Financial Risk Management/ Risks for Iron Ore Producers Centre

Financial Risk Management/ Risks for Iron Ore Producers Centre*** IMPORTANT ***
In completing this assignment, you must not in any way use the work of any other person and present it as your own. You must always clearly and accurately acknowledge the source of any material you use. In particular, you must NOT without clear and accurate acknowledgement:
• Copy material (including audio-visual or computer based material);
• Use another person’s concepts, results, or conclusions; or
• Summarise another person’s work.
To avoid others copying your work without permission, take care never to share your spreadsheet with another student and never supply another student with your written work (whether in draft or final form). If you infringe these rules or encourage or assist another person to infringe them, severe penalties can apply (see Student Handbook on plagiarism).
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The core criteria used to assess this Assignment are:
Criteria
Knowledge and understanding:
Understanding of key ideas in FRM syllabus, including mathematical and statistical concepts.
Application:
Ability to apply theoretical ideas, frameworks and quantitative risk models in practice and in a critically reflective way.
Reasoning and analysis:
Ability to analyse, use critical reasoning and principles to formulate a position, balancing theory and personal reflection.
Professional literacy:
Understanding of the risk management profession including recent issues, trends, cases and special terminology.
Communication and presentation:
Ability to communicate complex technical concepts clearly and effectively in writing.
Research:
Ability to explore a complex issue by finding and absorbing relevant documents, assessing competing view-points and ultimately forming an opinion on the issue.
Some Research Suggestions
IBIS Industry Reports. Go to university library website. Follow the link to Databases (in Multisearch). Search for ibisworld and once you are in this database, do a search on ‘iron ore’. You should see some highly relevant industry reports.
De Mello, L., Sheedy, E. and Storck, S. (2015), A Practical Guide for Non-Financial Companies When Modeling Longer-Term Currency and Commodity Exposures. Journal of Applied Corporate Finance, 27: 89–100.
Commodity derivatives, markets and applications by Neil Schofield, published by Wiley Finance, available in the university library as an e-book.
Commodities and commodity derivatives by Helyette Geman, published Wiley Finance, available in the university library as an e-book.
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Risks for Iron Ore Producers
Your company has invested in a joint venture which is an iron ore mine called Orefull JV operating in Australia. Orefull produces 2 million tonnes of iron ore every month.
In your model you should make the following assumptions:
• You will be selling iron ore at the spot price, expressed in USD per tonne.
• The USD revenues are converted to AUD at the prevailing spot rate.
• We will assume fixed costs of AUD 15 million per month.
• Variable costs are assumed to be AUD 45.00 per tonne.
• The quantity of iron ore produced each month is constant, except for the fact that one month in 20 there are significant production problems that reduce the volume sold to only 1 million tonnes of iron ore.
1. As explained above, there is some degree of production uncertainty. Do you think that changes in Orefull’s production are likely to have any impact on the spot price of iron ore? Explain. 3 marks
2. Assuming that this month the iron ore price is USD 50.00 per tonne and the exchange rate is AUD/USD 0.7500, calculate earnings (EBITDA) for Orefull for the month in AUD (assuming no production problems). 2 marks
Examine the assignment data file which you may download from the course iLearn site (under Assignment). Here you will find monthly price data for both iron ore and the AUD/USD. Log returns have been calculated for you.
3. Complete the following table:
Iron Ore returns in USD
AUD/USD returns
Volatility per month
(using all available data)
Volatility per annum
(using all available data)
Mean per month (using all available data)
Mean per annum (using all available data)
5 marks
4. Assuming normality of returns, what is the probability that in any given month iron ore returns in USD will be lower than -28%? 3 marks
5. Based on the empirical data, what is the actual probability that in any given month iron ore returns in USD will be lower than -28%? Compare with your answer in question 4 and discuss the implications. 3 marks
6. Calculate the correlation between iron ore returns and AUD/USD returns using all the available data and discuss the implications for Orefull. 4 marks
7. Simulate 1000 paths of monthly prices for the next 12 months for iron ore in USD and the AUD/USD assuming that: 4
• the mean of log returns for both series is zero,
• the volatility of the iron ore returns is 12% per month,
• the volatility of the AUD/USD returns is 4% per month, and
• the two series have a correlation of 0.5.
For each path, calculate the earnings each month. For the first path you should show all your workings (no other calculations to be presented).
8. For each path, calculate the total earnings over the next 12 months (no need to adjust for time value). Hint: to simulate production volumes, use the ‘=if’ function with rand() as part of your logical test. Think about drawing a random number from a uniform distribution. If the random number falls below 0.05, volume = 1 million. If it is greater than or equal to 0.05, volume = 2 million.
Now analyse the 12 month earnings across all the simulated paths, calculating 95th percentile, 50th percentile, 5th percentile and EaR with 95% confidence. (Hint: you can use the percentile function in Excel)
Each time you hit the F9 button, Excel will recalculate everything with a new set of random numbers. Try doing this at least ten times and see how the EaR changes for different samples. To reduce sampling error, take an average across the ten different samples i.e. you now have a total of 10,000 Monte Carlo Simulations.
Repeat your analysis, this time for the case of production certainty i.e. 2 million tonnes of ore every month.
Complete the following table using the average across the ten samples. Discuss the effect of sampling error and production uncertainty on the distribution of outcomes:
Production Certainty
Production Uncertainty
a. 95th percentile
b. Median or 50th percentile
c. 5th percentile
d. Earnings at Risk (with 95% confidence)
15 marks
9. You consider hedging by selling iron ore and purchasing AUD forward in the OTC market. Assume that you can lock in the current spot prices for all future delivery months i.e. no forward premium/discount.
Calculate the minimum variance hedge ratio both for the case of production certainty and uncertainty. Discuss your findings.
10 marks
10. A banker comes to Orefull with the following recommendation: ‘You should hedge AUD/USD but not the iron ore price. We suggest this because we think that both the iron ore price and the AUD are likely to rise. This way you can protect against an adverse move in the AUD but benefit from the expected iron ore price increase’.
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If implemented, how would this recommendation affect the Earnings-at-risk of Orefull? Explain using appropriate reasoning and analysis.
10 marks
11. The notes for Topic 3 emphasise the importance of parameter assumptions for risk modelling. Test some other parameter assumptions to see how your risk analysis is affected and discuss the implications of your analysis. You should provide justification for any parameters you test.
10 marks
12. Consider a real-world iron ore mine operating in Australia i.e. without the simplifying assumptions we have made above. What are the ten most important risks facing iron ore producers? Describe each of them briefly and rank them in order of importance. To answer this you will need to do some research into the iron ore industry.
10 marks
13. Based on your research, your analysis and what you have learned in the FRM classes/notes, what recommendations would you make for both the analysis of risk and the treatment of risk in a real-world iron ore mine?
25 marks
Total 100 marks
Further Guidance
Research: This is a research assignment. You are expected to access multiple sources of information and use appropriate referencing. Avoid using extensive quotes from source documents (even if referenced); try to put ideas in your own words as much as possible. See further guidance on the FRM iLearn site under the Assignment tab.
Format: The assignment will be graded (in part) on your ability to communicate clearly and succinctly using non-technical language. Think very carefully about how you present your material. Assume a business audience i.e. someone who works in the finance industry but is probably not quantitatively gifted. Make your presentation inviting and easy to read, with clear and concise language. Essay format is probably not the most effective way to reach a business audience. Think about headings, bullet-points, tables, diagrams etc. 6

Iron ore spot Price in US$ per tonne AUD in terms of USD (spot)
6/16/2008 181 0.9394
7/16/2008 190 0.9786 4.9% 4.1%
8/16/2008 178 0.8639 -6.5% -12.5%
9/16/2008 135 0.7905 -27.7% -8.9%
10/16/2008 88 0.6681 -42.8% -16.8%
11/16/2008 72 0.6568 -20.1% -1.7%
12/16/2008 78 0.6697 8.0% 1.9%
1/16/2009 82 0.6731 5.0% 0.5%
2/16/2009 85 0.6516 3.6% -3.2%
3/16/2009 66 0.6564 -25.3% 0.7%
4/16/2009 63 0.7253 -4.7% 10.0%
5/16/2009 68 0.7591 7.6% 4.6%
6/16/2009 76 0.7929 11.1% 4.4%
7/16/2009 85 0.7982 11.2% 0.7%
8/16/2009 109 0.843 24.9% 5.5%
9/16/2009 85 0.8622 -24.9% 2.3%
10/16/2009 90 0.9222 5.7% 6.7%
11/16/2009 105 0.9349 15.4% 1.4%
12/16/2009 109 0.8993 3.7% -3.9%
1/16/2010 134 0.9272 20.6% 3.1%
2/16/2010 130 0.8937 -3.0% -3.7%
3/16/2010 143 0.9152 9.5% 2.4%
4/16/2010 180 0.9304 23.0% 1.6%
5/16/2010 176 0.8944 -2.2% -3.9%
6/16/2010 153 0.8645 -14.0% -3.4%
7/16/2010 125 0.8757 -20.2% 1.3%
8/16/2010 155 0.8924 21.5% 1.9%
9/16/2010 147 0.934 -5.3% 4.6%
10/16/2010 159 0.995 7.8% 6.3%
11/16/2010 165 0.9864 3.7% -0.9%
12/16/2010 174 0.9864 5.3% 0.0%
1/16/2011 181 0.9964 3.9% 1.0%
2/16/2011 195 0.9998 7.5% 0.3%
3/16/2011 172 0.991 -12.6% -0.9%
4/16/2011 189 1.0524 9.4% 6.0%
5/16/2011 188 1.0554 -0.5% 0.3%
6/16/2011 181 1.0527 -3.8% -0.3%
7/16/2011 182 1.0703 0.6% 1.7%
8/16/2011 185 1.0467 1.6% -2.2%
9/16/2011 188 1.0345 1.6% -1.2%
10/16/2011 172 1.0192 -8.9% -1.5%
11/16/2011 150 1.0086 -13.7% -1.0%
12/16/2011 143 0.9985 -4.8% -1.0%
1/16/2012 148 1.0278 3.4% 2.9%
2/16/2012 148 1.0683 0.0% 3.9%
3/16/2012 149 1.0536 0.7% -1.4%
4/16/2012 151 1.0331 1.3% -2.0%
5/16/2012 140 0.9915 -7.6% -4.1%
6/16/2012 137 1.0015 -2.2% 1.0%
7/16/2012 137 1.0234 0.0% 2.2%
8/16/2012 120 1.0483 -13.2% 2.4%
9/16/2012 106 1.0579 -12.4% 0.9%
10/16/2012 118 1.0264 10.7% -3.0%
11/16/2012 124 1.0326 5.0% 0.6%
12/16/2012 128 1.054 3.2% 2.1%
1/16/2013 150 1.0564 15.9% 0.2%
2/16/2013 156 1.0366 3.9% -1.9%
3/16/2013 137 1.0376 -13.0% 0.1%
4/16/2013 142 1.0362 3.6% -0.1%
5/16/2013 130 0.9861 -8.8% -5.0%
6/16/2013 116 0.9581 -11.4% -2.9%
7/16/2013 128 0.9194 9.8% -4.1%
8/16/2013 141 0.9147 9.7% -0.5%
9/16/2013 137 0.9324 -2.9% 1.9%
10/16/2013 134.5 0.9525 -1.8% 2.1%
11/16/2013 137 0.9344 1.8% -1.9%
12/16/2013 138 0.8954 0.7% -4.3%
1/16/2014 132 0.8814 -4.4% -1.6%
2/16/2014 123 0.8996 -7.1% 2.0%
3/16/2014 111 0.901 -10.3% 0.2%
4/16/2014 116 0.9378 4.4% 4.0%
5/16/2014 101 0.9354 -13.8% -0.3%
6/16/2014 90 0.9412 -11.5% 0.6%
7/16/2014 98 0.9337 8.5% -0.8%
8/16/2014 94.5 0.9332 -3.6% -0.1%
9/16/2014 87 0.9007 -8.3% -3.5%
10/16/2014 83 0.8788 -4.7% -2.5%
11/16/2014 76 0.8691 -8.8% -1.1%
12/16/2014 68.5 0.8223 -10.4% -5.5%
1/16/2015 70 0.8238 2.2% 0.2%
2/16/2015 64.5 0.7791 -8.2% -5.6%
3/16/2015 58 0.7642 -10.6% -1.9%
4/16/2015 51 0.7743 -12.9% 1.3%
5/16/2015 62 0.8043 19.5% 3.8%
6/16/2015 63.5 0.7754 2.4% -3.7%

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