To support an effective risk management system, it is necessary to have a sophisticated and complete internal control system that leads to organized and proficient operations. System for risk management is required to have definite financial and other appropriate information to guard the bank's assets. Controls must also be developed against functional risks of losses appearing from uncertainty, errors and frauds. Controls assure conformity with the policies, rules and regulations. For an effective internal control system of risk management, involvement and fostering of risk management culture across the organization is of immense importance. Internal control system of bank is required to control following areas for the dynamics risk management (Gundlach & Lehrbass, 2004):
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Hence control functions for dynamically risk management system is a mechanism that independently and collectively controls price mechanism, validates and reconciles the operations across functions.
Internal Audit Function
Audit function of bank typically assumes internal audit function. Auditors have to verify periodically about the framework of the risk management certified by the board and whether it is correctly executed or not. Audit also explores about the policies and procedures established being satisfactorily implemented and respective ability to control the risks. Objectively internal audit function clearly prevents conflict of interests; independent in auditing operational unit and has power to access any documents and records. The audit function verifies the conduct of the banking operation in core compliance with the set rules, policies, risks limits etc. In addition to this, audit of the data used, assumptions and methodologies being employed in compliance with bank's risk management function and also regulatory functions (Gundlach & Lehrbass, 2004).
The risk management at bank is conducted with high level of sophisticated techniques. There are wide number of measures taken for the purpose that are increasingly getting complex in nature. Banks employ combination of these measures to assess the risk posed to bank. Some have been discussed below (Gundlach & Lehrbass, 2004):
VAR: value at risk
Banks setup many techniques including mathematical and statistical techniques to manage the market risk. Among these techniques, VAR has become a standard for measuring risk of market over the past years. VaR is a method that calculates the worst expected loss under the normal conditions. For worst loss estimation, VaR considers defined confidence interval for a given period of time (Berry, n.d.).
Banks and other financial institution employ VaR at various confidence intervals for different time horizons. For example, for random distribution of return established for time t with confidence interval of 95 percent, VaR is the value of loss with probability of five percent of occurrence.
The time horizon and confidence interval (or probability) for measuring VaR depends on financial products' respective risk and return patterns. In general, the VaR assumes normal distribution of returns across the period taken for measure. The return is the random variable and is measured as follows:
Rt+1 = µt +σ t € t+1
µt is the return expectation with condition to the information available at point in time taken as t
σ t is the standard deviation of Rt+1 with condition to the information available at point in time taken as t.
€ t+1 is the shock with zero conditional mean and one conditional standard deviation.
Banks have two types of choices which are being offered by VAR model; one is the simulation scenario including Monte Carlo versus historical simulation and the other is the valuation approach which involves full revaluation versus sensitivities based risk assessment is conducted (Cornalba & Giudici, 2004).
Monte Carlo Versus Historical Simulation
It is considered to be a better theoretical approach towards the simulation of risk with an advantage of providing absolute pictures of probable risks being installed in the distribution's tail. Monte Carlo helps in analyzing the risk by employing possible outcomes for the instrument as well as various probability distributions. Range of estimations provides outcome information including most conservative, outgoing as well as middle of the road results. It encourages the bank for modifying individual risk factors and equivalent presumptions with attention. Monte Carlo provides complexity for the bank especially when it is used in revaluation. It takes a long time for reaction which results in a bottleneck as compared to the historical situation which is less accurate but easier (Mehta, Neukirchen, Pfetsch, and Poppensieker, 2012).
Historical simulation on the other hand develops results on the basis weight-age or proportion allocated to different periods in past. Weight-age or proportion provides immense insight to banks specifically in the stress time period. The simulations are made in accordance such as single year equal weightage, multi-year equal or time based weightage and other weightages (Mehta, Neukirchen, Pfetsch, and Poppensieker, 2012).
Full Revaluation Versus Sensitivities
Both full revaluation and sensitivities approaches provide approximately equal results for same products and as a result, most of the banks prefer sensitivities approach. In the case of complexity full revaluation provides more accurate and authentic calculation of risks including the extra amount in time and effort.
Full revaluation method of measuring VaR refers to the method where calculation of the prices is taken at full. For example, to all 499 possibilities in case the total possibilities include 500 outcomes. Using all 499 values of each instrument in the portfolio the scenario result is calculated and then return of the instrument and portfolio is determined. Whereas in contrast in sensitivities, the returns for sensitive scenarios are measured such as increase in the factor price that have direct impact on the portfolio instrument etc. For example, using Delta Approximation first order sensitivities of the instrument are measured using following formula:
∆(V) = Delta * ∆(X)
∆(V) is the calculated change in the instrument's value as a result of change in factor.
∆(X) is the change in value of the underlying factor.
Delta: is the first order derivative of the instrument with respect to the underlying factor (Cognizant, 2012).
Most banks are opting for stress testing as their additional technique to complement VaR. Banks opt this testing in order to address the current and possible risks concentration. It also aids in the development of tools that are used to manage the risk and diminishing measures over a range of stressed circumstances. These stress tests are conducted by the supervisors on regularity on major activities involving operational risks which discloses the bank towards the market risk (Mehta, Neukirchen, Pfetsch, and Poppensieker, 2012).
Graphs have been developed based on the stress testing with VaR. For example, credit loss at 95 percent confidence interval is nearly 8.6 billion while the probability of loss increases to 13.6 billion at 99 percent confidence interval. Similar assessment has been conducted for exploring the chances of total loss, market risk and contagion risk at different stress level. The past data or the projected data is being used for such assessments. Different stress levels are being tested to gauge the capacity of the bank to sustain the pressure (Quagliariello, 2009).
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The literature review chapter has developed the detailed outline for risk management system at banks. Assessment of bank's risk management system including the structural framework, system and tool applied provide concrete idea about the importance and role of dynamic risk management across the banking system.
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