Financial Risk Management: Credit, Liquidity, Currency Risks

This report delves into the financial risk management strategies employed by Arcelor Mittal, focusing on key areas such as credit risk, liquidity risk, and currency risk. It examines the application of sensitivity analysis to evaluate the impact of interest rate fluctuations on the company's assets and liabilities, providing numerical examples to illustrate potential financial outcomes. The report further explores model risk, its components, and its significance in financial decision-making. It highlights the importance of model risk in mitigating business losses and enhancing operational efficiency. The report concludes by emphasizing the role of model risk in identifying and addressing uncertainties, ultimately aiding in the development of effective financial risk management strategies. The report also includes references to academic sources supporting the analysis.

Financial risk management
Risk Management
[DATE]
[Company name]
[Company address]

Answer to question no- 2(A)
There are many sort of the financial risk could be faced by Arcelor Mittal Company in its
business process. However, the major financial risks faced by company is credit risk,
liquidity risk and currency risk.
In Iron and Steel business, these three financial risks are mainly faced by corporation such as
credit risk, liquidity risk and currency risk. However, estimation of the factual changes is not
feasible due to several other factors such as GDP Rate, Budget of country, market fluctuation
and Bank rates (Bollerslev, Engle, & Wooldridge, 2012).
The sensitivity analysis is crafted to evaluate the range of variables that may impact the
business growth and sales of Arcelor Mittal Company. By using the sensitive analysis we
could identify the possible fluctuation in the sales and growth rate of Arcelor Mittal
Company.
For instance, on the basis of the credit risk, liquidity risk and currency risk, sensitivity
analysis is implemented to determine the interest rate sensitivity of its balance sheet.
Arcelor Mittal Company has 10-year $1 million loan with a coupon rate and yield 12% and
same is financed with the 10-year $1 million CD with a coupon rate and yield of 10 percent.
In this case, due to the sensitivity factors, increase in the 100 basis points in the interest rate
will cause the market value of the assets and undertaken liabilities to decrease the following
amount (Danielsson, et al. 2016).
The Loan amount: $120,000*PVIFAn=10,
Interest rate=13% + $1,000,000*PVIFn=10,i=13%
This will be equal to = $945,737.57.
CD: $100,000*PVIFAn=10,i=11% + $1,000,000*PVIFn
=10,i=11% = $941,107.68.
In this case, it could be analyzed that Arcelor Mittal Company will have to face downfall in
tis loan value by $54,262.43 and at the same time CD value of the loan will decreased to
$58,892.32. However, on the basis of the used sensitivity analysis, it could be inferred that

the downfall in the assets is $4,629.89 less than the liability. In this case, change in the 100
points has been made on the basis of the other external economic factors such as GDP Rate,
Budget of country, market fluctuation and Bank rates (Danielsson, et al. 2016). Therefore, it
could be inferred that the sensitivity analysis could be used to evaluate the fluctuation in the
process.

Answer to question no- Part-B
The model risk could be determined as type of risk that occurs when the financial model
deployed by Arcelor Mittal Company fails to measure firm’s market risk and business failure.
It is the sub part of the operational risk which affects the mostly the firm which uses it.
However, due to its increased complexities, investors do not use this model to make their
investment decisions (Bignozzi, Puccetti, & Rüschendorf, 2015).
The model risk model has three main components such as input which form the data, method
which reflects the technique used and other is reporting components which is useful to make
decision. This risk model is imperative to the financial risk management of the firms as it
strengthen the business decision making based on the internal and external factors, lower
down the business losses and strengthen the business effeteness by reducing the unnecessary
costing. The financial management firms focuses on the cost reduction and strengthen the
business efficiency (Bollerslev, Engle, & Wooldridge, 2012). The model risk is the key to
avoid and eliminate the undue capital buffers and add-ons which not only increase their work
process but also increase the overall process outcomes. For instance, Model risk identify,
inherent risk, control risk and detention risk either in financial risk analysis or audit risk
analysis (Saltelli, Chan, & Scott, 2010). These three factors are necessary to evaluate the
possible uncertainty in the external financial factors. If these risk could be identified by using
the model risk then financial management firm could easily evaluate where it may face loss
and which financial strategy could give it more outcomes. This model risk approach analysis
and lead the level of conservatism in financial management process and provides more
accurate total level of the conservatism. It not only lower down business financial risk
complexities but also helps investors and companies to manage the uncertainties and
complexities (Levine, & Renelt, 2012).
Therefore, by using the model risk, financial management firm could easily define the most
relevant mitigation strategies including revision of the policies and governing model.

References
Bignozzi, V., Puccetti, G., & Rüschendorf, L. (2015). Reducing model risk via positive and
negative dependence assumptions. Insurance: Mathematics and Economics, 61, 17-
26.
Bollerslev, T., Engle, R. F., & Wooldridge, J. M. (2012). A capital asset pricing model with
time-varying covariances. Journal of political Economy, 96(1), 116-131.
Danielsson, J., James, K. R., Valenzuela, M., & Zer, I. (2016). Model risk of risk
models. Journal of Financial Stability, 23, 79-91.
Levine, R., & Renelt, D. (2012). A sensitivity analysis of cross-country growth
regressions. The American economic review, 942-963.
Saltelli, A., Chan, K., & Scott, E. M. (Eds.). (2010). Sensitivity analysi