Monte carloanalysisstakeholderinterestot tr_print

A MONTE CARLO ANALYSIS OF STAKEHOLDER

INTEREST IN OYU TOLGOI MINERALSLyndon Beharry

Ulaanbaatar, [email protected]

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 study first assembles raw data from historical pricing of the mineral commodities. The researcher analyzed these data for pricing trends, drawing Compound Annual Growth rates for several intervals: moving year, 10-year, 20-year, 30-year, and 40-year. As these statistics suggest a relationship to the U.S. dollar inflation rate embodied in the US Consumer Price Index,the analyst performed regression analysis. The regressions reveal strong correlations among the mineral prices and the CPI. The analyst used these data to construct probabilistic Monte Carlo multiple iteration models to forecast projected mineral prices, revenue, and cash-flow for the Oyu Tolgoi project.

The Simulation produces: 1) projected aggregate (over prospective 35 years) cash-flow; 2) Discounted Cash Flow, Net Present Value (DCF NPV) estimates; and finally 3) appraises the overall Oyu Tolgoi Project and apportions cash value estimates for the principal shareholders: Government of Mongolia represented by Erdenes MGL LLC; Rio Tinto (through its holdings in TRQ); and Turquoise Hill Resources, LTD (TRQ excluding Rio Tinto).1 The model clearly shows that the Government of Mongolia is highly favored by this investment scheme. The forecasts suggest GoM will secure over 73% of the projected CashFlows, inclusive of Royalties, Customs charges, Taxes, and Free CashFlow to Equity (FCFE).

Keywords: Copper, Economic Development, Economics, Finance, Financial Engineering, Gold, Inflation, Mining, Minerals, Mongolia, Monte Carlo, Oracle Crystal Ball, Oyu Tolgoi, US Consumer Price Index.

1 See Assumption 3 WACC (p. 15) and Data and Analysis of Data (pp. 19-22) for a comprehensive accounting of issues concerning the Weighted Average Cost of Capital, and the protocol for isolating and discounting the cash-flows.

1 of 44 | Lyndon Beharry | MonteCarloAnalysisStakeholderInterestOT_TR | 5/2/2014 12:25 PM

MonteCarloAnalysisStakeholderInterestOT_TR | Page 2 of 44 ContentsABSTRACT .................................................................................................................................................................................................................. 1

A CONCISE SUMMARY OF PROGRESS ON THE OYU TOLGOI PROJECT............................................................................................................................4

A BRIEF DISCUSSION CONCERNING: THE MACROECONOMICS OF COPPER (CU) IN PARTICULAR......................................................................................5

A BRIEF DISCUSSION CONCERNING: THE MACROECONOMICS OF GOLD (AU) IN PARTICULAR: .......................................................................................9

ASSUMPTIONS UNDERLYING THE MONTE CARLO FINANCIAL MODEL ........................................................................................................................11ASSUMPTION 1: INFLATION. ................................................................................................................................................................................. 11

Supporting data to Assumption 1:..................................................................................................................................................................12

ASSUMPTION 2: OYU TOLGOI PRODUCTION SCHEDULE.........................................................................................................................................14

ASSUMPTION 3: WEIGHTED AVERAGE COST OF CAPITAL FOR THE FIRM ...............................................................................................................15

ASSUMPTION 4: WEIGHTED AVERAGE COST OF CAPITAL FOR THE GOVERNMENT .................................................................................................15

ASSUMPTION 5: COSTS SCHEDULE AS FUNCTION OF REVENUE ..............................................................................................................................15

ASSUMPTION 6: GOVERNMENT TAXES AND ROYALTIES........................................................................................................................................15

ASSUMPTION 7: MAJOR SHAREHOLDER POSITIONS ...............................................................................................................................................16

OBJECTIVE AND METHOD.......................................................................................................................................................................................... 17

DATA AND ANALYSIS OF DATA ................................................................................................................................................................................. 19

APPX-TABLE 1: WORLD REFINED COPPER PRODUCTION INTERNATIONAL COPPER STUDY GROUP (2013) ................................................................24APPX-TABLE 2: GRAPHIC: CPISTATS ......................................................................................................................................................................25APPX-TABLE 3: GRAPHIC: CORRELATION ................................................................................................................................................................26APPX-TABLE 4: GRAPHIC: CUSTATS........................................................................................................................................................................27APPX-TABLE 5: GRAPHIC: AUSTATS .......................................................................................................................................................................28APPX-TABLE 6: PRIMARY DATA AND REGRESSION ANALYSIS FOR LOG10 COPPER PRICE..........................................................................................29APPX-TABLE 7: PRIMARY DATA AND REGRESSION ANALYSIS FOR LOG10 GOLD PRICE.............................................................................................30APPX-GRAPHIC01: SNAPSHOT01 OF THE MONTE CARLO MODEL: NON-ITERATION MODE .......................................................................................31APPX-GRAPHIC02: SNAPSHOT02 OF THE MONTE CARLO MODEL: ITERATION MODE................................................................................................32APPX-GRAPHIC03: SNAPSHOT03 OF THE MONTE CARLO MODEL: ITERATION MODE................................................................................................33APPX-GRAPHIC04: SNAPSHOT04 OF THE MONTE CARLO MODEL: ITERATION MODE................................................................................................34APPX-GRAPHIC05: SNAPSHOT05 OF THE MONTE CARLO MODEL: ITERATION MODE................................................................................................35APPX-GRAPHIC06: PLOT OF CPI, CU, AU WITH TRENDLINES (1952-2013)...............................................................................................................36APPX-GRAPHIC07: GOM DCF NPV VALUATION OF PROJECTED CASHFLOW (ROYALTIES, CUSTOMS, VAT, CORPORATE INCOME TAX,WITHHOLDING TAX, FREE CASHFLOW TO EQUITY: FCFE) ........................................................................................................................................37APPX-GRAPHIC08: GOM DCF NPV VALUATION OF CASHFLOW (ROYALTIES, CUSTOMS, VAT, CORPORATE INCOME TAX, WITHHOLDING TAX) ....38APPX-GRAPHIC09: PROJECTION OF AGGREGATE CASHFLOWS (FREE CASHFLOW TO EQUITY) TO THE OYU TOLGOI LLC EQUITY INVESTORS (ERDENES MGL LLC, RIO TINTO, TRQ EXCLUDING RIO TINTO) (ASSUMES INFLATION, LOM).................................................................................39APPX-GRAPHIC10: OVERLAY OF OYU TOLGOI LLC AGGREGATE CASHFLOW TO GOM AND TO THE OTHER EQUITY HOLDERS (RIO TINTO AND TRQEXCLUDING RIO TINTO) ............................................................................................................................................................................................ 40APPX-GRAPHIC11: OVERLAY OF GOM CASHFLOW (ROYALTIES, CUSTOMS, VAT, CORPORATE INCOME TAX, WITHHOLDING TAX) TO THE GOVERNMENT BY INSTANT YEAR.............................................................................................................................................................................. 41APPX-GRAPHIC12: OVERVIEW OF GOM CASHFLOW (ROYALTIES, CUSTOMS, VAT, CORPORATE INCOME TAX, WITHHOLDING TAX) BY INSTANT YEAR (ASSUMING CAGR MIMICS INFLATION)...........................................................................................................................................................42APPX-GRAPHIC13: OVERLAY PROJECTION OF GOM CUMULATIVE CASHFLOW (ROYALTIES, CUSTOMS, VAT, CORPORATE INCOME TAX,WITHHOLDING TAX) AT 5-YEAR INTERVALS (ASSUMING CAGR MIMICS INFLATION). ...............................................................................................43APPX-GRAPHIC14: PROJECTION OF GOM CUMULATIVE CASHFLOW (ROYALTIES, CUSTOMS, VAT, CORPORATE INCOME TAX, WITHHOLDING TAX)AT YEAR (ASSUMING CAGR MIMICS INFLATION). .....................................................................................................................................................44


MonteCarloAnalysisStakeholderInterestOT_TR | Page 3 of 44 List of Figures (Excluding Appendices)

Figure 1: Cu Constant 2010 Prices: The history of copper $USD/pound adjusted to 2010 CPI. ...............................................................................5Figure 2: Global Backhaul Technology Forecast.......................................................................................................................................................6Figure 3: Global Hybrid and Plug-in Sales................................................................................................................................................................7Figure 4: Cu Consumption, Production, GDP ...........................................................................................................................................................8Figure 5: Correlation Table .......................................................................................................................................................................................8Figure 6: 20th Century Greenback.............................................................................................................................................................................9Figure 7: Plot of CPI and Log10 of Gold..................................................................................................................................................................10Figure 8: A-1A Plot of CPI and Log10 of Copper and Gold Price Vs. Year ...........................................................................................................11Figure 9 Table A-1A: US CPI Statistics ..................................................................................................................................................................12Figure 10 Table A-1B: Cu Historic Statistics ..........................................................................................................................................................12Figure 11 Table A-1C: Au Historic Statistics..........................................................................................................................................................13Figure 12 Table A-1D: Coal Historic Statistics ......................................................................................................................................................13Figure 13 Table A-2A: TRQ: OT Production Schedule ..........................................................................................................................................14Figure 14 Table A-5A: LoM Cost Structure............................................................................................................................................................15Figure 15 Table A-6A: Principal Shareholders........................................................................................................................................................16Figure 16: Inputed CAGR Copper Price LoM.........................................................................................................................................................19Figure 17: Imputed CAGR Au Price LoM...............................................................................................................................................................20Figure 18 Table DA-1A Results Summary..............................................................................................................................................................21Figure 19 Table DA-1B Results Summary ..............................................................................................................................................................22


MonteCarloAnalysisStakeholderInterestOT_TR | Page 4 of 44 A CONCISE SUMMARY OF PROGRESS ON THE OYU TOLGOI PROJECT2

Mongolian geologist Garamjav Dondog first visited the Oyu Tolgoi site in 1983, observing evidence of greenish-blue (turquoise) staining. Garamjav recognized the color signature of copper content in the area. Corporate entities Magma Copper and its successor company BHP Billiton (BHP) explored the area and made several series of test drills at different depths to determine the ore content and grade. After inconclusive results on the test drills by 1999, BHP put the project on hold.

In 2000, Ivanhoe Mines (the predecessor to Turquoise Hill Resources, LTD.) purchased an option from BHP to acquire 100% rights to the exploration concession. Ivanhoe found the initial test drills encouraging enough (OTD-150 discovery) to expand the drilling program to create a full map of potential deposits in the zone. In March 2002, Ivanhoe released a broad audit of the resource potential at the Oyu Tolgoi site. By the end the same year, Ivanhoe had purchased full rights to the site from BHP. Ivanhoe’s test drills in 2003 suggested the far north of the site (named Hugo Dummett) is among the most substantial gold/copper porphyry systems. By 2009, additional deep test drills isolated the high-grade deposits at Heruga, Southern Oyu, and Heruga North.

Gaining an interest in Ivanhoe Mines in 2006, Rio Tinto invested in Ivanhoe equity and formed the Ivanhoe-Rio Technical Committee to engineer, construct, and operate Ivanhoe’s Oyu Tolgoi mining complex in the South-Gobi region.

Ivanhoe released the results of its analysis of the region’s minerals content in the Integrated Development Plan in 2010 (IDP-10), estimating annual production of 1.2 billion pounds of copper, 650,000 ounces of gold, and 3 million ounces of silver during the first ten years.

After Rio Tinto took a majority equity stake (51%) in Ivanhoe Mines, the entity renamed to Turquoise Hill Resources, LTD.

This change was synchronous with the Comprehensive Investment Agreement signed in Autumn 2009 among the principal partners in the controlling entity, Oyu Tolgoi, LLC: Government of Mongolia (GoM), Rio Tinto, and Turquoise Hill (TRQ). The Comprehensive Investment Agreement apportioned positions in OT as follows:

i) GoM (represented by state-owned entity Erdenes MGL LLC): 34%;

ii) TRQ (including Rio Tinto): 66%. TRQ holding apportioned as follows:(1) Rio Tinto (through 51% interest in TRQ):

33.660%; and(2) TRQ (excluding Rio Tinto): 32.340%.

Mining commenced in early 2013 when OT realized completion of the open pit mine and milling-concentrator complex. In that year, OT produced 290,000 tonnes of concentrate, resulting in 76,700 tonnes of copper (yield: 26.448%). The entity earned Gross Revenue of $51.6 million on 26,400 tonnes of concentrate.3 The analyst presumes this approximate 2013 Net Revenue allocation:

Copper Sold (lbs) Copper Price/lb Gross Revenue

15,393,275 3.35211323 51,600,000

Less TCs and RCs per lb 0.50 -7,696,637

Less Royalties (on Gross) 5.00% -2,580,000

Less CoGs (Operating Costs) 44.14% -22,775,724Less Customs (on Gross less

CoGS) 5.00% -1,441,214

Less VAT 10.00% -1,710,642

Operating Revenue on Cu 15,395,782

By early 2014, GoM and Rio Tinto have failed to finalize the arrangement to finance the structure to access the mother lode, the highly lucrative underground portion of the complex. Nevertheless, GoM hopes to complete this financing and begin the build-out by late 2014.

2 This matter is common published information. The analyst produced this synopsis from various newspaper sources and from the TRQ website here: http://www.turquoisehill.com/s/Oyu_Tolgoi.asp3 The TRQ website (http://www.turquoisehill.com/s/Oyu_Tolgoi.asp) states the following: “Net revenue for 2013 was $51.6 million on 26,400 tonnes of concentrate.” The analyst, presuming this to be in error, shows OT produced 51.6 Million in GROSS Revenue on 26,400 tonnes of concentrate (in lieu of net revenue). This correction fits the pricing and yield structure of 26.448% copper yield from copper at an average price of $3.35 per pound.


MonteCarloAnalysisStakeholderInterestOT_TR | Page 5 of 44 A BRIEF DISCUSSION CONCERNING: THE MACROECONOMICS OF COPPER (CU) IN PARTICULAR On its moving average compound annual growth rate (CAGR), the real price of copper (adjusted to CPI2010base = 1.00) showed consistent growth throughout the 1950s-1970s. Economic decline/stagnation in the 1970's and 1980's halted this trend. Real copper prices declined during the years 1981-1987, and again in 1991-2005 (CAGR basis). Copper's real price firmly rebounded in 2006, likely reflecting stronger demand in various consumer markets: robust construction and manufacturing in the BRIC countries, Singapore, and other Asian markets; Eastern Europe; and other Latin American states. This pricing shift tracks fairly well across the following analyses of real price changes: 1) Moving Year CAGR; 2) 10-Year CAGR; 20-Year CAGR; 30-Year CAGR; and 40-Year CAGR.4

Copper’s real price (i.e. adjusted for inflation CPI2010=1.00) retracted in the 1980’s. The data support one or more of the following possibilities: 1) new technologies replaced copper’s utility in one or more sectors; 2) technology for copper extraction contributed to a stronger supply side; 3) economic recession (either local or global) forced a reduction in demand; or 4) geologists uncovered new high-yield sources of copper ore.

The data show that fundamental market forces continually mold and reshape the copper industry. Prior to the 1980’s, longstanding US producers had become complacent in their extraction processes and production methods. New facilities in LDCs boasting innovative technology and extraction methods threatened and defeated US market hegemony in copper. These new and robust producers forced short-term boosts to supply, depressing prices. In the past, this dynamic was all the more problematic during periods of economic downturn, either global or internal to particular nation-states. Elder players were forced to act for survival, or expire. Some older production facilities locked down for lack of competitive ability. If terminal, these shutdowns totally erased these facilities’ contribution to aggregate supply. If short-term, the shutdowns reduced immediate and intermediate aggregate supply. Survivors adopted new methods, lowering their production costs in order to compete globally.5 As demand sprang back historically following recession, or more recently economic growth in the LDCs and in Eastern Europe, the market prices recovered with long-term CAGR comparable to US inflation rates.

Figure 1: Cu Constant 2010 Prices: The history of copper $USD/pound adjusted to 2010 CPI.The dark blue line plots the 5-year moving average in real copper price.The graph clearly shows the largely consistent downward trends in copper price during global slowdowns in the early through middle 1980’s, and again in the first few years of the new millenium.

4 The analyst tabulated primary data and statistics in the workbook: CB Monte Carlo Equity_Minerals_TRQ_Cumulative; in the worksheet: CuStats (See APPX-Table 4: Graphic: CuStats, p. 27) 5 Copper: Technology and Competitiveness, September 1988, US Congress Office of Technology Assessment NTIS order #PB89-138887


MonteCarloAnalysisStakeholderInterestOT_TR | Page 6 of 44 The US experience specifically highlights this trend. The nation lost its lead in copper production to Chile during the 1970s through 1980s. Through the 1990s, US copper producers surrendered additional market share precisely because the US production mines had high costs for extraction-purification combined with low yields from concentrate. Many US production facilities had failed to modernize and adopt new production methods: leaching precipitate (SX) and electro-winning (EW), for example, until the market forced them to adapt. And many US producers perished to market forces; but mostly for failure to lead the arenain upgrades and efficient production.6 Clearly, the industry favors 1) production mines boasting high copper yields from concentrate; significantly 2) with the newest technology for mining and production; and 3) with refining and purification in close proximity (vertical integration). The Oyu Tolgoi project in Mongolia has "1)" and "2)," but not "3)".

With regard to technological change creating a rival to copper, clearly fiber-optic technology caused areduction in demand for twisted copper wire for land-line telecommunications infrastructure worldwide.7 Fiber-optic is a superlative competitor for twisted copper wire both in terms of scale (volume of data transmission) and scope (energy costs to transmit over distance).

Compared to optical fiber, electrical copper cables suffer great attenuation (transmission loss over distance). Carriers also prefer optical fiber for broadband,8 for its immunity from electromagnetic interference, and for its relative low cost vis a vis copper wire.

From the 1980’s through the present, developed countries upgraded twisted copper wire with optical fiber, recycling substantial quantities of copper. As Less Developed Countries started building their communications infrastructure in the 1990’s and 2000’s, they jumped directly to cellular technology (mobile telecommunications). This leapfrog effect resulted in developing countries reducing their demand for twisted copper wire for landline telephony. LDCs erected microwave cellular towers for distance communication, bypassing traditional land-line communications infrastructure. These changes, and other macro-economic trends, notably recession, pressed a true reduction in copper demand and reduction in copper price during the 1980’s and 1990’s. Prices did not show significant recovery until toward the second half of the first decade of the new century. Fiber-optic and microwave transmission currently still threaten twisted copper wire in telephony and internet communications.9

Figure 2: Global Backhaul Technology ForecastSource: Mobile Backhaul: Fiber vs. Microwave, Case Study Analyzing Various Backhaul Technology Strategies, p. 2; quoting: Ethernet Backhaul Quarterly Market Tracker, Heavy Reading Research, July 2009

6 Copper: Technology and Competitiveness, September 1988, US Congress Office of Technology Assessment NTIS order #PB89-1388877 Requirement for transmission speed has placed a high demand for fiber optic to replace copper communications cable. …the fiber optic technology offers a significantly greater barrier to obsolescence. Recent interviews with various major U.S. cabling manufacturers and distributors (e.g., CSC, Accu-Tech and Graybar) confirmed cabling costs have risen in the last few months because of increased copper and plastics (FEP) costs. (2011) - See more at: http://www.ecmag.com/section/systems/law-supply-and-demand-copper#sthash.PIKXOpQJ.dpuf8 One optical fiber may carry 90,000 TV channels and approximately 3,000,000 full-duplex voice.9 Operators choose from among three physical mediums; copper, fiber or microwave. A recent report by Heavy Reading Research1 estimates that microwave represents nearly 50% of global backhaul deployments. …the report also finds that the use of microwave is not distributed evenly across nations. In fact, microwave is frequently deployed in developing markets and in emerging markets in which fiber is not available. Microwave is also frequently used in developed markets as an alternative to costly line-leasing services offered by Telecom incumbents. … copper networks, comprising nearly 20% of all backhaul deployments, will likely decrease due to their limited capacity support and their inability to scale in a cost efficient manner. Fiber will likely take the place of copper based wire-line connections, and increase its overall share (albeit not at the expense of Microwave). http://www.digitalairwireless.com/files/Fiber-vs-Microwave-White-Paper_1333235596.pdf


MonteCarloAnalysisStakeholderInterestOT_TR | Page 7 of 44 Post millennium pricing trends reflect strong copper demand among the BRIC countries10 – not for twisted copper wire in telephony, but for construction,11

including electrical wiring.12 During this period, secondary sources achieved a lower threshold cost of recycling copper even as those countries laid fiber-optic cable. Lower recycling costs for copper achieved profit from copper reclaimed from structure demolitions and renovations. As copper prices rebounded and recycling mechanisms became more profitable, the sector for recycled copper production grew, contributing another source of supply.13 This technology transition pushed the supply curve for pure copper toward the right, again suggesting a lower macroeconomic equilibrium price. The recycling mechanism may form a bound on upward price volatility.

As the mineral price increases beyond the recycling threshold price, recycled copper becomes a more viable alternative to mined product. The data shows that between 2007 and 2012, world refined production increased CAGR 2.370% (from 2007: 17,903,000 MT to 2012: 20,127,000 MT). Primary production from mined sources increased 1.758% year over year, while secondary refined production increased CAGR 5.509% during the interval.14

Secondary source contribution notwithstanding, the analyst posits a firm outlook for long-term copper demand. Technology will push this demand. As oil and petrol become scarcer and more expensive, consumers will increase demand for hybrid and electric vehicles, and manufacturers will ramp up the production. Electric and hybrid vehicles15 use wound copper in the motor.16 This will be a new and potentially robust demand for copper in the intermediate (10 to 20 years) and long-term (20 years and more), pushing the demand curve to the right.

Figure 3: Global Hybrid and Plug-in Sales17

10 While copper consumption in developed countries has remained stagnant or even fallen over the past five years, the appetite for copper in developing countries is growing at an astonishing pace. In China, Brazil and India for example, copper consumption is rising dramatically and contributing to an overall rise in world copper consumption. (2012) http://www.oracleminingcorp.com/copper/11 The largest demand for copper (37%) is from building construction, and as population growth and economic change have driven that market, the demand has put a strain on available supplies. Experts estimate that each new home incorporates over 400 pounds of copper. (MLW marketing document)http://asklug.web13.hubspot.com/Portals/213219/docs/SupplyDemand.pdf12 "The most important application of copper metal is electrical wiring. Nearly every electrical device relies on copper wiring because copper metal is highly conductive and inexpensive. These devices include electric clocks, stoves, portable CD players, and transmission wires that carry electricity. A large skyscraper contains miles of copper wiring for all its electrical needs. Older telephone lines are thick bundles of copper wires. And computers depend on circuit boards imprinted with minute copper pathways." Read more: http://www.chemistryexplained.com/elements/C-K/Copper.html#ixzz2vogCJO7613 The analysis sourced primary data from International Copper Study Group (worksheet: CuICSG). Secondary Refined Copper as a percentage of Total Refined Copper grew from 2007: 15.294% to 2012: 17.787% ; a compound annual growth rate of 3.067%.14 See APPX-Table 1: World Refined Copper Production International Copper Study Group (2013), p. 24 in this document. 15 The average new car contains 60.72 pounds (27.542 kg) of copper. Hybrid cars, which incorporate electric motors in conjunction with combustion engines, could lead to further rises in copper demand by this segment. A typical electric hybrid car might use around two times the current usage of copper in extra cabling and windings for electric motors. - See more at: http://www.ecmag.com/section/systems/law-supply-and-demand-copper#sthash.PIKXOpQJ.dpuf16 Amount of copper used in each motor depends on motor size, but various from 1.5 to about 9 kg (3.3 to 19.8 lbs.), General Motors Company (GM):http://media.gm.com/content/Pages/news/us/en/2011/Oct/1026_spark_elec_mtr/_jcr_content/iconrow/textfile/file.res/Electric_Motors_101.pdf,p. 4.17 Graphic image sourced from: https://stonybrook.digication.com/abbasmirza/GE_Watt_Station_a_Technology_Assessment


MonteCarloAnalysisStakeholderInterestOT_TR | Page 8 of 44

Figure 4: Cu Consumption, Production, GDPThe graphic displays the close relationships among demand, supply, and Per Capita GDP 1960-2008.Since 1960, Copper supply has kept pace with demand with only occasional gluts (early 1970’s, late 1990’s) and occasional relative scarcity (late 1970’s-1980’s, mid-2000’s). With regard to Copper, these data show strong positive correlations of average consumption (demand), average production (supply), and average nominal GDP. These data support the following:

1. Producers mine and recycle Copper on order. In other words, much of copper stock is managed to satiate forward orders. Because production is so costly (variable costs) and subject to demanding financing structures to service capital requirements, the mechanism aims to keep production in line with demand. The industry is focused upon lean production with little wiggle-room for over-stock.

2. Generally, as GDP rises, demand and supply rise in accordance with construction and manufacturing needs. In other words, as nation-states go through development and improve their standards of living, their economies tend to demand more copper for building infrastructure and to satisfy manufacturing needs.

Figure 5:Correlation

Table Year Mine

Production Refined

Production Refined

Usage

World Per

Capita GDP

Year 1.000 0.978 0.973 0.972 0.981Mine

Production 0.978 1.000 0.997 0.994 0.988

Refined Production 0.973 0.997 1.000 0.997 0.991

Refined Usage 0.972 0.994 0.997 1.000 0.992

World Per Capita

GDP 0.981 0.988 0.991 0.992 1.000

This correlation table (r-values) shows that overall refined production (including secondary sources), usage, and per capita GDP generally work in lockstep. R-values are close to 1.000.


MonteCarloAnalysisStakeholderInterestOT_TR | Page 9 of 44 A BRIEF DISCUSSION CONCERNING: THE MACROECONOMICS OF GOLD (AU) IN PARTICULAR:

Human society has maintained a long-term affair with gold (Au). Though soft and suitable for neither practical weapons nor armor, gold is scarce in the earth’s crust. It resists corrosion, rust, and tarnish. It maintains its luster under a range of conditions. For the scope of human history, civilization has imputed mystical qualities upon this precious metal. Furthermore, society has often looked to gold as a standard store of monetary wealth. The scarceness of the mineral and the labor to extract it create a benchmark for exchange value. Finally, businesses and manufacturers demand gold for various processes. Gold is a premiere conductor of electric current, with little signal lost to resistance over distance. And because of its anti-corrosive quality, gold is ideal for many engineering and manufacturing applications (consumer electronics; dental and medical; avionics, aviation, aerospace technology and aeronautical equipment including low atmosphere and space pressure suits; nano-technology, etc.).

In modern times, gold has continued its role as the premiere long-term warehouse of society’s wealth. Modernity, coincident with U.S. dollar hegemony, witnessed the transition of gold (and British £Pound) hegemony to U.S. dollar hegemony for international financial transactions.

For its initial 100-year run, the U.S. followed a bi-metallic (gold and silver) basis for currency reserve. But by the twentieth century, the U.S. Federal Reserve banking system (1914) pushed a gold-promissory system. Great Britain held dominion during the first two decades of the last century, but the Great Depression and its aftermath brought drastic changes to the world economic system.

By 1934, at the mid-phase of the Great Depression, to stave off an ultimate concentration of assets to an elite few, FDR’s administration amended the U.S. Federal Reserve Board’s promise to uphold gold parity. FDR revoked gold parity (except for foreign exchange). The administration also enacted laws prohibiting private ownership of substantial amounts of gold (hoarding). World War Two brought an end to the Great Depression.18

REDEEMABLE IN GOLD ON DEMAND AT THE UNITED STATES TREASURY, OR IN GOLD OR LAWFUL MONEY AT ANY FEDERAL RESERVE BANK. (1914)The U.S. federal government promises to redeem the greenback for gold.

THIS CERTIFICATE IS LEGAL TENDER IN THE AMOUNT THEREOF OF ALL DEBTS, PUBLIC AND PRIVATE. (1934)Fiat money. Though, the U.S. government aimed to create an “illusion” of gold. This graphic depicts a “Gold Certificate” from 1934, distinct from the greenbacks introduced that year.

Figure 6: 20th Century Greenback

18 The international pre-wartime and wartime economy: Germany, Japan, and U.S. increased industrial production, reduced unemployment and basically expunged the effects of depression.


MonteCarloAnalysisStakeholderInterestOT_TR | Page 10 of 44 In one sense, WWI and Versailles had left intact remnants of the Ancien Regime.19 Under this premise, WWII was merely a continuation of WWI; and European fascism served as the conduit to the whispers of Ancien Regime from the first two decades of the last century. Hitler’s fascist Nazi Germany finessed infrastructure works and industrial projects combined with pseudo-military and youth para-military endeavors to create near full employment and national pride among the German people. This recipe aimed to resurrect Germany from the deleterious impact of the Versailles Treaty and from the ruinous Great Depression. Nevertheless, even before its rise to power, the fascist Nationalsozialistische Deutsche Arbeiterpartei party had clearly stated its aim for war in Europe. Hitler’s Nazi party had stated its goals for Germany: land and gold, through war focused to restore German (Prussian) empire and hegemony in Europe.

World War II abandoned Europe and Asia to devastation. Yet, the war left U.S. infrastructure and military might wholly intact. Furthermore, WWII had provided an environment of high employment and heightened manufacturing capacity for U.S. business facilities (nearly 100% capacity). After WWII, American engineers and contractors (compensated in dollars) provided the backbone for rebuilding Europe and Asia. The international banking industries evolved to accept U.S. dollars for deposit.

This system created Euro-dollars (U.S. currency with credence and velocity in Europe and Asia). Post-WWII Bretton Woods Agreement (1944-1946) formally set up the World Bank and International Monetary Fund. Bretton Woods provided the basis for U.S. Dollar Hegemony. European nations pegged their currencies to the U.S. Dollar; and the U.S. contractually promised to redeem foreign exchange dollars for gold. The U.S. and its early leadership in EBRD, continual leadership in IBRD, and traditional play within ADB aimed to retain this hegemonic position in the international financial community.

But by the early 1960’s, the U.S. federal government realized that dollar hegemony had evolved such a high value of dollar denominated international trade, that the U.S. could not redeem its trade obligations in gold. Ultimately, in 1970-1971, President Nixon’s Administration formally announced that the U.S. would (could) no longer guarantee to redeem U.S. dollars for the gold the Federal Reserve Bank had formerly specified. Nevertheless, gold has continued to maintain a relationship with the U.S. Consumer Price Index even after 1971 when the U.S. announced a truism that had been in play for the prior two generations. In the post-WWII period characterized by US Dollar Hegemony, the price of gold is correlated to the U.S. CPI2010.

Figure 7: Plot of CPI and Log10 of Gold

This graphic plots the U.S. CPI and the log10 of gold’s average annual price by year (X-axis).

The line regression formula:Y = Gold Price Log10= 0.0276 X Year – 52.477

R2 = 0.8466Other correlation analysis returned an r-value of 0.809755.20

19 For the premiere treatment of this thesis, see Arno Meyer, Persistence of the Old Regime: Europe to the Great War, (New York, NY: Pantheon Books, 1981). ISBN 978-0-394-51141-220 See APPX-Table 3: Graphic: Correlation, p. 26 for the statistics and the derivation of the r-value.


MonteCarloAnalysisStakeholderInterestOT_TR | Page 11 of 44 ASSUMPTIONS UNDERLYING THE MONTE CARLO FINANCIAL MODEL

ASSUMPTION 1: INFLATION.

Historic patterns of inflation, price changes, and volatility will continue for the Life of the Mine (LoM) project. Entities transact business in a world where prices are subject to inflationary pressures and where governments and financial firms influence monetary trends by continually fluctuating money supply and velocity. Inflation influences all transactions.

The post-WWII world evolved to accommodate a two-pole socio-economic system centered upon the dominant military and economic powers: United States and Soviet Union. Even still, the U.S. dollar (open and exposed to broad-based international transactions) became the de facto unit for the majority of cross-border trade in goods and services.

Hence, by default, movements in U.S. inflation bear directly upon the market price of a wide variety of commodities where the trading community values these items in U.S. dollars. The collapse of the Soviet communist state system, the advent of the Euro, and the rise of mainland China as an economic dynamo have all brought queries regarding whether the U.S. dollar will continue to dominate international transactions. Nevertheless, the force of tradition and a general consensus to retain USD for economic stability continue to reinforce the reality of US dollar hegemony. The US dollar will likely continue to underlie international trade for the intermediate 10-25 year time horizon. The analyst believes that US dollar denominated inflation (referenced by the US CPI in the graphic) will likewise affect price changes among the mineral commodities: Copper and Gold.

Figure 8: A-1A Plot of CPI and Log10 of Copper and Gold Price Vs. YearThe line regression formula predicts CPI and the minerals prices as a function of time. (See APPX-Graphic06: Plot of CPI, Cu, Au with Trendlines (1952-2013), p. 36 for an enlarged graphic.)


MonteCarloAnalysisStakeholderInterestOT_TR | Page 12 of 44 Supporting data to Assumption 1:

Figure 9 Table A-1A: US CPI Statistics

CPIMoving Year

CAGR [Constant 2010 CPI:

Geometric Growth]

10-Year CAGR [Constant 2010 CPI: Geometric

Growth]


Growth]


Growth]


Growth]

Mean 3.580% 3.995% 4.408% 4.611% 4.477%Median 4.010% 3.278% 4.206% 4.792% 4.460%

1.085% 2.109% 1.462% 0.739% 0.139%Best fit Min-Extreme Lognormal Gamma Min-Extreme Beta

Oracle Crystal Ball Batch-fit feature isolates a Min-Extreme distribution for CPI Moving-Year CAGR (moving average), with the following characteristics: Likeliest=0.04072, Scale=0.00783.

The nominal prices of Copper and Gold are highly correlated with the US Consumer Price Index: respective correlation r value: 0.786 and 0.809.21 Since 1955, the United States Consumer Price Index (aggregate basket) has exhibited a mean annual value change of 3.580% with a standard deviation volatility of 1.085% following its Compound Annual Growth Rate (CAGR) over all periods (moving average accounting for all intervals from 1955).22

Nominal Copper price has exhibited a mean annual change of 3.198% with a standard deviation volatility of 1.782% following its Compound Annual Growth Rate (CAGR) on 30-year moving average basis: all 30-year periods (accounting for all intervals from 1952-1982, and subsequent 30-year intervals). (See the worksheet: CuStats for the determination.) Line regression analysis predicts a 2014 Cu price of $2.364 per pound. The Monte Carlo model for simulating productionrevenue will use these figures as the basis of variation going forward.23

Figure 10 Table A-1B: Cu Historic Statistics

CuNominal

Moving Year CAGR [Nominal Price: Geometric

Growth]

10-Year CAGR [Nominal Price:

Geometric Growth]


Geometric Growth]


Geometric Growth]


Geometric Growth]

Mean 4.668% 4.486% 3.383% 3.198% 3.303%Median 3.862% 3.842% 3.638% 3.319% 3.182%

3.142% 5.760% 2.412% 1.782% 0.966%Best fit Logistic Lognormal Weibull Weibull Lognormal

Oracle Crystal Ball Batch-fit feature isolates a Weibull distribution for 30-Year CAGR Cu (moving average), with the following characteristics: Location=-0.03449, Scale=0.07312, Shape=4.20631. The analyst completed trials with both Normal and Weibull distributions. This document assumes normal distribution, which produces slightly more conservative results for in price.

CuReal

Moving Year CAGR [Constant

2010 Price: Geometric Growth]

10-Year CAGR [Constant 2010

Price: Geometric Growth]







Mean 1.663% 0.544% -0.953% -1.339% -1.123%

Median 0.619% -0.238% -1.523% -1.297% -1.246%4.046% 6.450% 3.057% 2.172% 0.954%

21 APPX-Table 3: Graphic: Correlation, p. 26 22 APPX-Table 2: Graphic: CPIStats, p. 25 23 See APPX-Graphic06: Plot of CPI, Cu, Au with Trendlines (1952-2013), p. 36 for an enlarged graphic.



Figure 11 Table A-1C: Au Historic Statistics

AuNominal


Growth]


Geometric Growth]


Geometric Growth]


Geometric Growth]


Geometric Growth]

Mean 4.398% 6.765% 6.771% 6.603% 6.725%Median 5.440% 2.058% 6.914% 7.350% 6.184%

3.015% 8.998% 5.018% 1.936% 1.323%Best Fit Min-Extreme Gamma Beta Triangular LognormalOracle Crystal Ball Batch-fit feature isolates a Triangular distribution for 30-Year CAGR Au (moving average), with the following characteristics: Minimum=0.01166, Likeliest=0.08862, Maximum=0.08962. The analyst completed trials with both Normal and Triangular distributions. This document assumes normal distribution, which produces slightly more conservative results for in price.

AuReal











Mean 1.343% 2.602% 2.241% 1.900% 2.152%

Median 1.570% -0.785% 3.089% 2.196% 1.655%1.851% 7.499% 4.035% 1.463% 1.274%

Nominal Gold price has exhibited a mean annual change of 6.603% with a standard deviation volatility of 1.936% following its Compound Annual Growth Rate (CAGR) over the moving average of all 30-year periods (accounting for all intervals from 1952-1982, andsubsequent 30-year intervals). (See the worksheet: AuStats for the determination.) Line regression analysis predicts a 2014 Au price of $1,304.765 per ounce. The Monte Carlo model for simulating production revenue will use these figures as the basis of variation going forward.24

Nominal coal price has exhibited a mean annual change of 3.700% with a standard deviation volatility of 1.819% following its Compound Annual Growth Rate (CAGR) over the moving average of all 30-year periods (accounting for all intervals from 1965-1982, and subsequent 30-year intervals). (See the worksheet: CoalStats for the determination.)

Figure 12 Table A-1D: Coal Historic Statistics

CoalNominal


Growth]


Geometric Growth]


Geometric Growth]


Geometric Growth]


Geometric Growth]

Mean 2.304% 3.805% 3.912% 3.700% 3.868%Median 2.882% 1.464% 4.277% 4.612% 3.536%

2.590% 6.603% 4.049% 1.819% 0.780%Best Fit Beta Lognormal Weibull Min-Extreme LognormalOracle Crystal Ball Batch-fit feature isolates a Min-Extreme distribution for 30-Year CAGR Coal (moving average), with the following characteristics: Likeliest=0.04527, Scale=0.01315.

24 See APPX-Graphic06: Plot of CPI, Cu, Au with Trendlines (1952-2013), p. 36 for an enlarged graphic.



Figure 12 Table A-1D: Coal Historic Statistics

CoalReal











Mean -0.675% -0.242% -0.530% -0.926% -0.594%

Median -0.851% -2.131% -0.100% -0.620% -0.851%1.512% 5.454% 3.362% 1.489% 0.743%

ASSUMPTION 2: OYU TOLGOI PRODUCTION SCHEDULE

Oyu Tolgoi project will follow a production trajectory structure outlined in the Integrated Development Plan first assembled in 2005-2006 (albeit the phases are adjusted forward by 8.5 years). Oyu Tolgoiwill follow a production schedule closely similar to the following:

Figure 13 Table A-2A: TRQ: OT Production Schedule Mean Coefficient of Variation25

Cu Concentrate Per Annum ('000 DMT): Year0 149 7.450 5.000%Cu Concentrate Per Annum ('000 DMT): Year1 510 25.500 5.000%Cu Concentrate Per Annum ('000 DMT): Year2 576 28.800 5.000%Cu Concentrate Per Annum ('000 DMT): Year3 701 35.050 5.000%Cu Concentrate Per Annum ('000 DMT): Year4 971 48.550 5.000%Cu Concentrate Per Annum ('000 DMT): Year5 1162 58.100 5.000%Cu Concentrate Per Annum ('000 DMT): Years 6-10 1357.6 67.880 5.000%Cu Concentrate Per Annum ('000 DMT): Years 11-20 1079.7 53.985 5.000%Cu Concentrate Per Annum ('000 DMT): Years 21-40 994.75 49.738 5.000%

Cu Transit Cost/Lb (Incl. Treatment/Refine) 0.50 0.050 10.000%Cu Yield Percentage 0.32 3.200% 10.000%Au Production Per Annum ('000 Oz): Year0 76 3.800 5.000%Au Production Per Annum ('000 Oz): Year1 441 22.050 5.000%Au Production Per Annum ('000 Oz): Year2 653 32.650 5.000%Au Production Per Annum ('000 Oz): Year3 508 25.400 5.000%Au Production Per Annum ('000 Oz): Year4 440 22.000 5.000%Au Production Per Annum ('000 Oz): Year5 223 11.150 5.000%Au Production Per Annum ('000 Oz): Years 6-10 330.6 16.530 5.000%Au Production Per Annum ('000 Oz): Years 11-20 204.7 10.235 5.000%Au Production Per Annum ('000 Oz): Years 21-40 214.75 10.738 5.000%

25 The analyst forced a calculation of standard deviation to conform to the stated Coefficients of Variation. Production processes typically do not exactly follow budgetary projections. The analyst proposes that a well-managed business process should hold within a tolerance of 5.00% or lower CV. The analyst proposed a higher CV for transit (Ts and Rs) and for Cu Yield because these elements are beyond the direct control of the entity. Ts and Rs are subject to fluctuations in diesel and petrol for transit; Rs are subject the negotiated rates with the facility in China; yield is subject to the geology at different grades of ore at different stages of production.



ASSUMPTION 3: WEIGHTED AVERAGE COST OF CAPITAL FOR THE FIRM

Because Oyu Tolgoi, LLC has not finalized the debt placement, the analyst is presently unable to calculate the overall debt placement loading for the Project. Shareholders’ pending class action against TRQ alleging improprieties in accounting and other matters relevant to shareholder value could also significantly affect the specific cost of risk for the TRQ entity and the TRQ WACC. In sum, this analysis is able to use the best information presently available; but it does not yet account for certain extraneous risk factors includingsettlement costs arising out of potential litigation.

The Oyu Tolgoi TRQ Weighted Average Cost of (Equity) Capital (WACC26) is approximately 12.913%. The analyst derived this figure from the Capital Asset Pricing Model (CAPM) with the following inputs: 1) U.S. long-bond (30-year) Risk-Free rate is 3.550% (approximating the long-term inflation horizon); 2) long-term historic equities market return: 8.900%; 3) TRQ Beta: 1.750. (Note: the analyst chose the long bond to secure a firm hold onto the inflationary likelihood of future cash-flows from the project, i.e. a 30-year to 40-year time horizon.)CAPM: E(RTRQ) = RF + TRQ X [E(RM) – RF ]

E(RTRQ) is the expected return on TRQ investmentRF is the Risk Free rate

TRQ is TRQ betaE(RM) is the expected return on the equities market

E(RTRQ) = 3.550% + 1.75 X [8.900% – 3.550%] = 12.913%

ASSUMPTION 4: WEIGHTED AVERAGE COST OF CAPITAL FOR THE GOVERNMENT

The cash-flows from Oyu Tolgoi to the Mongolian government are so intrinsically tied to TRQ, the cash-flows to the government follow the same WACC as TRQ.

ASSUMPTION 5: COSTS SCHEDULE AS FUNCTION OF REVENUE

Oyu Tolgoi had proposed the following aggregated values in their report (Oyu Tolgoi Project, Mongolia; Integrated Development Plan; Executive Summary, p. 32 of 93)

Figure 14 Table A-5A: LoM Cost Structure

Millions% Rev

Y=F(Rev)Revenue 25,481 100.000%

Operations Expense 11,247 44.139%Capital Expenditure 3,948 15.494%

Depreciation 4,099 16.086%

The Monte Carlo financial simulation will use these data to approximate Operations 44.139%, CapEx 15.494%, and Depreciation/Amortization 16.086% as Functions of Revenue.

ASSUMPTION 6: GOVERNMENT TAXES AND ROYALTIES

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 protocol;GoM assesses a 10% Value-Added Tax on EBT;GoM assesses a 20% With-holding tax on EBT.

26 After GoM and Rio Tinto finalize the arrangement for the underground mining facility, the debt financing will improve leverage and produce a lower Weighted Average Cost of Capital for OT, LLC. At that time, the DCF calculations ill reflect higher value due to lower capitalization costs. At present, these calculations are based solely in equity.



ASSUMPTION 7: MAJOR SHAREHOLDER POSITIONS

According to the terms of the investment agreement, the principal partners apportioned share ownership in Oyu Tolgoi following this scheme:

Figure 15 Table A-6A: Principal Shareholders

1Government of Mongolia (GoM)

Controls 34% of OT, LLC through its entity: Erdenes MGL LLC27

34.000%

2Rio Tinto (through 51% Equity position in TRQ which controls

66% of OT, LLC)33.660%

3Other investors (through 49% Equity position in TRQ which

controls 66% of OT, LLC)32.340%

100.000%

27 The Government of Mongolia created the entity: Erdenes MGL LLC to efficiently manage its position in the project. By contract, this position retains 34.00% in the project, and it may not be diluted.


MonteCarloAnalysisStakeholderInterestOT_TR | Page 17 of 44 OBJECTIVE AND METHOD The multi-iteration Monte Carlo predictive model uses the foregoing assumptions and data to project forward pricing of the mineral commodities in an inflationary environment. The model aims to isolate the following:

Predictive analysis of the Cumulative Aggregate and of the Discounted Cash-flow (Net Present Value basis) of Free CashFlow to Equity to GoM, in an inflationary environment;Predictive analysis of the Cumulative Aggregate and of the Discounted Cash-flow (Net Present Value basis) of Royalties and Tax to GoM, in an inflationary environment;Predictive analysis of the Cumulative Aggregate and of the Discounted Cash-flow (Net Present Value basis) of Free CashFlow to Equity to the TRQ Equity holders, in an inflationary environment;Predictive analysis of the Cumulative Aggregate and of the Discounted Cash-flow (Net Present Value basis) of Free CashFlow to Equity to the Rio Tinto Equity holders, in an inflationary environment;Sensitivity analysis predicting the variables which most significantly impact the forecasted values;

The model randomly varies future volatility in price changes ( % price) based upon historic patterns (mean change and standard deviations, following an implied normal distribution) for the following items:

1. Copper price percent change: % price;2. Gold price percent change: % price.

The model randomly varies future Transportation, Treatment/Refinement, and Tariff charges following the historic pattern of price changes (mean change and standard deviations, following an implied normal distribution) in Copper.

The model randomly varies future Production (Assumption 2, above) around the stated means, and with deviation imputing the stated Coefficient of Variation.

Production Costs vary around the stated percentages of Revenue, and with a standard deviation returning an imputed Coefficient of Variation of 10.00% .

The model uses the CAPM WACC (presently Equity only) as the mean discount factor (Assumption 3 above). The model will vary the WACC around a standard deviation imputing a coefficient of variation of 10.00% .

The model will return frequency data and probability of outcome. Since forward prices assume price changes tied to historic patterns, the aggregated values are loaded for inflation.

The model draws approximately 275,000 iterations of randomly selected parameters across each of all variable fields (variables are restricted to the distribution, means, deviations, and/or limits described above in assumptions).

The model design follows these simple steps to calculate annual revenues and cost parameters for each of 35 years forward. For a visual inspection of the model, review snapshots of the model in Monte Carlo Iteration mode:28

1. Isolate the base year price (2013-2014) of the mineral commodities (the analyst defaults to the line regression formula for the base year, believing that the historic growth rates tempered by the particular distribution parameters in growth should catch potential noise and anomalies in current year and future year pricing)29;

2. Assign a Monte Carlo variable in Excel to increase the subsequent year price of the mineral according to its historic distribution parameters of price change (Assumption 1 above);

3. Assign a Monte Carlo variable in Excel to predict each year’s production of mineral according to the firm’s projection, subject to its historic distribution parameters of production (Assumption 2, above);

28 See examples of the model in action APPX-Graphic01: Snapshot01 of the Monte Carlo Model: Non-Iteration Mode, p. 31; APPX-Graphic02: Snapshot02 of the Monte Carlo Model: Iteration Mode, p. 32; APPX-Graphic03: Snapshot03 of the Monte Carlo Model: Iteration Mode, p. 33; APPX-Graphic04: Snapshot04 of the Monte Carlo Model: Iteration Mode, p. 34; APPX-Graphic05: Snapshot05 of the Monte Carlo Model: Iteration Mode, p. 35.29 Monte Carlo is superior to static models precisely because Monte Carlo projects scenarios weighted by probabilistic variation in inputs. In any given year, the mineral price may be higher or lower than predicted, but the Monte Carlo variations would catch the most likely pricing structure, assuming future pricing follows historic trends rooted in inflation, demand, and supply.



4. To achieve projected Revenue, assign the algorithm in Excel to multiply the price of the mineral commodity by its projected production. Note that in the case of Copper, A) one must multiply the Monte Carlo projection of concentrate by the Monte Carlo projection of yield to secure its production volume; B) this model subtracts the Monte Carlo projection of Treatment and Refining costs from projected price. The Revenue calculation for Gold multiplies the projected Gold price less Gold refining costs by the projected production in ounces;

5. Assign a Monte Carlo variable in Excel to predict the production costs as a function of Revenue, according to the firm’s cost schedule (Assumption 5 above);

6. Assign the variable in Excel to deduct Interest on Debt from Operating Revenue. (Pending final approval and placement of the debt portion).

7. Account for the Government Royalties as a percentage of Revenue, and Taxes as a percentage of EBT (Assumption 6 above);

8. Assign the algorithm in Excel to tabulate Royalties, Customs, VAT, Corporate Income Tax, and Withholding for each year;

9. Assign the algorithm in Excel to tabulate Free CashFlow to Equity for each year

10. Assign the algorithms in Excel to allocate Free Cash among the principal investors: GoM, represented by Erdenes MGL LLC; Rio Tinto (represented by its holdings in Turquoise Hill Mining); and Turquoise Hill excluding Rio Tinto (represented by their share holdings in Turquoise Hill Mining (Assumption 7 Above);

11. Assign the algorithm in Excel to calculate the NPV for Government and for the NPV for Investors.30

Note: Because Oyu Tolgoi is barely in its first few years of production, the analyst is unable to access a range of production figure data to fine-tune the efficiency parameters for Monte Carlo. The current model certainly allows for amendments to the standard deviation around the parameters the entity and its consultant produced from 2005-2013. Nevertheless, the choice of coefficient of variation to impute the standard deviations for each production input (Costs of Production as a percentage of revenue, SGA, CapEx, etc.) alters the volatility and range of outcomes. The analyst believes this to be less reliable that actual production figures after a 3-5 year history of mining at the project.

30 The projected FCFE and WACC will change when the debt it placed. The WACC will likely drop substantially.


MonteCarloAnalysisStakeholderInterestOT_TR | Page 19 of 44 DATA AND ANALYSIS OF DATA

Note, all aggregate valuations assume an inflationary environment, and figures are inclusive of most probable future inflation (based upon historic patterns and historic volatility). DCF values are adjusted by the WACC factors to present values, obviously.

The Monte Carlo random iteration model predicted the future mean Compound Annual Growth rate of change in Copper price for the 30-year life of the mine time horizon, virtually identical to the historic 30-year trends, but with a lower level of volatility (CV = 0.0920).

This projection supports the validity of the model to predict Copper price, provided the underlying fundamental inflationary, demand, and supply forces of the past 60 years do not change drastically. Barring sudden or rapid alterations in the functional currency or shifts in either demand or supply curves, this evidence supports that the Monte Carlo model is an adequate predictor of future copper price.

Figure 16: Inputed CAGR Copper Price LoMThis graphic captures the model’s projected distribution of future growth rates in the price of copper.

Fit: Beta Historic CAGR (30 years) Forecast CAGR (30 years)Mean 3.198% 3.18%

Median 3.319% 3.18%Standard Deviation 1.782% 0.29%

Coefficient of Variation 0.55708 0.0920

The formula to determine the CAGR: 1



Figure 17: Imputed CAGR Au Price LoMThis graphic captures the model’s projected distribution of future growth rates in the price of gold.

Fit: Lognormal Historic CAGR (30 years) Forecast CAGR (30-35 years)Mean 6.603% 6.59%

Median 7.350% 6.59%Standard Deviation 1.936% 0.32%

Coefficient of Variation 0.29327 0.0484

The formula to determine the CAGR: 1 As in the case for modeling copper price, the Monte Carlo random iteration model predicted the future mean Compound Annual Growth rate of change in Gold price for the 30-year life of the mine time horizon, virtually identical to the historic 30-year trends, but with a low level of volatility (CV = 0.0484). This projection supports the validity of the model to predict Gold price, provided the underlying fundamental inflationary, demand, and supply forces of the past 60 years do not change drastically. Barring sudden or rapid alterations in the functional currency or shifts in either demand or supply curves, this evidence supports that the Monte Carlo model is an adequate predictor of future gold price.


MonteCarloAnalysisStakeholderInterestOT_TR | Page 21 of 44 Units: '000's $USD (except Per Share values); Model is loaded for Inflation Forecast Min Mean Forecast Max Range Range:

MeanProjection of FCFE to All Equity Investors (Assuming Inflation: LoM) $15,131,194.419 $21,807,807.219 $31,053,535.203 $15,922,340.784 73.01%

GoM: Aggregate Cashflows (Royalties, Customs, Taxes) (Assuming Inflation: LoM) $22,280,806.137 $32,265,139.036 $44,311,817.694 $22,031,011.557 68.28%

GoM: FCFE (34.00%) (Assuming Inflation: LoM) $5,144,606.103 $7,414,654.454 $10,558,201.969 $5,413,595.87 73.01%

GoM: Aggregate Cashflows (Royalties, Customs, Tax, and Equity) (Assuming Inflation: LoM) $27,507,637.256 $39,679,793.491 $54,870,019.663 $27,362,382.407 68.96%

Rio Tinto Claims to FCFE (33.66%) (Assuming Inflation: LoM) $5,093,160.041 $7,340,507.910 $10,452,619.949 $5,359,459.908 73.01%

TRQ Excluding Rio Claims to FCFE (32.34%) (Assuming Inflation: LoM) $4,893,428.275 $7,052,644.854 $10,042,713.285 $5,149,285.01 73.01%

DCF of Aggregate Cashflows (Royalties, Customs, Taxes, and Equity) to GoM $3,436,375.066 $5,175,253.283 $8,080,264.163 $4,643,889.097 89.73%

Figure 18 Table DA-1A Results Summary

The model provides data showing that while the OT minerals project is lucrative indeed, the projected aggregate cash-flows are spread over a broad range of potential. On the one hand, the model predicts a mean cash-flow of $39.680 Billion of Royalties, Customs, and Taxes to GoM (73% of the projected total mean cash-flows). Yet the volatility in the mineral pricing structure (Assumption 1 above), coupled with the presumed volatility of production processes (Assumptions 2 and 5 above) suggests a $27.362 Billion range of possible cash-flows (Forecast Min=$27.508 Billion to Forecast Max=$54.870 Billion). That range is equivalent to 68.96% of the projected mean! (Note that though the model restricts the standard deviation of production costs to 10% of the mean costs, the projections suggest a large volatility.)

The Monte Carlo projections of DCF to the Mongolian Government (Royalties, Customs, Taxes, FCFE) return a mean of $5.175 Billion, but with a range of $4.644 Billion from projected minimum [$3.436 Billion] to maximum [$8.080 Billion]; a range of 89.73% of the mean.31

The Equity holders (in total) face a volatility in the projection of ultimate aggregate cash-flows. While the model predicts equity investors would secure a mean aggregate cash-flow of $21.807 Billion over the LoM, the range from forecast low [$15.131 Billion] to forecast high [$31.054 Billion] is $15.922 Billion. That range is equivalent to 73.01% of the projected mean!

These cash-flow data suggest high volatility with regard to projected prices of the mineral commodities within the OT project.

31 The projection is largely sensitive to the deviation in production costs. The projection returns a max-extreme distribution with a positive skew of 1.14 . Following the historic trends in Copper and Gold pricing, the data suggests high degrees of volatility. In addition, the DCF calculations will change after OT finalized the debt placement and the analyst updates the WACC.


MonteCarloAnalysisStakeholderInterestOT_TR | Page 22 of 44 Figure 19 Table DA-1B Results SummaryThese graphics illustrate the apportionment of projected 1) Royalties, Customs, VAT and Taxes; and 2) Free CashFlows to Equity.

These illustrations show that the Oyu Tolgoi, LLC investment agreement strongly favors the Government of Mongolia. GoM retains substantial claims to the project and the wealth it will generate. The Monte Carlo analysis projects that Government of Mongolia is entitled to over 73% of the cash the venture will produce for the stakeholders over a 30-35 year period.



This bar chart depicts ultimate projected GoM CashFlows (Royalties, Customs, Taxes, and Free CashFlow to Equity) vs. Free CashFlow to Equity for all principal investors (inclusive of GoM). In addition to claims to Equity, the GoM columns (in red) also include the cash from Royalties and Tax, imputing a large differential between GoM and the entities financing the project.The graphic clearly shows that this project largely favors the Government of Mongolia through its holdings in the project (i.e. Erdenes MGL LLC stake in OT, LLC).


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APPX-TABLE 2: GRAPHIC: CPISTATS


MonteCarloAnalysisStakeholderInterestOT_TR | Page 26 of 44 APPX-TABLE 3: GRAPHIC: CORRELATION


MonteCarloAnalysisStakeholderInterestOT_TR | Page 27 of 44 APPX-TABLE 4: GRAPHIC: CUSTATS


MonteCarloAnalysisStakeholderInterestOT_TR | Page 28 of 44 APPX-TABLE 5: GRAPHIC: AUSTATS


MonteCarloAnalysisStakeholderInterestOT_TR | Page 29 of 44 APPX-TABLE 6: PRIMARY DATA AND REGRESSION ANALYSIS FOR LOG10 COPPER PRICE


MonteCarloAnalysisStakeholderInterestOT_TR | Page 30 of 44 APPX-TABLE 7: PRIMARY DATA AND REGRESSION ANALYSIS FOR LOG10 GOLD PRICE


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