how and what needs to be stressed, designing effective and meaningful scenarios, Gathering sufficient data, and communicating the results for action.
Determining How and What Needs to Be Stressed
Many banks experience problems with this initial step of determining what should be stressed. Instead of determining what to be stressed from a bank’s market and risk analysis perspective, many organizations align their efforts with market best practices or standard regulatory requirements (Thun, 2012). For example, over the last two decades, two main methods of stress testing have gained more appeal, including scenario analysis and sensitivity tests. Sensitivity tests are criticized because they assume that only a single factor like the shift in the yield curve change. Sensitivity tests, on the other hand, are easy and straightforward to execute but are criticized because they do not consider the interdependence between the risk factors.
Designing Meaningful and Effective Scenarios
A major challenge with stress testing models is designing meaningful and effective scenarios. Based on the scenario designed, the outcome of the stress test could significantly misrepresent the risks that a bank is actually exposed to (Battiston & Martinez-Jaramillo, 2018). The scenario may not be plausible or severe enough, thus it may not mitigate the bank against critical risks. Classic examples of this form of misrepresentation are the unforeseen problems that befell Franco-Belgian Bank in October 2011 and the sudden problems that the bank of Ireland faced in 2010 despite having passed stress tests outlined by the European Banking Authority regulators.
Gathering Sufficient Data
The greatest challenge that banks face is the lack of sufficient data. Specifically, data from periods of severe stress is not always available (Fell, 2006). Such information would be the basis for designing meaningful scenarios as well as understanding the link between risk drivers and macroeconomic variables. Given the relationship between various macroeconomic variables like inflation, GDP, oil prices, and GDP, gathering sufficient information allows banks to model behavior in times of stress (Fell, 2006). On the contrary, the lack of sufficient data about the macroeconomic variables leads to an unstable and weak relationship between the scenarios designed and the relevant risk factors.
Communicating the Results for Action
Efforts to design meaningful and effective stress tests are useless if communication, which is a critical aspect is missing. Internal communication, which should be in the format prescribed by the regulator is as important as external communication. For effectiveness, the stress test must be communicated clearly to everyone internally (Battiston & Martinez-Jaramillo, 2018). In addition, the stress test must be well understood, particularly by the senior managers. Furthermore, the stress tests must indicate the degree of risk exposure and the impact on the business.
Overall, even though much has been achieved over the last few decades in terms of designing an effective stress test framework, risk managers still face several challenges as mentioned above. These challenges must be addressed in order to turn stress testing models into powerful risk management tools.
Issues Associated with Credit Risk in Measuring Risks in Banking
With the recent global crisis, credit risk management remains a top priority for many banks and regulatory authorities. Although strict credit requirements like the top-down approach have been useful in mitigating economic risk, such approaches have left many financial organizations struggling to achieve effective credit risk assessment (Brown & Moles, 2014). In an attempt to implement a raft of risk strategies to improve overall performance and gain a competitive advantage, banks have to overcome a number of credit risk management challenges. According to Altman (2002), some of the main challenges associated with credit risk management include, ineffective data management, limited group-wide risk infrastructure, inefficient risk tools, and less-intuitive reporting.
Ineffective Data Management
Effective credit management requires an organization to securely gather data, analyze it, and store it securely based on a particular criterion. All databases need to be regularly updated to ensure easier retrieval as well as to avoid relying on outdated information in making decisions (Altman, 2002). Streamlining the manner in which data is gathered, analyzed, stored, and retrieved is critical to effective credit risk management. Data centralization is also important as it allows for easier analysis and modeling, which provides a clearer picture of a business or individual’s credit worthiness.
Limited Group-wide risk Infrastructure
Most often, it is not enough to evaluate the risk posed by a single individual or entity. A comprehensive and broader view of all risk measures is key to understanding the risk of a new borrower. In addition, having efficient stress testing models and capabilities is also important to ensure that an organization has an accurate assessment of risks (Cumming & Hirtle, 2001). Various rating agencies are also important as far as establishing credit scores for individuals is concerned. Banks can utilize a credit rating score system to determine default risk and make accurate credit decisions. Thus, having a 360-degree view of a financial organization’s risk that covers the entire group can create new opportunities for lending while maintaining risk at lower levels.
Insufficient Risk Tools
A broader and comprehensive scorecard should identify potential strengths and weaknesses linked to a loan. Risk analytics, for instance, took a step forward when financial institutions, particularly the bigger banks began embracing big data programs (Altman, 2002). However, small and medium banks experienced a slower adoption because of the huge investment required. With the modern risk tools being made available in the market, banks of all sizes will have access to big data in the near future. Risk analytics is considered a huge transformation that allows banks and other lending institutions to align their culture, governance, strategies, and technology in order to optimize risk management. Through optimization, banks can mitigate the risk management process, thus broadening their returns from lending.
Less-intuitive Reporting
In order to provide valuable insights, financial reports should be presented in a clear, clean, visualized, and intuitive manner. For example, eliminating irrelevant data from financial statements that only overburden analysts can help in narrowing down to the most pertinent information (Cumming & Hirtle, 2001). Using analytics requires a powerful reporting process that allows the bank to clearly visualize at the individual borrower level as well as across the organization. Thus, the lack of clear, clean, visualized, and intuitive reporting is a major hindrance to effective credit risk management.
Ways of Reducing or Eliminating Problems Raised in Part A
The various problems and issues associated with the use of the Value at Risk Model, Stress Testing Model, and credit risk can be minimized or eliminated through a range of ways as outlined below:
Addressing Issues Associated with Value at Risk Model
Despite its underlying limitations and problems, the Value at Risk Model can still be useful as long as the users understand its weaknesses. As a recommendation, Value at Risk should just be complemented with other risk management tools, particularly those that take into account the 1% worst-case that the Value at Risk Model ignores completely (Choudhry, 2011). Thus, banks should not allow the Value at Risk Model to become a false sense of security.
Addressing Issues Associated with Stress Testing Model
The results of stress testing can significantly affect the process of decision-making. For this reason, there is a need to benchmark stress test results against a bank’s appetite. Benchmarking allows a bank to review its underlying risk profile. The bank management has the responsibility of preparing strategic plans for early interventions. These strategic plans include raising more funds, suspending the payment of dividends to the shareholders, and eliminating or minimizing some business activities (Vyas & Singh, 2011). In addition, banks can ensure frequent reporting by incorporating in the company’s strategic planning the outcomes of hypothetical stress tests and scenarios, including those that are not likely to materialize.
Addressing Issues Associated with Credit Risk
The various challenges in using credit risk to evaluate risks in banking can be overcome through close monitoring, mitigation, creating relationships between different risks, and regular reporting. Mitigation involves reducing or minimizing the likelihood of uncertain events occurring (Kanchu & Kumar, 2013). Credit risk should be continually reviewed to ensure that the bank is adequately protected. Regular monitoring should be made a routine and a proactive process. Close monitoring allows banks to address emerging trends in order to establish whether or not progress is being made in mitigating credit risk. Furthermore, creating relationships between different risks, mitigation activities, and business units provide a cohesive picture of a bank (Kanchu & Kumar, 2013). These relationships help in the recognition of downstream and upstream dependencies, as well as the identification of other risks. Designing centralized controls further eliminates the probability of missing important pieces of information. Frequent reporting involves presenting regular updates regarding the way the risk management program is progressing. The information should be reported in a clear and engaging manner to attract the support of different stakeholders at the bank. Developing regular risk reports that give a dynamic view and centralize all information is step forward towards giving a broader view of the bank’s risk profile.
Conclusion
In summary, whether a bank is managing risks as defined by the UK, USA, EU, or other regulatory authorities, it is important to consider risk management as more than just a compliance requirement. The different risk models are designed to address the unique needs of every bank, as well as the dynamic needs of the banking industry. The Value at Risk Model and Stress Testing Model alone is not enough to sufficiently manage all risks a bank faces. To be effective, these models should be complemented with other risk management tools. In addition, understanding the weaknesses of each model helps the management to use every model selectively.
References
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