Financial Analysis of Oyu Tolgoi Presented to: OPPORTUNITIES & CHALLENGES: THE CHANGING FACE OF 21ST CENTURY MONGOLIA: AN INTERDISCIPLINARY INTERNATIONAL SYMPOSIUM (IDIS 2014)

2. MONTE CARLO ANALYSIS
OT CASHFLOWS
ABSTRACT
This pedagogical project constructs a multi-iteration Monte Carlo
model (with Oracle Crystal Ball) purposed to value the Oyu Tolgoi,
LLC Copper and Gold mineral production. The analyst pursued a
novel approach, using neither constant mineral pricing, nor step
increments in mineral pricing. Rather, the analyst created the
simulation to follow fifty to sixty-year historic patterns of inflation
and individual mineral’s price volatility (Compound Annual Growth
Rates: CAGR) to project future volatility (modeling inflation within
the future mineral’s pricing structure). The analyst prefers this
approach because inflation occurs in the real world.
The Simulation produces: 1) projected aggregate (over prospective 30-
35 years) cash-flow; 2) DCF NPV projections; and finally 3)
appraises the overall Oyu Tolgoi Project and apportions estimates
for the principal shareholders: Erdenes MGL LLC, Rio Tinto (through
its holdings in TRQ), and Turquoise Hill (TRQ excluding Rio Tinto).
The model clearly shows that the Government of Mongolia is highly
favored by this investment scheme. The projections suggest GoM
will secure over 73% of the projected CashFlows, inclusive of
Royalties, Customs charges, Taxes, and FCFE.

3. What is the Monte Carlo
method?
 Developed by physicists in
the early 20th century to
account for complex
interactions in quantum
uncertainty;
 Randomly fluctuates underlying
parameters;
 The analysis constrains the
underlying parameters by some
reasonable and justifiable
measure (distribution parameters
or strange attractors).
By the 1960’s
economists and
financial engineers
had adopted the
Monte Carlo method.
 Merton Black Scholes
options pricing model, for
instance, predicts security
price using a continuous
lognormal distribution based
upon Brownian Motion with
Drift (a Monte Carlo random
fluctuation, constrained to the
curve parameters).
MONTE CARLO ANALYSIS

4. Uncertainty
surrounds
potential
outcomes
 Inflation rate;
 Securities prices;
 Commodities prices;
 Energy prices;
 Machine downtime;
 Productivity;
 Maintenance time;
 Etc.
Monte Carlo forces
the analyst to
study variables
and isolate
patterns (if any) to
project a
continuum of
potential
influence.
 Often based upon
historical data;
 Tuned to the analyst’s
knowledge of current
news, and the behaviors of
the market, the group, etc.
BENEFITS OF MONTE CARLO
FOR FINANCE

5. BENEFITS OF MONTE CARLO
FOR FINANCE
Monte Carlo produces a probable range of
outcome;
Some consider this superior to the traditional weighted
average calculation of: Best Case; Likely Case; Worst
Case scenario modeling.
Certainly, the model requires:
Excellent hard data from which to draw patterns;
 Skill and acumen in analysis;
Fine-tuning to correct for changes in trends and market
behaviors;
Careful interpretation of the model projections.

6. MONTE CARLO
STUDY OF COPPER AND GOLD PRICES
This analysis draws historical price data for the
major mineral commodities OT produces:
namely Cu and Au;
 Price data is readily available online from a host of sources:
World Bank, various universities, trade firms, etc.
This analysis isolates statistical trends (if any)
among the price data;
 The analyst focused on percent change in price over various
intervals: moving year; 5-year; 10-year; 20-year; and 40-year;
 Draw basic statistics references: means, deviations, and curve shape.
 Oracle Crystal Ball software includes functions to isolate the particular
curve shape parameters based upon data input.
Perform an honest check for correlation;
 The analyst looked for correlation to a metric of inflation, in
this case, the US CPI.

7. Historical Statistics
Cu, Au and US CPI

8. Historical Statistics
Cu, Au and USCPI

9. Historical Statistics
Cu, Au and USCPI

10. MONTE CARLO
STUDY OF COPPER AND GOLD PRICES
Historical Data (1952-2013) show high
correlation between the following
United States CPI and Cu Price r-Value: 0.78628
United States CPI and Au Price r-Value: 0.80976
CAGR US CPI and CAGR Au r-Value: 0.89198 (all years
from 1955 to 2013)
Correlation analysis compelled the analyst to
produce a regression equation.

11. Historical Statistics
Gold and US CPI

12. Historical Statistics
Smoothing the Curves with Log10

13. MONTE CARLO
STUDY OF COPPER AND GOLD PRICES
The correlation and plots of the changes in price of the
data suggest the following pattern:
 The mean (arithmetic average) of US CPI 40-year CAGR equals
4.477% (Accounting for all years from 1952-2013)
 One ought to temper this view of US inflation to include the 1970’s rise of OPEC as
a factor in world economy. By the late 1970’s, this contributed to a drastic rise to
US inflation; and many economists would argue that long-term US inflation is more
on the order of 3.00% to 3.50%.
 The mean (arithmetic average) of Cu $/lb 40-year CAGR equals
3.303% (Nominal basis and Accounting for all years from 1952-2013)
 In the recent past, nominal price of copper rose approximately 1.1% less than US
CPI.
 Fiber optic cable (reduced demand for copper in telephony)
 Recycled copper (mainly telephony and construction) produce a secondary source of supply.
 The mean (arithmetic average) Au $/oz 40-year CAGR equals 6.725%
(Nominal basis and Accounting for all years from 1952-2013)
 In the recent past, nominal price of gold rose approximately 2.25% higher than US
CPI.
 After revisions to the Bretton Woods accord, particularly after 1971, gold price corrected
itself away from a forced parity relationship to USD to reflect a price more in tune with true
supply and demand for gold to satisfy industrial use and requirements.

14. MONTE CARLO
STUDY OF COPPER AND GOLD PRICES
Problems with the correlation and parameters:
The parameters are based upon past data.
 A range of political entities and economic situations influenced the
unfolding of this past data. The future prices may unfold according
to different parameters.
 Future technology (influencing supply and demand), future
consumption (demand), and future production (supply) may unfold
differently than in the past 60 years, nullifying any implied or
observed correlation.
 Etc.
Solution to these problems?
Temper the parameters by applying current knowledge
and trends to improve the model.

15. COPPER MACROECONOMICS
1970’s-Present, telephony
created drastic changes
to Copper Supply and
Demand.
 These forces may be primarily
responsible for the decrease in
real (inflation-adjusted) copper
price.
Hybrid Electric Vehicles
and Plug-in Hybrid
Electric Vehicles are
again forcing a shift in
Copper Demand
 Electric vehicles use wound
copper in the motor for the
electro-magnet.

16. COPPER MACROECONOMICS
J.D. Power expects the
compounded annual growth
rate for global HEV sales
between 2010 and 2020 to be
13.8%. Still, despite the
expected rapid growth rate,
sales are projected to be just
3.88 million units in 2020, or
only 5.5% of the 70.9 million
passenger vehicles to be sold
by that year.
The United States is forecasted to
account for 53% of the global
HEV total, followed by Japan
(20%) and Europe (16%), while
the remaining 11% will be
spread among all other
countries.
(http://www.jdpower.com/sites/defa
ult/files/2010_WhitePaper_DriveGre
en2020.pdf)

17. APPLYING THE MONTE CARLO MODEL
FOR OT
Create a cashflow
budget
projection;
 Isolate the OT project
parameters for
prospective annual
production, costs,
taxes, and so forth;
 Assure that the coding
for the budget allows
that revenue and costs
drivers pull parameters
from a variable field for
dynamic calculation.

18. OT Production Projection
See "Oyu Tolgoi Project, Mongolia Integrated Development Plan" (August 2005), p. 32 and "2013
OYU TOLGOI TECHNICAL REPORT TURQUOISE HILL RESOURCES LTD." (March 2013), p. 54.

19. APPLYING THE MONTE CARLO MODEL
FOR OT
Run the Monte Carlo
model, randomly
fluctuating the
underlying
parameters;
 This model runs
approximately 250,000
iterations among each of
several hundred
variables.
Collect the results
and analyze the
projections.

20. WHAT THE MODEL SHOWS FOR OT
Analysis of the existing
Royalties and
Taxation regime:
 GoM assesses a 5% on
Royalties directly out of the
mineral sales;
 GoM assesses a 5%
Customs duty on Revenue
(less CoGs) minus Treatment
and Refining charges;
 GoM assesses a 25% tax on
EBT, following GAAP;
 GoM assesses a 10% Value-
Added Tax on EBT;
 GoM assesses a 20% With-holding
tax on EBT.

21. CONTINGENCIES AND OPEN ITEMS
Oyu Tolgoi has not
completed
financing for the
lucrative
underground sector
of the mine:
 Estimate of debt
placement of $5 Billion or
higher
 Add a debt service allocation
to the Cash Flow
Projections;
 Amend all cash-flows to the
stakeholders;
 Lower the weighted average
cost of capital; thereby
boosting the DCF valuation
 These graphics reflect a high
WACC of 12.913%, estimated
solely upon equity. The debt
component will reduce the
WACC overall.

22. This analysis is
incomplete
 After GoM and OT confirm
the debt placement and the
cost of debt, the analyst will
complete a more thorough
analysis of Aggregate
CashFlow and Discounted
Cash Flow (NPV);
 Five years of hard production
data will produce a greater
confidence in the predictive
power of the Monte Carlo
analysis
The analyst is anxious
(along with all
stakeholders) for
Mongolia’s prosperity
and productivity –
within all economic
sectors.
FINE-TUNING THE MODEL