mining risk and valuation

7/27/2019 Mining risk and valuation

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Dynamic DCF / Real Optionapplications in the Mining IndustryRichard Crosson, PartnerMichael Samis, Associate Partner

September 2012

Americas Mining and Metals Forum McLean, VA, USA


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Page 2 Dynamic DCF/ Real option applications Americas Mining and Metals Forum 20122012 EYGM Limited. All Rights Reserved.

Agenda

Refresher on Dynamic DCF / Real Options

Dynamic modelling uncertainty and non-linearities

Case study 1 Commodity-linked debt

Case study 2 Cash flow risk effects of mining tax

Concluding comments


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Dynamic Discounted Cash Flow (Dynamic DCF) and Real Option (RO) are NPVcalculations that are increasingly being used for financial reporting and investmentdecision-making in the mining industry.

In the 2011 Americas Mining Forum, we explored applications of:

Dynamic modelling;

RO discounting; and

Management flexibility.

Dynamic DCF and Real Options Refresher


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There are three reasons you may want to extend the economic and risk analysis of astatic DCF model through the introduction of Dynamic DCF / RO.

Reason 1: A static DCF model can provide a cash flow estimate that is unacceptablybiased due to flexibility or finance / taxation cash flow non-linearities.

Reason 2: The risk adjustment contained in a standard DCF discounting formula maybe unable to adequately recognize how risk varies during the investment.

Reason 3: Standard sensitivity or scenario analysis does not fully communicate projectrisk characteristics so that more sophisticated tools are required.

Moving beyond a static DCF model will be motivated by the impact of these concernson your investment analysis.

Note that a Static DCF model is always your starting point in an investment analysis.

Dynamic DCF and Real Options Why question the use of static DCF models?


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There are a number of project and economic factors that influence mining investmentanalysis. These factors can be recognized in Dynamic DCF / RO cash flow model.

These project and economic factors include but are not limited to:

Metal price uncertainty where expectations are continually revised and may revert to a long-term equilibrium level.

Dynamic and erratic risk variation over the life of an investment due to changes in metal grades,

increasing unit costs, changes in financing and taxation, and switching mining techniquesamong other factors.

Design and operational flexibility that allow managers to change project operating policy inresponse to new information.

Contingent non-equity payouts to royalty holders, project financiers, and government which mayalso include dynamic interaction between various project stakeholders.

We will consider the effect of metal price uncertainty and contingent non-equity payoutsfor remainder of the presentation.

Dynamic DCF and Real Options Project and business factors influencing mining valuation


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The evidence that the mining industry is beginning to accept Dynamic DCF and ROmethods includes:

1) These methods have been used for the economic analysis in NI43-101 technical reports.

2) Two global mining companies are using these techniques for select investment decisions.

3) Dynamic DCF / RO analysis has appeared in financial reporting.

EY has been at the forefront of using Dynamic DCF / RO methods in the mining industry.

Dynamic DCF and Real Options Industry acceptance of alternative NPV techniques

Dynamic DCF / RO engagements at EY

Dynamic DCF / RO application 2008 2009 2010 2011 2012

Financial reporting X X

Tax analysis X X X

Project finance X X X X

Economic analysis for NI43-101 X X X

Investment decisions and studies X X X X X

Education X X X X X


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Concluding comments


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0

400

800

1200

1600

2000

2400

2800

3200

3600

4000

01-Jan-2012 31-Dec-2021 01-Jan-2032 31-Dec-2041 01-Jan-2052

Constantdollargoldprice($/oz)

Project time (year)

One simulated stochastic gold price path.

Expected AU price at 1/1/12 AU price of $1650/oz.

Expected AU price at 1/1/12 AU price of $2776/oz.Expected AU price at 1/1/32 AU price of $1374/oz.

10%/90% confidence bdy f or 1/1/12 forecast AU price.

10%/90% confidence bdy f or 1/1/22 forecast AU price.

10%/90% confidence bdy for 1/1/32 f orecast AU price.

Initial long-term expected price = $1650/oz

The price of precious metal may be modeled asa non-reverting process.

This is the same type of process used to modelstock price uncertainty in option pricing models.

Key uncertainty characteristics illustrated in theupper graph are:

Uncertainty increases with term (i.e. time in thefuture). Confidence boundaries continue to diverge.

A change in spot price result in a similar change infuture expectations. Price forecasts update inparallel manner after a price shock.

Graphing the historic gold spot price and

forward curves (Lower Graph) shows a parallelshift in forward curve as the spot price changes.This is visual evidence of non-reversion.

Most gold mining companies use flat expectationswhen forecasting gold price. Could marketinformation change forecasting outlook?

Metal price uncertaintyNon-reverting price models for precious metals

200

400

600

800

1000

1200

1400

1600

1800

2000

Jan-00 Jan-02 Dec-03 Dec-05 Dec-07 Dec-09 Dec-11 Dec-13 Dec-15

Spotandforwar

dprice($/oz)

Date and forward delivery date

Simulated gold price path with updated forecasts

Historic gold spot and forward prices


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Metal price uncertaintyReverting price models for base metals and energy

0

50

100

150

200

250

300

350

400

450

500

Jan-00 Jan-02 Dec-03 Dec-05 Dec-07 Dec-09 Dec-11 Dec-13 Dec-15

Spotandforwardprice(cents/lb)

Date and forward delivery date

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

01-Jan-2012 31-Dec-2021 01-Jan-2032 31-Dec-2041 01-Jan-2052

Constantdollarcopperprice($/lb

)

Project time (year)

Long-term expected price = $3.40/lb

One simulated stochastic copper price path.

Expected CU price at 1/1/12 CU price of $4.10/lb.

Expected CU price at 1/1/22 CU price of $4.89/lb.Expected CU price at 1/1/32 CU price of $2.64/lb.

10%/90% confidence bdy f or 1/1/12 forecast CU price.

10%/90% confidence bdy f or 1/1/22 forecast CU price.

10%/90% confidence bdy for 1/1/32 forecast CU price.

Simulated copper price path with updated forecasts

Historic copper spot and forward prices

Base metal and energy price movementsexhibit reversion whereby prices tend to movearound a long-term equilibrium level.

Prices revert to this level after a dislocation due tothe effects of supply and demand.

Discussions about copper price cycles support theidea of reversion.

A reverting copper process is displayed in theTop Graph where key characteristics are:

Price uncertainty initially grows and then saturatesas confidence boundaries become parallel due toreversion.

Impact of price shock declines with term as price

forecast reverts to long-term equilibrium level.

Historic copper spot and forward prices (LowerGraph) provide visual support for reversion asforward curve displays both contango andbackwardation.


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The terms of finance and taxation often include contingent payouts that affect equity,creditor and government cash flow in unexpected ways.

Government contingent payouts include net profit royalties, windfall taxes, corporate income taxthrough loss carry forwards and depreciation.

Creditor contingent payouts include equity participation, loan conversion features, and embeddedcommodity derivatives.

Static cash flow models have difficulty correctly calculating contingent cash flows andcan generate a misleading cash flow estimate.

The error generated with a static cash flow model will vary in importance depending on the project,application, and terms.

The problems associated with static DCF models can be reduced by introducing dynamicnumerical methods and the valuation / risk management concepts of advanced finance.

Finance theory has the ability to differentiate the equity, creditor, and government cash flowstreams based on each streams unique risk characteristics. Competing financing proposals canbe compared in a similar manner.

Financing and taxation Non-linear cash flows from government and finance


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Gold price range ($/oz) Sliding rate

< $850 0.5%

$850-$1000 1%

$1000-$1150 2%$1150-$1300 3%

$1300-$1450 4%

$1450-$1600 5%

>$1600 6%

Financing and taxation Brief example of contingent royalty cash flows

A gold mine produces 0.5 million ozs per year for 10 years at an expected gold price of$1200/oz. The mine pays ad valorem and sliding scale royalties.

Ad valorem royalty: 3% of gold revenues are paid directly to the government.

Sliding scale royalty: Graduated royalty rate based on gold price (see table).

Total cash flow estimate for each royalty holder is linked to numerical method.

Static model: Both royalties generate $180m over 10 years.

Simulation model: Ad valorem royalty receives $180m whereas the sliding scale royalty receives$223m over 10 years (almost 25% more).


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Concluding comments


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Case study #1: Pre-paid forward agreementGold-linked notes and embedded commodity derivatives

Gold-linked payments from Bank Co. toMining Co. in commodity-linked debt createfinancing costs that are unrecognized bystatic models.

Simulate metal price to obtain unbiased estimateof debt cash flows and calculate the true financingcost of commodity-linked debt.

Issue: Solution:

An investment bank (BankCo) is providing $75 million through a pre-paid forward

agreement to a junior mining company (MinCo) for development purposes. BankCo receives 88,875 ounces of gold over a 5 year period. However, BankCo has also

agreed to make additional cash payments when the gold price is above $1000 per ounce.

The company news release informing investors of the deal states:

The transaction has an IRR of 11%. The bank will also make additional cash payments whenthe gold price moves above certain levels so the company can participate in higher gold prices.

The primary investment valuation characteristics are:

Non-reverting gold price: Gold price uncertainty continues to grow through time.

Contingent BankCo payment: The link between the gold price and BankCo paymentalters agreement cash flows such that financing costs may be higher than expected.


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The monthly gold delivery schedule toBankCo is:

Months 1 to 19: 1625 oz/month



Value of delivery is the amount of gold

multiplied by the delivery date spot price.

The contingent monthly payment fromBankCo to MinCo is linked to the goldprice:

Gold price Payment per delivered oz

< $1000: $0$1000 to $1300: AU price -$1000

>$1300: $300

0

500

1000

1500

2000

2500

0 10 20 30 40 50 60

Amountofgolddelivered(oz)

Gold delivery month

Monthly gold delivery amount

0

50

100

150

200

250

300

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400

700 800 900 1000 1100 1200 1300 1400 1500 1600

Contingentpaymen

tperozdelivered($)

Gold price ($/oz)

Contingent payment per AU oz delivered

Monthly gold delivery

Contingent BankCo payment

Case study #1: Pre-paid forward agreementGold delivery schedule and bank gold repayment


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Many organizations would use a static DCF cash flow model to analyse the forwardagreement. This static cash flow model outlines MinCo cash outflows and inflows.

Transaction cash flows are summarized on an annual basis.

This static cash flow model estimates that the cost of finance for MinCo is 11%.

Year 0 1 2 3 4 5

Gold price ($/oz) 1285 1305 1342 1381 1421 1461

Gold delivered (ozs) -19500 -20625 -22200 -16950 -9600

Cash flows ($ million)

Forward sale -25.445 -27.698 -30.656 -24.016 -14.028

Bank payment 5.802 6.188 6.660 5.085 2.880Bank forward prepayment 75.000

Net agreement 75.000 -19.643 -21.511 -23.996 -18.931 -11.148

MinCo cost of finance (IRR) 11%

Notes: 1. Gold price is an average of the forecast price over the year. Gold delivery amounts are the cumulative amount for the year.

2. Transaction IRR is based on monthly cash flows. IRR for annual cash flows is 9% due to all cash flows occurring at year end.

Case study #1: Pre-paid forward agreementStatic cash flow calculation


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0

5

10

15

20

25

30

0 1 2 3 4 5

BankCo.

netcashflow

($million)

Year

Simulated cash flow Static cash flow model

Simulation is required to recognize impact of contingent BankCo payment on MinCosnet transaction cash flows.

Simulate gold price with initial spot price of $1285 and contango forward structure.

Non-linearity contained in BankCo payment results in the simulated BankCo net cashflows being lower than those estimated by the static model.

Graph displays the annual static / simulated cash flows. Static cash flows are outlined by theBLUE line; simulated cash flows are displayed by GREEN line.

Annual BankCo net cash flow Simulated BankCo cash inflows total

$104.1m compared to static cash flowestimate of $95.2m Transaction IRR is 16%with simulation.

Difference between average annual cashflows of each model is $1.8m.

Bias in the static cash flow model under-estimates BankCo net cash inflow by $9mand IRR by 5%.

Case study #1: Pre-paid forward agreementSimulated expected BankCo net cash flows


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Option pricing methods relying on the forward curve can be used to estimate thetransaction NPV and opportunity cost of capital to BankCo.

Simulation price model for expected transaction cash flow in previous slide is adjusted to reflectforward curve.

The transaction option pricing NPV is negative $11.2 million for MinCo. This resultimplies that an additional $11m in upfront capital (i.e. $86m instead of $75m) could beprovided before transaction risk and reward is balanced (i.e. transaction NPV is ZERO).

Transaction componentOption pricingNPV ($ million)

Gold forward sale 98.0

BankCo contingent payment 14.4

Default recovery 2.6BankCo initial payment 75.0

Transaction NPV to BankCo 11.2

The BankCo opportunity cost ofcapital is calculated by findingdiscount rate that equates NPVsof the option and the expectedcash flow models.

The BankCo opportunity cost of

capital may be as low as 8%.Transaction spread for BankComay be 8% (16% cost of financeminus 8% cost of capital).

Case study #1: Pre-paid forward agreementOption pricing valuation of BankCo cash flows


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The cash flow impact of contingent financing terms may not be appropriatelyrecognized in a static cash flow model which leads to biased estimates.

In this case, a contingent payment to MinCo results in the static cash flow underestimating thetransactions average annual cash flows by $1.8m. Simulation shows the MinCo cost offinance or transaction IRR is 16% and not 11%.

Option pricing or derivative methods can provide further insights into the transactionsrisk and return characteristics that are not apparent in a static cash flow model.

The construction of a Option pricing model with market information suggested that the trueopportunity cost of capital for BankCo may be as low as 8% and that the BankCo transactionNPV is $11.2 m.

Mining companies may find the use of simulation and market-based methods helpfulwhen judging the relative merits of competing financing arrangements.

These techniques provide better understanding of the true cost of contingent financing terms

which may useful in negotiations between equity and creditors.

Case study #1: Pre-paid forward agreementConcluding comments


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Concluding comments


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Case study #2: Risk effects of mining taxTax increases may generate unacceptably high cash flow uncertainty

Host governments may look to miningprojects for additional revenues by imposingadditional mining taxes without consideringthe impact on project risk.

Simulate metal price to demonstrate howchanges in mining taxes can increase projectcash flow risk and decrease investment.

Issue: Solution:

Resource nationalism, higher metal prices, and government deficits are resulting in

additional mining taxes or royalties being levied in many jurisdictions. Increased taxes cause value transfer to government. New taxes can also result in a

large increases in cash flow risk also an important consideration when assessing theeconomic impact of new taxes.

The primary investment valuation characteristics are:

Metal price uncertainty: The leverage effect of taxes translates metal price uncertainty

into higher and varying levels of cash flow uncertainty over the life of the projectContingent tax and royalty payments: Mining tax regimes are structured so that equityinterests are fully exposed to operating losses while operating profits are reduced by taxpayments. Static cash flow models may over-value equity interests and under-value thegovernment interest.


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An interesting case study is to compare the economics of pre-feasibility stage projectunder varying tax regimes and in particular to look at the differential impact of taxeson low-margin, medium-margin and high-margin projects.

The three projects examined have the same development costs, operating costs, andproduction rates. They only vary by grade (ie, the same mine model, just altered to change

profitability).

Project cash flows and NPVs are modelled under the tax regimes of three countries:

Low Tax (all profits based tax)

Mid-Tax (mix of profits and revenue based taxes; lower proportion of revenue based taxes)

High-Tax (mix of profits and revenue based taxes; higher proportion of revenue based taxes)

Case study #2: Risk effects of mining taxAnalysing the impact of tax regime


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As shown, equity NPVs depend on both grade and tax regime. Calculating effective tax rates (defined as: NPV of taxes / NPV of pre-tax cash flow)

gives some insight into the how punitive a high tax countrys tax regime can be.

Effective tax rates in the High Tax country are 103% and 131% for the low and medium marginprojects - which means that government is taking over 100% of project value through taxation.

In the Mid Tax country, the low margin project is not economic but the medium margin project

is likely viable. In the Low Tax country, all three projects are attractive. Investment decisions are more likely

tied to project characteristics such as grade or operating costs than tax structure.

Case study #2: Risk effects of mining taxProject NPVs and effective tax rates

Equity NPV ($ million)

Project / tax

regime

High

tax

Mid

tax

Low

taxLow margin (273) 64 458

Medium margin (42) 364 818

High margin 281 771 1332

Effective tax rate (%)

Project / tax

regime

High

tax

Mid

tax

Low

taxLow margin 131% 92% 46%

Medium margin 103% 73% 38%

High margin 86% 62% 34%


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Tax rates and structure also impact cash flow uncertainty. We use the Coefficient of Variation (CoV) to measure cash flow uncertainty. This is defined

for each year as the standard deviation of cash flow divided by the expected cash flow

Higher taxes increase annual cash flow uncertainty (graph) particularly when the higher taxrate includes revenue-based taxes (like NSR royalties).

Average equity cash flow uncertainty in the High Tax country is approximately 20% higher. It isabout 8% higher in the Mid-Tax country.

Case study #2: Risk effects of mining taxCash flow uncertainty levels

60%

80%

100%

120%

140%

160%

Low margin Medium margin High margin

Equity

cash

flowC

oV

Project type

High-tax Mid-tax Low-tax

Average annual equity cash flow uncertaintyby project and tax regime


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Equity cash flow uncertainty for the Medium Margin project (graph) is also higher ineach year with the High Tax country. Cash flow uncertainty increases sharply once thetax shields are exhausted and grade begins to decline.

Case study #2: Risk effects of mining taxCash flow uncertainty levels

0%

50%

100%

150%

200%

250%

2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

Equitycashflow

CoV

Year


Annual equity cash flow uncertainty for theMedium Margin Project


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Another risk-based measure to compare tax regimes is the probability of incurring anegative equity NPV in the presence of metal price uncertainty.

The higher tax regimes increase the probability of having a negative NPV.

The High-tax country tends to have negative NPV probabilities that are 50% higher than theLow-tax country. For the Mid-tax country, these probabilities are more than 25% higher.

Case study #2: Risk effects of mining taxProbability of negative equity NPVs

Probability of negative equity NPVby project and tax regime

0%

10%

20%

30%

40%

50%

60%

70%

80%

L ow margin Medium margin High margin

Probability

of

negative

equityNPV

Project type



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While the impact of tax rates on equity NPV is apparent to investors, governments andthe public are not always aware of the share of project value that is being absorbed bytaxation.

Higher tax rates also increase equity cash flow uncertainty. When taxes are revenuebased, investor cash flow risk is higher and risk adjustments should be correspondinglygreater. The effect of tax structure isnt apparent from static DCF models.

Note that this observation also holds for non-government NSRs and streaming agreements.

In our example, ETRs are calculated based on static DCF models. In some countries,particularly those with limited loss carry forward provisions and revenue-based taxes,Dynamic models can show significantly higher ETRs due to better modelling of non-linearities.

Resource nationalism is currently #1 mining and metals risk. We are using this type ofanalysis to inform government of the impact on mining investment which imposition ofnew mining taxes might have.

Case study #2: Risk effects of mining taxConcluding comments


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Concluding comments


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Summing up

We have introduced Dynamic DCF and RO methods as an extension to Static DCFNPV calculations to improve the economic analysis of mining investments.

Static cash flow models have limitations that may create analytical biases.

Two practical examples were presented to illustrate the limitations of a static cash flowmodel and suggest situations when a more powerful analysis approach is required.

A gold-linked note example highlighted that embedded derivatives can alter debt returns in a

hidden manner. Simulation can be used to demonstrate how project risk may change with the introduction of

new mining taxes in addition to the project value captured by new taxes.

The diversity of mining investments requires valuation methods, such as Dynamic DCFand RO, that correct biases in static cash flow models and recognize the full variabilityof investment risk. Better investment decision-making will be the result.

Other Dynamic DCF / RO applications that we are working on include:

1) Strategic capital allocation tools for corporate mining portfolios

2) Financing and financial management policy linked to mining risk characteristics


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Professional background of presenters


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Richard Crosson, CA, CBVPartnerValuation & Business ModellingTel: +1 604 684 3371Mobile: +1 604 209 2800

Email: [email protected]

Richard is a Partner in Ernst & Youngs Transaction Advisory Services practice based in Vancouver. He is an experienced businessvaluation and transaction advisor. He is responsible for business and intangible-asset valuations across a broad spectrum of industries,for acquisitions, divestitures, financings, reorganizations, dispute resolution and financial reporting. With over 30 years experience inthe professional services, accounting and banking industries, he has worked with both public and private companies. Richard hasexperience providing fairness opinions, advising boards and special committees on transaction fairness issues, and acting as avaluation professional for litigation matters. Richards past roles with Ernst & Young have included leading our Canadian Valuation &Business Modelling practice and leading our transaction practice in the western United States. He holds a Bachelor of Commerce fromthe University of British Columbia and is a Chartered Accountant and Chartered Business Valuator.

Richard has extensive experience with valuations in the natural resource industries. He was the Engagement partner for a purchase

price allocation assignment relating to a $10 billion acquisition of a Tier 1 gold producer; the valuation of a $20 billion global basemetal mining company, including a portfolio of operating mines, exploration and development properties, and smelters; and theevaluation of many $1 billion plus world-class mining development projects. He has been the E&Y Valuation partner responsible foraudit Fair Value reviews for purposes of impairment testing and purchase price allocation for E&Y audit clients in the mining, aluminum,cement, forest products and many other industries, and for reporting under Canadian GAAP, IFRS and US GAAP.

Richard has led a number of major projects involving the use of Dynamic DCF and RO methods. He has published an industry paper andpresented at industry conferences on the subject. He has supervised the use of Dynamic DCF and RO evaluation methods in public NI43-101 technical reports and has worked with the senior management of numerous mining companies to explore and implement theuse of advanced valuation techniques for financial reporting and decision analytic purposes.

Professional background and qualifications:

Chartered Accountant, Canadian Institute of Chartered Accountants, 1981Chartered Business Valuator, Canadian Institute of Chartered Business Valuators, 1989Bachelor of Commerce, University of British Columbia, 1979


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Michael Samis, Ph.D., P.Eng.Associate partnerValuation & Business ModellingTel: +1 416 943 4487Mobile: +1 416 527 3421

Email: [email protected]

Dr. Michael Samis, P.Eng. is a leading Dynamic DCF and Real Option practitioner in the natural resource industries with more than 25years of mining experience. He has extensive professional experience valuing base and precious metals, diamond, and petroleumprojects with complex forms of flexibility and risk. His assignments have ranged from exploration stage to late-stage capitalinvestments and have also included analysis of project financing and contingent taxes on project economics. Mike has also presentedmore than 30 professional courses on advanced valuation at universities, natural resource companies, and professional organizationsworld-wide and has published or presented numerous valuation papers about flexible pushback development, multi-stage explorationprograms, windfall taxes, and the economic impact of project finance and hedging. Dr Samis is a registered Professional Engineer inOntario, Canada, and a qualified person for project valuation under NI43-101 guidelines. Mike holds a Ph.D. from the University ofBritish Columbia that combines mining engineering and finance.

Dr Samis is currently an Associate Partner (Valuation and Business Modelling) in the Toronto office of Ernst and Youngs TransactionAdvisory Service where he also values complex financial securities such as employee stock options, convertible debt with embeddedderivatives, contingent contracts, and derivatives linked to interest rates, commodities, and foreign exchange.

Professional background and qualifications:

University of British Columbia, Ph.D. in Mining Engineering and FinanceUniversity of the Witwatersand, MSc. In Mineral EconomicsUniversity of British Columbia, BSc. in Mining EngineeringProfessional engineer registered in Ontario, Canada

Qualified person for project evaluation under NI43-101 guidelinesMember of the 2012 Review Committee for the CIMVal Mine Valuation guidelines


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