Model of Mongolian Tugrik Foreign Exchange

LMB ENTERPRISES, L.C. HOME ADDRESS:

BAYANZURKH 14-KH AKHMADIIN KH.

253 NARNI ZAM, 45

ULAANBAATAR

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MONGOLIA

LAND: 976-773-336-69

LYNDON MARTIN W.

BEHARRY

[email protected]

1 of 40 | © BEHARRY, LYNDON MARTIN W. | 2016-04-25_MNT-FX-Analysis | 2016-04-19 22:17

March 12, 2016

Abstract

Mongolia has suffered a four-year period of falling strength in foreign

exchange: 2012-2016. Oddly though, the dramatic plunge in the power of Mongolia's foreign exchange coincides with the first four years' production of

minerals through the world-class mining facility: Oyu Tolgoi. This instant research aims to isolate and model relationships comparing Mongolia's

currency's plummeting foreign exchange power against $USD prices of mineral commodities: Copper, Gold, and Silver. This enquiry creates a model isolating correlation between minerals sales and currency strength.

Ultimately, this study concludes that Mongolia's foreign exchange is highly influenced by the $USD prices of Gold and Silver, and of Copper to a lesser

degree. This research suggests that Mongolia's excessive reliance on minerals export is currently impairing the nation’s ability to manage its foreign

exchange. Dutch Disease and insufficient diversification of investment across other economic sectors has created severe volatility in ForEx. Government of

Mongolia’s (GoM) explicit dependence on minerals trade has tied ₮MNT exchange rate to $USD prices for minerals. This impacts GoM planning for stable economy and future investment toward infrastructure and

improvement of social services. Lastly, this paper will propose arguments toward diversification of investment in Mongolia, especially toward other

sectors: tourism, agro/animal husbandry, and textiles; to stabilize economic growth, and minimize volatility in GDP and GoM revenue.

mailto:[email protected]

2016-04-25_MNT-FX-Analysis | Page 2 of 40


Contents Abstract ..........................................................................................................................................................1

Important Notes concerning Data and Manipulation of Data .....................................................3

Introduction ..................................................................................................................................................4

Fiat Currency and Mongolia’s Entrée into International Economy .......................................4

Part 1: ..............................................................................................................................................................7

A Review of Mongolia’s Foreign Exchange Since 1990’s with Regression Analysis to

$US price of Gold per Ounce ..............................................................................................................7

Part 2: ............................................................................................................................................................13

A Review of Mongolia’s Foreign Exchange Since 1990’s with Regression Analysis to

the Major Minerals/Commodities Export .....................................................................................13

Sensitivity Analysis ..............................................................................................................................16

Part 3: ............................................................................................................................................................20

Analysis of the Data and Implications of the Model .................................................................20

Part 4: ............................................................................................................................................................21

How Diversification of Mongolia’s Cash-Generating Assets Would Reduce Volatility to

GDP and Foreign Exchange ..............................................................................................................21

The Diversification Model ...................................................................................................................23

Volatility in Commodities Prices ......................................................................................................25

The Model’s Statistical Parameters .................................................................................................30

Global Price Parameters: All Models ..........................................................................................30

Unique Parameters: Conservative Model ..................................................................................32

Unique Parameters: Moderate Model .........................................................................................32

Unique Parameters: Aggressive Model .......................................................................................33

Simulation Results and Analysis ....................................................................................................34

The Conservative Model ..................................................................................................................34

The Moderate Model .........................................................................................................................36

The Optimistic Model .......................................................................................................................37

Part 5: ............................................................................................................................................................38

Conclusion...............................................................................................................................................38

Part 6: ............................................................................................................................................................39

References................................................................................................................................................39

Part 7: ............................................................................................................................................................40

Appendices ..............................................................................................................................................40



Important Notes concerning Data and Manipulation of Data

1. The analyst drew ALL RAW DATA from Quandl.com

a. “Get the Data You Need In the Format You Want; Quandl helps data analysts save time, effort and money by delivering high-quality financial and economic data in the precise format they

need.” 2. Pricing conventions:

a. Copper Price: $USD per pound b. Gold price: $USD per ounce

c. Silver price: $USD per ounce d. Thermal Coal price: $USD per Metric Tonne

3. Timing convention for synchronizing minerals prices with Foreign

Exchange rate data: a. The analyst used Excel Vlookup function to synchronize

minerals prices with daily ForEx information (i.e. lookup the minerals price coincident with the instant date of foreign

exchange); b. The analyst used Excel Average function to isolate minerals

price averages for the time intervals under consideration;

4. Manipulation of Data: a. The analyst used standard Excel Linest array, Trendline, and

statistics functions to isolate regression formulae and statistics intervals;

b. The analyst used Excel to produce all graphs and charts; and then converted these into MS Word;

c. The analyst converted the Word document to Adobe PDF format.



Introduction

Fiat Currency and Mongolia’s Entrée into International Economy

Numerous tomes discuss the origins and development of money and foreign exchange. Encyclopedia of Business and Finance summarizes its article on

Money:

Money simplifies the exchange of goods and services and facilitates

specialization and division of labor. It does this by serving as a medium of exchange, as a measure of value, and as a store of value.1

The history of human civilization shows that social groups have used varieties

of items as money: cowry shells, Gold and Silver coinage and bullion (specie), promissory notes (paper contractually promising to deliver commodity,

typically precious metal), and banknotes and paper fiat currency, among other things. The post-WWII international system evolved from 1945/1946

through 1971 to conduct trade on fiat2 money, typically with the U.S. dollar as the lynchpin among the currencies of other nation-states.

Nations create fiat money as a matter of regulation or law. Fiat money sharply

contrasts against commodity (specie) money and representative (currency board) money. Commodity money bases its exchange value on a good

(typically specie: precious metal such as Gold or Silver). Representative money represents a transaction to exchange paper for another good or another paper

(currency board). Fiat money proclaims simply that money is worth only what the transacting public believe it is worth, and fiat money does not promise to deliver Gold or Silver. The post-World War II international system (following

John Maynard Keynes’ advice) began to move toward fiat money transactions as a matter of course. Signatories to the Bretton Woods accords agreed to peg

all major currencies to the United States dollar; with 35 U.S. dollars equivalent to one troy ounce of Gold. Ultimately, the U.S. moved away from

its promissory obligation, and the international world economic system seemingly became a fiat currency system after 1971. (Though some might argue that petroleum and petro-dollars became the substitute for promissory

receipts for Gold.)

Mongolia’s national currency is the Tugrik (tögrög Cyrillic: төгрөг: coin /

circle). The international currency markets have designated it with sign: ₮ and

code: MNT. First introduced in 1925 during the Communist era, Mongolia’s government designed the one tugrik coin as commodity money to redeem 18 grams of Silver. At that time, the government subdivided the tugrik according

to 100 möngö (мөнгө), with coin denominations ranging from 1 möngö and up

to 50 möngö, and 1 tögrög. But since the dissolution of the Soviet Union and Mongolia’s entrée into the world free-market system in the 1990’s, the country

has experienced rampant inflation. Today, the möngö coins are only novelty items. Bank of Mongolia only issues tugrik notes greater than 1, with the

1 Kaliski, Burton S. (ed.), Encyclopedia of Business and Finance, Second Edition, p. 519; Money article by Denise Woodbury 2 From the Latin fiat ("let it be done", "it shall be") used in the sense of an order or decree.



standard issue 1,000 tugrik note. At Q1 2016 market rates, 1,000 tugrik is

worth approximately $0.50 US.

Mongolia’s entry into the international economic system in 1990’s was a

shocking moment for its economy. The prior sixty-five years of relative isolation from world polity and economy had divorced its technology and

infrastructure from many advances. And though the nation is resource-rich, its minerals extraction capabilities were woefully out-dated and inadequate to produce competitively.

Nevertheless, because Mongolia was quick to adopt democratic-republican style government, international aid agencies and the world banking

community seemingly sought to adopt the land-locked Central Asian nation. International organizations were quick to provide aid in the form of grants

and loans – to stabilize government and free-market reforms. And over one generation, the twenty-year interval: 1993-2013, the nation had seemingly

blossomed into a booming economy, and a contender to foster a social system bringing prosperity to its citizens.

During this time, Mongolia’s Tugrik (₮ MNT) seemed to comply with normal

behavior, and the currency seemed to adhere to the standard model for fiat currencies. Bank of Mongolia’s interest rate policies, tourism and trade, and

the relative supply and demand of other currencies fostered apparent order and continuity to foreign exchange. Regarding Mongolia’s currency, The U.S.

State Department’s May 2015 report states:

"Mongolia’s currency is fully convertible for a wide array of international currencies and does fluctuate regularly in response to economic trends."3

Bank of Mongolia (BoM) 2014 Annual Report statement likely laid the groundwork for U.S. State Department’s economic assessment of Mongolia:

“…the Bank of Mongolia has been persistent in pursuing a floating exchange rate regime 4 that is consistent with macroeconomic

fundamentals and supports MNT’s stability and balanced development of the national economy. The Bank has been taking active participatory role in the domestic foreign exchange market through the foreign exchange

auction, in order to mitigate fluctuations in the exchange rate arising from the changes of short term imbalance in foreign exchange supply and

demand and to stabilize market participant’s expectation errors in the foreign exchange market.”5

Most English speakers would likely conclude that ‘consistent with macroeconomic fundamentals’ and ‘economic trend’ describe “the overall direction in which a nation's economy is moving,”6 with a range of measures

3 Embassy of the United States; Ulaanbaatar, Mongolia; Reports on Mongolia: 2015 Investment Climate Statement, May 2015 (http://mongolia.usembassy.gov/mobile//ics2015.html) 4 Analyst’s emphasis with bold italics. 5 Bank of Mongolia Annual Report 2014e, p. 65: https://www.mongolbank.mn/documents/annualreport/2014e.pdf 6 Economic Trend defined by Business Dictionary dot com: http://www.businessdictionary.com/definition/economic-trend.html#ixzz425qPyb29

https://www.mongolbank.mn/documents/annualreport/2014e.pdf

http://www.businessdictionary.com/definition/economic-trend.html#ixzz425qPyb29



including Balance of Payments, Employment statistics, GDP growth,

purchasing power comparisons, inflation measures and so on.

In 2015, though, Bank of Mongolia issued this following note with its

Monetary Policy Guidelines, 2015E. This verbiage: “flexible enough to support external economic balance and thus, in line with expanding domestic

production” may suggest some structure fitted to tie domestic currency to peg in specie.

1.2 The BoM will adhere to an exchange rate policy that ensures financial

and macroeconomic stability, is flexible enough to support external economic balance and thus, in line with expanding domestic production

and safeguarding employment in the medium term.7

So while neither Bank of Mongolia, Government of Mongolia, Mongolia

Development Bank, European Bank for Reconstruction and Development, International Monetary Fund, nor World Bank has directly and officially

shown that Mongolia has strategically tied its foreign exchange to the U.S. dollar price for gold, the following analysis shows that gold price has had a statistically significant bearing upon Mongolia’s currency’s foreign exchange

(Q1 2013 - Q1 2016); and on a formulaic basis.

7 Bank of Mongolia, Monetary Policy Guidelines, 2015E, https://www.mongolbank.mn/eng/listmonetarypolicy.aspx?id=04

https://www.mongolbank.mn/eng/listmonetarypolicy.aspx?id=04



Part 1:

A Review of Mongolia’s Foreign Exchange Since 1990’s with Regression

Analysis to $US price of Gold per Ounce In general, under a fiat currency floating exchange rate regime pursued by

BoM8 exchange rate volatility ought to bear no relationship to value founded upon specie (Gold or Silver). One expects, rather, that foreign exchange in fiat

currency relates solely to economic trends in the overall economy: a) Balance of Payments among the respective fiat currencies; b) free-market hedging based upon the expectations of the Net Present Value of potential future cash

flows among investments using the respective fiat currencies, c) hedging/arbitrage aimed to secure equalization in purchasing power parity

terms; d) hedging/arbitrage aimed to secure equalization in interest rate parity terms; and e) the respective governments’ adjustments in interest rates

to affect M1 supply via their respective reserve banking systems. As such, one ought to expect a currency’s foreign exchange rate should have minor, little, or no significant absolute correlation to a potential underlying support from

either Gold or Silver specie (as in the following Chart 1A plotting Mongolia’s Tugrik ForEx rates against $USD price for Gold: c. March 1996 – December

31, 2012).

Chart 1A shows that over the majority of the modern life record of Mongolia’s

foreign exchange as a fiat currency in the international system (16-year interval: 1996-2012), there is virtually no indication that the Mongolian Tugrik’s foreign exchange value had been based in specie. This above Chart

1A exhibits scant evidence of a relationship between Mongolia’s currency’s

8 Bank of Mongolia Annual Report 2014e, p. 65: https://www.mongolbank.mn/documents/annualreport/2014e.pdf

y = -0.0004x + 1.2404R² = 0.1655

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2.50

0 500 1,000 1,500 2,000

1000

₮ B

uys

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S Sp

ot

FX R

ate

$USD Gold/Oz

Chart 1A: March 29, 1996 - December 31, 20121000 ₮ Buys ___$US Spot ForEx (Y-Axis) Vs. Gold Price $US/Oz (X-Axis)

1000 ₮ Buys ___$US Spot FX Rate Linear (1000 ₮ Buys ___$US Spot FX Rate)

https://www.mongolbank.mn/documents/annualreport/2014e.pdf



external purchasing power and the price of Gold (with R-squared of only

0.1655).

The following Table 1 and accompanying Chart 1B indicate a similar

conclusion. Chart 1B illustrates the Pearson

Correlation R-Values comparing Spot ₮MNT:$USD foreign exchange rates

against Spot Minerals Prices ($USD) over eighteen

subsequent four-year intervals (1996-1999

through 2013-2016). The results show declining and

negative relationships (i.e. negative correlations) between ₮MNT:$USD rates

from 1999 through 2007/2008; but then

sharply increasing correlations between

Mongolia’s foreign exchange purchasing power and the price of mineral

commodities: Gold, Silver, and Copper. The final two

data points are for 2.25 years (Jan 2014-Mar 2016),

and 1.25 years (Jan 2015-Mar 2016). The last data point reflects a far lower

correlation than the other post-2011 R-values, possibly

because of the rapid changes in $USD commodities prices working in tandem with the 90-day forward

pricing model discussed in Part 2 of this paper. In other words, the drastic drop and recovery in $USD Gold and Silver prices from end of 2015 through early 2016 may not manifest itself in ₮MNT:$USD rates until mid-2016.

TABLE 1: PEARSON CORRELATION STATISTIC AT INTERVAL

R-Value of Spot ForEx to Instant Mineral Price

Gold: Au Silver: Ag Copper: Cu

1996-1999 0.874373094 -0.398417

1997-2000 0.865794444 -0.234618

1998-2001 0.815548825 -0.027091

1999-2002 -0.190249752 0.6045666

2000-2003 -0.60543551 0.1445936

2001-2004 -0.852218492 -0.595042

2002-2005 -0.69238145 -0.513088

2003-2006 -0.481862234 -0.547678

2004-2007 -0.804763088 -0.804824 -0.026162

2005-2008 -0.700923396 -0.629202 0.3251596

2006-2009 -0.728379763 -0.289731 0.5522136

2007-2010 -0.564927518 -0.167453 0.3986507

2008-2011 -0.018810716 0.2104225 0.4097571

2009-2012 0.558008452 0.7376508 0.7322662

2010-2013 0.351075718 0.6099962 0.677818

2011-2014 0.736496197 0.8814165 0.7983614

2012-2015 0.909754823 0.9117455 0.8389528

2013-2016 0.848284922 0.8744399 0.7683818

2014-2016 0.718547466 0.8434107 0.8576519

2015-2016 0.432295892 0.548437 0.6369279



Chart 2 (below) covers the January 2013 through February 2016 trends and

inflection in Gold price alongside ₮MNT:$USD foreign exchange volatility. This Chart 2 intimates correlation between $USD Gold price and ₮MNT:$USD

rates.9 Visual inspection of the Chart 2 (below) indicates some underlying relationship; especially a temporal connection between inflections in $USD

Gold price/Oz and the later manifestation of similar inflection in the foreign exchange rate.

9 The period: 2013-2016 coincides with the issue of Mongolia’s sovereign bonds December 2012; and commencement of Copper, Gold, and Silver production at Oyu Tolgoi: January 2013.

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Chart 1B:Correlation of 1000 ₮ MNT Buying ___ $USD to Spot Mineral Prices By interval

Au Ag Cu

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Chart 2: January 1, 2013 - March 01, 2016Gold Price $US/Oz (Y1-Axis); 1000 ₮ Buys ___$US Spot ForEx (Y2-Axis)

Gold Price $US/Oz 1000 ₮ Buys ___$US Spot FX Rate



This initial visual inspection and analysis of Chart 2 suggests a relationship

between Mongolia’s ForEx regime and the $USD Gold price/Oz; correlations between Gold price and Mongolia’s exchange rates, in other words.

The line regression profile analysis in Chart 3 (below) compares each day’s Gold price against the same day’s rate of foreign exchange, limiting the time

scale to the period since Mongolia issued its sovereign debt (vis. Dec 2012/Jan 2013). This Chart 3 plots the incidence of 1000 ₮MNT purchasing its equivalent in $US (Y-axis) against $US Gold Price (X), for each trading day

between January 1, 2013 through March 1, 2016. The regression produced a fairly strong correlation statistic (R2=0.72); again suggesting a relationship

between the $US Gold price and Mongolian ₮ foreign exchange.

Yet, because of the apparent time lag between inflections in $US Gold price

and the ₮MNT:$USD exchange rate (evident in Chart 2 above), the analyst sought to compare the instant day’s Mongolian tugrik foreign exchange rate

against Gold prices from a prior period. The analyst performed line regression analysis comparing the instant foreign exchange rate against spot Gold price 30-days prior, 45-days prior, and 90-days prior. The regression of

the day’s foreign exchange rate to spot Gold price from 90-days prior returned very high correlation statistics (Chart 4 R2=0.885).

y = 0.0004x + 0.0073R² = 0.7196

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$USD Gold/Oz

Chart 3: January 1, 2013 - March 01, 20161000 ₮ Buys ___$US Spot ForEx (Y-Axis); Gold Price $US/Oz (X-Axis);




This last analysis (Chart 4) provides firm evidence suggesting a relationship

in Mongolia’s foreign exchange to $US Gold price of 90-days prior. In other words, Chart 4 suggests a foreign exchange regime on 90-day forward

contract fixed to a formula.

But mathematics and statistical analysis support Moving Average as among

the most stable configurations for reducing volatility. Chart 5 assesses the ₮MNT:$USD and 90-Day Moving Average of $USD Gold price per ounce.

y = 0.0004x + 0.0548R² = 0.8853

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$USD Gold/Oz

Chart 4: January 1, 2013 - March 01, 20161000 ₮ Buys ___$US Spot ForEx (Y-Axis) (2013-01-01 - 2016-03-02)

90-Day Prior Spot Gold Price $US/Oz (X-Axis) (2012-10-03 - 2015-12-03);


y = 0.0004x + 0.0398R² = 0.9528

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$USD Gold/Oz

Chart 5: January 1, 2013 - March 01, 20161000 ₮ Buys ___$US Spot ForEx (Y-Axis) (2013-01-01 - 2016-03-02)

90-Day Prior 90-Day MA Gold Price $US/Oz (X-Axis)(2012-07-05 - 2012-10-03 through 2015-09-04 - 2015-12-03);




Chart 5 compares each day’s Mongolian ₮MNT:$USD foreign exchange rate

against the 90-day moving average of the $US Gold price from 90-days prior of the day (in other words, the average of $US Gold prices from 180-90 days

prior to the ₮MNT:$USD spot rate). This model pushed the correlation statistics up to an R-value of 0.976; with the R-Squared over 0.95. This

graphic most strongly suggests an underlying relationship between the moving average of the Gold price to forward currency contracts 90 days hence.



Part 2:

A Review of Mongolia’s Foreign Exchange Since 1990’s with Regression

Analysis to the Major Minerals/Commodities Export Mongolia’s main commodities exports are Copper, Thermal Coal, and Coking

Coal. Other valuable minerals products include Gold and Silver. The nation may also pursue uranium mining in the future, and also look into extraction

of shale oil for export.

The analysis in Part 1 strongly suggests a mathematical relationship between 90-day forward ₮MNT:$USD foreign exchange rate rooted in moving average of

past Gold prices quoted in $USD. Perhaps others of Mongolia’s commodities also affect the strength of ₮MNT:$USD. This following Table 2 summarizes the

correlation statistics between the 90-day forward ₮MNT:$USD foreign exchange rate and the prior 90-day manifestation of the 90-day moving

average among Mongolia’s major export commodities:

TABLE 2:

Regression of 90-Day MA 90 Day Prior Commodities to ₮MNT:$USD10 (Period: Jan 2013-Mar 2016; Minerals Prices 90-days Prior) R2 R-Value

Silver (Ag) 0.96311 0.981382

Gold (Au) 0.952769 0.976099

Copper (Cu) 0.713805 0.84487

Thermal Coal 0.071304 0.267027

The correlation statistics clearly show significant fitness between ₮MNT:$USD foreign exchange and Silver (Ag), Gold (Au), and to a lesser degree Copper

(Cu). This regression suggests the extraordinary influence the forward sales of these mineral commodities bear upon fluctuations in Mongolia’s foreign

exchange rates. This report will generate predictive formulae using these three: Ag, Au, and Cu. But because the correlation statistics for thermal coal

are so low, this paper will not study its influence upon ₮MNT:$USD foreign exchange rates. Following the general algebraic formula for linear relationships, 3-Variable line regression suggests a stable fit for the line:

1000 ₮ MNT Buys ___USD = +mAg X [180-90 Prior day Moving Average $US/Oz Silver]

+mAu X [180-90 Prior day Moving Average $US/Oz Gold] +mCu X [180-90 Prior day Moving Average $US/Lb Copper]

+ B (where m is the slope coefficient for each component and B is the “Y”-intercept)

10 The analyst compiled these statistics using Microsoft Excel Linest function.



1000 ₮ MNT Buys ___USD =

+0.01599199 X [180-90 Prior day Moving Average $US/Oz Silver] -0.00004116 X [180-90 Prior day Moving Average $US/Oz Gold]

-0.02347884 X [180-90 Prior day Moving Average $US/Lb Copper] + 0.360038021

Chart 6 graphically illustrates the application of the regression formula to generate the 90-Day Forward Forecast (on the prior 90-Day [MA: Au, Cu, Ag]).

Strong descriptive statistics support Chart 6 (above). Table 3 summarizes the differences between the Actual ₮MNT:$USD and the Forecast ₮MNT:$USD

values calculated by applying the regression formula to data available 90 days prior. The calculation:

[(Actual-Forecast)/Actual]:

These data show that the

maximum divergence of the ₮MNT:$USD forecasted 90-days prior to the Actual

₮MNT:$USD foreign exchange rate was 8.45%.

Furthermore, 97.5%; numerically 827 forecast

instances out of 848 total data points fell within 2.5 standard deviations of the

0.096% Mean. In other words, during the interval

October 2, 2012 through December 2, 2015, this model quite accurately

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10

00

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Bu

ys _

__$

USD

Chart 6A:Forecasting Mongolia's 90-Day Forward Foreign Exchange Rates With Ag, Au, Cu

Spot FX Rate On Date 90-Day Forward Forecast (90-Day [MA: Au, Cu, Ag])

1000 MNT Buys ___USD =+ (0.01599199) X [90-Day Avg of Silver Price from 180 days to 90 days Prior to FX rate]+ (-0.00004116) X [90-Day Avg of Gold Price from 180 days to 90 days Prior to FX rate]+ (-0.02347884) X [90-Day Avg of Copper Price from 180 days to 90 days Prior to FX rate]+ 0.360038021

Raw Figures Table 3 Adjusted to

Absolute Values

8.452% Max 8.452%

-4.860% Min 0.021%

0.096% Mean 1.893%

2.496% StDev 1.628%

0.06336334 Expected Upper

at ___ StDev

0.059643

-0.0614532 Expected Lower at ___ StDev

-0.02177

2.5 StDev

848 Count 848

827 Count If

Between

820

97.524% PerCent in

Interval

96.698%



predicts the future exchange rate, based on the prior 90-days average price of

the minerals: Silver, Gold, and Copper; and the formula produces the prediction 90-days before the ₮MNT:$USD actually manifests itself in the

international finance market.

Table 4 summarizes the line regression fit at a mean of 0.045% +/- 1.50

StDev 2.494% showing that 86.675% of the Forecast values fall within a range of

3.741% the day’s Actual ₮MNT:$USD Spot rate. Fully

86.675% of the forecast values fall within bounds of

1.5 Standard Deviations from the mean value.

Chart 6B (below) displays upper and lower boundaries for each forecast point. The range calculation:

Forecasted Value +/- 3. Fully 99.29% of all actual values fall with the range of forecasted predictions at three standard deviations (within 7.48% above or below the stated forecast point).

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T B

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Chart 6B:Forecasting Mongolia's 90-Day Forward Foreign Exchange Rates With AuSpot ForEx Period: March 1, 2013 - March 1, 2016Minerals Pricing Period: Aug 30, 2012-Nov 30, 2012 Through Sep 1, 2015-Dec 2, 2015

Spot FX Rate On Date 90-Day Forward Forecast (90-Day [MA: Au])

Upper Limit: +1.50σ Lower Limit: -1.50σ

1000 MNT Buys ___USD =+ (0.0000578) X [90-Day Avg of Gold Price from 180 days to 90 days Prior to FX rate]+ (0.0127196) X [90-Day Avg of Silver Price from 180 days to 90 days Prior to FX rate]+ (-0.0186230) X [90-Day Avg of Copper Price from 180 days to 90 days Prior to FX rate]+ 0.2828598

Raw Figures Table 4 Adjusted to

Absolute Values

8.493% Max 8.452%

-5.492% Min 0.004%

0.045% Mean 1.941%

2.494% StDev 1.566%

3.69689% Expected Upper

at ___ StDev 4.29015%

3.78614% Expected Lower

at ___ StDev -0.40909%

1.5 StDev

848 Count 848

735 Count If

Between 772

86.675% PerCent in

Interval 91.038%



Sensitivity Analysis

SENSITIVITY ANALYSIS

COPPER (Note the extreme volatility referenced in the standard deviation.)

TABLE 5 [(Actual-Forecast)/Actual] Absolute Values

13.161% Max 13.161%

-11.189% Min 0.008%

-0.091% Mean 6.172%

7.092% StDev 3.488%

0.07000273 Expected Upper at ___ StDev 0.096596

-0.0718298 Expected Lower at ___ StDev 0.026836

1.0 StDev

848 Count 848

474 Count If Between 499

55.896% PerCent in Interval 58.844%

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1000

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uys

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_USD

Chart 6C:Forecasting Mongolia's 90-Day Forward Foreign Exchange Rates With CuSpot ForEx Period: March 1, 2013 - March 1, 2016Minerals Pricing Period: Aug 30, 2012-Nov 30, 2012 Through Sep 1, 2015-Dec 2, 2015

1000 MNT Buys ___USD =+ (0.175299368) X [90-Day Avg of Copper Price from 180 days to 90 days Prior to FX rate]+0.027133802R-squared = 0.713804798




GOLD


9.168% Max 9.168%

-5.993% Min 0.025%

0.030% Mean 2.447%

2.879% StDev 1.515%



1.0 StDev

848 Count 848



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Chart 6D:Forecasting Mongolia's 90-Day Forward Foreign Exchange Rates With AuSpot ForEx Period: March 1, 2013 - March 1, 2016Minerals Pricing Period: Aug 30, 2012-Nov 30, 2012 Through Sep 1, 2015-Dec 2, 2015

1000 MNT Buys ___USD =+ (0.000397564) X [90-Day Avg of Gold Price from 180 days to 90 days Prior to FX rate]+0.039672456R-squared = 0.952769447




SILVER


8.150% Max 8.150%

-5.197% Min 0.000%

0.099% Mean 2.042%

2.622% StDev 1.647%



1.0 StDev

848 Count 848



The sensitivity analysis records the fitness of the model forecasts to actual market ForEx over the time intervals within a bounded range of one standard deviation.

While the sensitivity analysis shows that Silver (R2=0.963) has been a slightly better predictor of the forward exchange rate than Gold (R2=0.953) has been,

this analysis will pursue Gold as the better indicator for three reasons: 1) since 2013, Ag and Au have moved in tandem with an R2 value greater than

0.97; 2) at 2013 and 2014, Bank of Mongolia retained only 305 and 116 ₮MNT millions of silver assets; but 251,440 {123,254,902 $USD} and 207,066 ₮MNT

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Chart 6E:Forecasting Mongolia's 90-Day Forward Foreign Exchange Rates With AgSpot ForEx Period: March 1, 2013 - March 1, 2016Minerals Pricing Period: Aug 30, 2012-Nov 30, 2012 Through Sep 1, 2015-Dec 2, 2015

1000 MNT Buys ___USD =+ (0.013280977) X [90-Day Avg of Silver Price from 180 days to 90 days Prior to FX rate]+0.289571343R-squared = 0.963110097



millions {101,502,941 $USD}11 in Gold (fair market value)12; and 3) Bank of

Mongolia engages vibrant and active trade in Gold, as the sole purchaser of Gold concentrate, ore, and other forms mined in Mongolia. And though

Copper concentrate is a far greater proportion of total exports (2015 Cu Concentrate=49%, non-monetary Gold=9%),13 the forward forecast of ForEx

using Copper prices is the most unreliable of the three, with the largest range of volatility and a regression profile statistic of only R2=0.714.

In fact, according to Mongolia’s Foreign Trade Review (December 2015):

Exports of coal, copper concentrate, iron ore and concentrate and crude oil have a weight of nearly 74% of total exports and 85% of mining

exports.14

So while non-monetary Gold exports comprise only 10.35% of Mongolia’s total

mineral export, this analysis

shows that $USD Gold/oz price bears a

substantial role in ₮MNT:$USD

foreign exchange.

Therefore, this sensitivity analysis

ultimately prefers the Gold

ForEx Prediction

model because of the greater trade weight

and reserves of Gold over Silver

and the extreme volatility embodied within the Copper model.

11 March 1, 2016 1$USD:2040₮MNT so Gold Holdings converted to 2016 value 2013: 251,400 Millions ₮MNT = 123,254,902 $USD; 2014: 207,066 Millions ₮MNT = 101,502,941 $USD. 12 Bank of Mongolia Annual Report 2014, pp. 117-131 13 Bank of Mongolia Foreign Trade Review (December 2015). 14 Ibid. 15 Ibid.

TABLE 815 2015

BANK OF MONGOLIA

FOREIGN TRADE REVIEW

Q:UNIT {'000, MT, KG}

$USD

MILLIONS

% OF

EXPORTS

% OF

MINERALS

MINERALS EXPORTS

COAL 14,426 555 11.88% 13.65%

COPPER CONCENTRATE 1,478 2,280 48.82% 56.06%

IRON ORE/CONC. 5,065 227 4.86% 5.58%

CRUDE OIL 8,135 387 8.29% 9.52%

ZINC ORE / CONCENTRATE 84 102 2.18% 2.51%

NON-MONETARY GOLD 11,343 421 9.01% 10.35%

SPAR, LEUCINE, NEPHELINE 280 66 1.41% 1.62%

MOLYBDENUM, ORE/CONC. 5 29 0.62% 0.71%

4,067 87.09% 100.00%

CASHMERE EXPORTS

WASHED CASHMERE 4,958 197 4.22%

COMBED CASHMERE 559 41 0.88%

238 5.10%

OTHER EXPORTS 365 7.82%

TOTAL 4,670 100.00%



Part 3:

Analysis of the Data and Implications of the Model

This research isolates testable mathematical models to describe relationships

among Mongolia’s major minerals commodities and the strength of Mongolia’s currency unit. The data show that for the majority of the past twenty years

(1996-2016), the exchange rate between Mongolian Tugrik and the U.S. Dollar bore no significant positive correlation to precious metal specie. However, data after 2007/2008 show increasing significantly positive correlations between

Mongolia’s foreign exchange power and the U.S. Dollar prices for Gold, Silver, and Copper, to a smaller degree. Pointedly, the ₮MNT:$USD ForEx rate has

come to reach exorbitantly high correlations with Gold and Silver since 2012/2013 (Chart 1B).

In Mongolia’s present economic portfolio, minerals export forms the lynchpin to Mongolia’s Gross Domestic Product. Because of this, one would certainly expect mineral commodities prices should bear upon the nation’s power in

foreign exchange to some degree; but indirectly, through effects upon real GDP and Balance of Payments. However, this analysis strongly suggests that

for more than 3.25 years, $USD minerals prices have virtually controlled ₮MNT:$USD ForEx rates within a timescale of 90 days of a change in the $USD

price of the mineral commodity. This inference forces a few rough conclusions:

1) Mongolia’s Tugrik behaves as though its value derives from the $USD prices for Gold and Silver (Mongolia is on a Gold standard, in other

words); 2) Mongolia’s actual GDP:productive capacity seemingly has little to do

with its purchasing power expressed through ₮MNT:$USD ForEx rates; 3) Arbitrary market shocks pushing investors towards safe-haven Gold

may tend to boost ₮MNT:$USD ForEx rates, offering the illusion that Mongolia’s fiscal condition is stronger than productivity would acclaim (and the converse, investors’ periodic flight away from Gold to equities

and bonds would offer the illusion that Mongolia’s economy is weaker than it is);

4) GoM’s continued reliance upon minerals sales and mining infrastructure endangers overall economic diversity (Dutch Disease),

and subjects Mongolia to continued broad swings in riches to rags to riches to rags, solely upon the arbitrary basis of $US price for Gold.



Part 4:

How Diversification of Mongolia’s Cash-Generating Assets Would Reduce

Volatility to GDP and Foreign Exchange

Discussing Mongolia's current economic position, World Bank Group's: Mongolia Economic Update (November 2015) includes a section:

Meanwhile, substantial external downside risks lie ahead.16

The World Bank report shows that China's economic growth slowdown may exert downward pressures on the regional demand for commodities. The

report also suggests that U.S. monetary policy, especially interest rate hikes, may also bear negatively upon Mongolia's economic position. And certainly,

as stated above within this paper, excessive reliance upon the Primary Sector: mining chains Mongolia to the volatility of the commodities pricing market.

Take coal and iron, for example. Three factors tend to reduce the value and/or volume of iron and coal exports if world petroleum prices remain stressed: 1) petroleum by-products17 and thermal coal are mutual intermediate and long-

term substitutes one for the other, so low petroleum prices will likely force down the price of thermal coal; 2) low petroleum prices decrease the likelihood

that oil producers will invest in exploration infrastructure (i.e. great volumes of steel), and this translates into reduced demand for coking coal; and 3)

reduced demand for steel will obviously reduce demand for iron ore. Hence, low petroleum prices impact Mongolia’s minerals sector for thermal coal, coking coal, and iron ore.

At the same instant, a myopic strategic focus upon petroleum or shale oil production in Mongolia might also prove disastrous to economic growth. If

Mongolia undertakes massive investment into petroleum and shale oil production in a depressed price environment, 18 production may never

produce the cash-flows necessary to recover the capital costs. Mongolia’s investment strategy ought to take a fresh look at the new world system where U.S. shale oil has transformed the dynamics of petroleum and natural gas

pricing and international demand.

Mongolia ought to diversify its Primary (raw materials production) Sector,

while the nation grows its Secondary (manufacturing) and Tertiary (services) Sectors. For example, while agriculture and animal husbandry may not be as

lucrative export products as minerals; investment to increase production in

16 World Bank Group: Mongolia Economic Update (November 2015), Catalog No. 101064, p. 36 17 Petroleum and shale oil extraction often produce natural gas as a significant by-product. Certainly many markets use petroleum and natural gas for home heating. Natural gas is a direct intermediate-term substitute for thermal coal electric power generation. Certainly other factors influence thermal coal popularity in the long-run: pollution, green energy alternatives, natural gas, and petroleum by-products. See charts 7A and 7B for a graphic representation of correlation between prices for long-term petroleum and coal. 18 The United States has assessed that it may have as many as 1.5 trillion barrels equivalent of recoverable shale oil within its boundaries. With such reserves, the U.S. has become virtually self-sufficient in fossil fuel energy. This forces the OPEC nations to underbid each other to sell their petroleum to Asian markets. This dynamic may keep petroleum prices under 60 $USD for intermediate term (potentially the long-term); and current prices of $38-$40 per barrel may not even reach $60 for several more years.



grains, meats, wool, and dairy would increase GDP, stabilize GDP volatility,

and potentially reduce population pressures in Ulaanbaatar. Secondary manufacturing, particularly in finished value-added wools and cashmere

textiles would also increase earnings and produce greater stability to GDP. Furthermore, tourism could produce substantial contributions to Gross

Domestic Product. By building adequate infrastructure (notably airport capacity and transnational rail) to facilitate greater numbers of tourist travel in-country, Mongolia should aim to double, and then treble the frequency of

tourists arriving annually (currently 450,000); while attracting high-value tourists. Clearly, if Mongolia could draw and accommodate an additional

500,000 tourists per annum, and each visitor spent an average of $1,000 in country, GDP would grow by $500 million; and this represents a 4.17%

increase over 2015 GDP of $12 Billion. Certainly, the country should engage a focused marketing strategy to engage more high-value tourists, as this

would result in broader improvements in revenue to the service sector. Ski resorts, for instance, situated within good alpine mountains could open up a whole new market for Mongolia. And notably, tourist revenue is generally not

subject to the wild price variability the commodities sector suffers.

And one other service industry Mongolia may wish to target: Banking and

Finance. Since Mongolia has invested so heavily in its minerals production, and because mining is a long-term pursuit (80 years and more), the nation

may wish to consider a commodities exchange to monetize forward and futures contracts on the minerals; but also on agriculture, wools, and cashmere production. In other words, Mongolia needs to excel in niche finance

and generate home-grown expertise in financial engineering and investment specialists. These types of diversifications and others will add to Mongolia’s

bottom line while mitigating the wild swings in GDP the country is now experiencing.



The Diversification Model

This section derives a Monte Carlo simulation of Mongolia’s export revenue

on baseline FY2015. It builds annual projections constrained by criteria:

1) The model isolates the base volumes of commodities sold;19

2) The model grows these volumes following expert guidance;20 3) The projections use the Monte Carlo 21 method tracking historical

changes in price;

4) The model forecasts a range of future export contributions to GDP and records projected metrics;

5) The model registers volatility as standard deviations around the projected compound annual growth rate (geometric mean);

6) Versions of the model simulate rapid growth in Primary Sector (non-minerals), Secondary Sector (finished food, textiles, etc.), and Tertiary

Sector (tourism) for comparison; 7) The simulation of export earnings requires conservative guidance:

a) Reasonable forecasts of the volumes of future exports, and the

volatility embedded within these year-over-year projections; b) Reasonable forecasts of export pricing by applying historical price

changes and volatility to current pricing; c) Reasonable expectations for developing infrastructure

(transportation and warehousing) to grow volumes of trade over time; and

d) Conservative estimates defining the macro-economic environment

under which the scenario(s) generates data.

Mongolia is land-locked between China and Russia; it has no seaport. For

trade, Mongolia must convey its commodities and manufactures by rail or motorway through China or Russia. Prior to the dissolution of the Soviet

Union (1989-1990), Russia had been Mongolia’s leading trade partner. But in recent years, China has become Mongolia's primary trade partner. By 2015,

China consumed nearly ninety percent of Mongolia’s exports. GoM continues to develop trade relationships and affiliations for economic development with members of the European Union (notably U.K., Italy, and Germany); other

Asian neighbors; and Canada. But any substantial trade with Europe could obtain only after Mongolia expands infrastructure. The nation must increase

industrial spurs and rail joining trans-Siberian lines. Unfortunately, this current Monte Carlo export model cannot reasonably accommodate

hypothetical future trans-national rail lines. Nor can the model contemplate

19 Principal exports: minerals, fossil fuels, tourism agro/animal husbandry commodities (cashmere and wool). Textile manufacture may potentially become a lucrative export. 20 The analysis will follow World Bank and GoM guidance whenever possible. 21 Wikipedia (https://en.wikipedia.org/wiki/Monte_Carlo_method) defines Monte Carlo method: "Monte Carlo methods (or Monte Carlo experiments) are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other mathematical methods... (Monte Carlo is quite useful) modeling phenomena with significant uncertainty in inputs such as the calculation of risk in business and, in math, evaluation of multidimensional definite integrals with complicated boundary conditions..."



physical reduction of transit barriers to Europe. This model focuses upon the

current trade regime. The model accepts China as Mongolia's most significant export market for the next ten-year interval (2016-2025).

China is Mongolia's principal market for export sales. And China’s demand subordinates Mongolia’s aspirations for wealth. Recent economic data for

China suggest a three to 5-yr period of contraction seeking equilibrium.22 This material constraint causes decline in Mongolia's export earnings. Two factors weaken export revenue: 1) reductions in volume sold; and 2) reduction in

world market price for commodity. Indeed, BoM's December 2015 report shows that Mongolia's export revenue declined 19% from FY2014.23 In other

words, China's economic slowdown forces a reduction in Chinese demand. China’s troubles coupled with declining minerals and fossil fuel prices eroded

one-fifth of Mongolia's earnings in 2015. The double curse of declining demand and declining prices has severely jeopardized Mongolia's financial

position.

22 “Further slowdown is expected going forward, and some projections show growth rates dropping to about 6 percent by the end of the decade.” (p. 2); “Our results confirm that the impact of a negative shock to Chinese real GDP on the Asian countries has significantly increased under the recent trade structures of 2005 and 2013 compared to the earlier trade structures of 1985 and 1995.” (p. 5); Tomoo Inoue, Demet Kaya, Hitoshi Ohshige: The Impact of China’s Slowdown on the Asia Pacific Region-An Application of the GVAR Model, World Bank Group, IDA Resource Mobilization Unit, (October 2015) 23 Mongolia’s 2015 imports were down over 27% from FY2014.



Volatility in Commodities Prices

Charts 7A and 7B provide economic perspective for GoM’s reliance on few

commodities for the bulk of national export earnings. They illustrate price volatility for the country’s most significant exports. The visuals cover a 64-

year interval (1952-2015) capturing several worldwide economic cycles. These representations smooth the price data (7A: Real prices; 7B: Nominal prices) using their logarithms (Log10). The data show, for instance: Real Gold prices

tend to rise; Real Coal prices are somewhat stable with slight downward trend; Real Petroleum prices (since early 1970s) have tended to increase; and Real

Copper prices tend to decline. The U.S. departure from redemption of Gold, coincident with the rise of Petro-dollars is clearly evident in the representation

(c. 1972-1973). Post-2010 illustrates the fundamental shift caused by U.S. shale oil. Finally, the graphs reveal the tendency of Coal price to follow the

trend in Petroleum price.

Chart 7A

With trend-line formulae: Log10 Real Gold Price = 0.0093x - 15.711 R² = 0.4784 Log10 Real Coal Price = -0.0019x + 5.3333 R² = 0.047 Log10 Real Petroleum Price = 0.0121x - 22.485 R² = 0.4994 Log10 Real Cu Price = -0.0032x + 6.6627 R² = 0.0951

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1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

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Prices Of Mongolia's Main Export CommoditiesLog10 Real Prices (U.S. CPI2010): (1952-2015)

Gold Real Price Log(10) Copper Real Price Log(10)

Crude Oil Real Price Log(10) Coal Real Price Log(10)

Linear (Gold Real Price Log(10)) Linear (Copper Real Price Log(10))

Linear (Crude Oil Real Price Log(10)) Linear (Coal Real Price Log(10))



Chart 7B

With trend-line formulae: Log10 Nominal Gold Price = 0.0273x - 51.953 R² = 0.8559 Log10 Nominal Coal Price = 0.0164x - 31.264 R² = 0.7565 Log10 Nominal Petroleum Price = 0.0301x - 58.727 R² = 0.8352 Log10 Nominal Copper Price = 0.0152x - 30.269 R² = 0.7777

Charts 7A and 7B reinforce the fact of volatile commodities prices; and this lends itself to mathematical simulation. Monte Carlo is better than static

models precisely because of the inherent uncertainty to commodities prices’ Brownian motion with drift. Though minerals prices are in constant flux, Monte Carlo is the most capable method to capture the impact of variation.

These analyses do not aim to predict exact future price. Rather, Monte Carlo uses trends to isolate historical parameters (typically: mean or median)

changes in price, linked to deviation. In other words, the Monte Carlo simulation does not set out to forecast Copper price in 2020. Monte Carlo

simply says that over 20-year intervals, on average, real Copper prices tend to decrease by about 0.95% per annum with a standard deviation of 3.05%. Clearly, then, Real Copper prices are extremely volatile, with a slight

downward slope.

In summation, this model uses an active multi-iterative routine to propose a

probabilistic range of outcomes for Mongolia’s near/medium-term trade revenue. Monte Carlo simulation is the superior approach to process the

immense variety of statistical and probabilistic parameters. The analysis

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Prices Of Mongolia's Main Export CommoditiesLog10 Nominal Prices: (1952-2015)

Gold Nominal Price Log(10) Copper Nominal Price Log(10)

Crude Oil Nominal Price Log(10) Coal Nominal Price Log(10)

Linear (Gold Nominal Price Log(10)) Linear (Copper Nominal Price Log(10))

Linear (Crude Oil Nominal Price Log(10)) Linear (Coal Nominal Price Log(10))



follows volatility in price changes and volume changes, weighted relative to

China's position as primary consumer. It calculates revenue within each period (Chart 8) by randomly varying changes in price and changes in volume

year-over-year, subject to historical parameters updated with new guidance. By assessing over a million random iterations in a host of inputs, Monte Carlo

returns a probabilistically weighted description of outcomes in export revenue (Chart 9).



Chart 8



Chart 9



The Model’s Statistical Parameters

Global Price Parameters: All Models

The Monte Carlo simulation uses these conservative commodity statistics to forecast export revenue. The analysis calculated price statistics of the commodities at risk from: 1952-2014; seeking central tendency by isolating

changes in real prices (CPI:2010100) and compound annual growth rates (geometric means) over subsequent 10-yr, 20-yr, 30-yr, and 40-yr time

intervals. Rigorous analysis of the commodities prices produced these data:

TABLE 9A: PARAMETRIC BOUNDARIES FOLLOWING CHANGES IN REAL PRICES (CAGR: 1952-2014)24

COMMODITY25 INTERVAL ACTUAL

DISTRIBUTION26 DEFAULT

DISTRIBUTION MEAN MEDIAN

STANDARD

DEVIATION

Mineral: Au 20-yr Undefined Normal 2.26054% 3.29655% 3.98549%

Mineral: Calcite

20-yr Undefined Normal

Mineral: Cu 20-yr Undefined Normal -0.95281% -1.52261% 3.05729%

Mineral: Fe 20-yr Undefined Normal -0.32719% 0.53294% 2.98167%

Mineral: Mo 20-yr Undefined Normal

Mineral: Zn 20-yr Undefined Normal -0.26567% -0.07477% 2.06125%

Fossil Fuel: Coal

20-yr Undefined Normal -0.35557% -0.04873% 3.37751%

Fossil Fuel: Petroleum

20-yr Undefined Normal 3.18256% 4.59812% 4.85951%

Significant features of these commodities’ long-term average changes in price:

1) The average real price of Au tends to rise approximately 2% higher than U.S. inflation;

2) Minerals Cu, Fe, and Zn tend to experience low or negative changes in real prices. Competition forces efficiencies ultimately leading to sales prices equivalent to: 1) opportunity costs (i.e. a metric such as cost of

capital or return on investment); added to 2) production costs. Recycled copper, for instance, uses far less energy to bring product to market

than product originating with mined and milled ore. Recycled minerals form the theoretical minimum sales price, particularly in periods of low

demand or extreme supply. 3) The sales price of fossil fuel Coal has tended to decrease over the forty-

four 20-yr intervals;

4) The sales price of fossil fuel Petroleum has tended to increase. However, U.S. shale oil has ushered in a new dynamic to fossil fuel pricing. U.S.

shale may have long-term implications governing future changes in fossil fuel prices.

24 There are no data available for Molybdenum and Calcites (including zeolites). The analysis defaults to Iron (Fe) to fill in the gaps. 25 Statistics on the changes in price range from multiple 20-year intervals from 1952-2014. This particular analysis contemplates changes in Real prices of the commodities, adjusted from nominal using U.S. CPI 2010 as the base 100. 26 The analysis sought goodness of fit statistics with Oracle Crystal Ball Monte Carlo software (http://www.oracle.com/us/products/middleware/bus-int/crystalball/index.html). In all cases (except Gold 30-yr CAGR), the software could not define an optimum distribution curve. Some data tended toward lognormal, other data pushed toward Beta or Weibull. To simplify the scenario, this analysis coded the random variation to normal distributions with the stated means, medians, and deviations.



Tables 9B through 9F list the Monte Carlo model’s change in volume

parameters and the change in price parameters for non-minerals exports. All forecasts retain the identical changes in minerals prices because these

parameters obtain from worldwide historical patterns (1952-2014). On the other hand, non-minerals prices (agricultural products, cashmere, meats,

and potentially contraband27) are more difficult to gauge. For instance, the average world real grain USD prices (2010=100) have tended to decrease by approximately CAGR 2.50% each annum from 1979 through 2014 inclusive.28

However, change in climate and local weather conditions, water resource management, and transportation costs regularly impose volatility and pricing

shifts to primary agricultural and meat production. In fact, the data clearly show firming of prices in recent years, which may suggest a realization of

resource limits to produce wheat. Overall, these exogenous variables complicate the parameter structure for Mongolia’s agricultural forecasts, and

revenue projections.

27 Contraband may include: smuggled gold, items of national or scientific significance (historical rarities, dinosaur fossils, other matter), and potentially drugs or other illegal items. 28 This analysis pulled data from World Bank GEM database for World Wheat in $US/mt. CAGR: Mean change = -2.683%; Median change = -2.498%; with Standard Deviation = 1.942% .



Unique Parameters: Conservative Model

Other parameters for the conservative forecast include:

TABLE 9B: PARAMETRIC BOUNDARIES MINERALS CHANGES IN VOLUME SOLD

VOLUME CV GUIDANCE

Mineral: Au 1.000% 10.000% (Is UK buying gold? Or mostly China???)

Mineral: Calcite 1.000% 25.000% Limited market; Subordinate to Chinese demand.

Mineral: Cu 1.000% 10.000% Subordinate to Chinese demand.

Mineral: Fe 1.000% 25.000% Subordinate to Chinese demand.

Mineral: Mo 1.000% 15.000% Subordinate to Chinese demand.

Mineral: Zn 1.000% 25.000% Subordinate to Chinese demand.

Fossil Fuel: Coal 1.000% 25.000% Subordinate to Chinese demand / competing against other fossil fuel suppliers.


1.000% 10.000% Subordinate to Chinese demand / competing against other fossil fuel suppliers.

TABLE 9C: NON MINERALS PARAMETRIC BOUNDARIES (VOLUME AND PRICE)

COMMODITY

VOLUME CV

PRICE CV GUIDANCE

Washed Cashmere

2.000% 5.000% 2.000% 10.000% Conservative and achievable; but product is susceptible to weather.

Combed Cashmere

2.000% 5.000% 2.000% 10.000% Conservative and achievable; but product is susceptible to weather.

Tourist 3.500% 20.000% 5.000% 0.003% GoM aims to achieve 1 million tourists in 2020; this growth achieves 485,000

Other 2.000% 0.100% 2.000% 0.005%

*Unknown plug* to balance the books: agricultural products, finished textiles, foodstuffs, potentially smuggled gold, and other contraband = 109 Million $USD

Unique Parameters: Moderate Model Other parameters for the moderate forecast include: TABLE 9D: PARAMETRIC BOUNDARIES MINERALS CHANGES IN VOLUME SOLD

VOLUME CV GUIDANCE

Mineral: Au 1.000% 10.000% (Is UK buying gold? Or mostly China???)

Mineral: Calcite 1.000% 25.000% Limited market; Subordinate to Chinese demand.

Mineral: Cu 1.000% 10.000% Subordinate to Chinese demand.

Mineral: Fe 1.000% 25.000% Subordinate to Chinese demand.

Mineral: Mo 1.000% 15.000% Subordinate to Chinese demand.

Mineral: Zn 1.000% 25.000% Subordinate to Chinese demand.

Fossil Fuel: Coal 1.000% 25.000% Subordinate to Chinese demand / competing against other fossil fuel suppliers.


1.000% 10.000% Subordinate to Chinese demand / competing against other fossil fuel suppliers.

TABLE 9E: NON MINERALS PARAMETRIC BOUNDARIES (VOLUME AND PRICE)

COMMODITY

VOLUME CV

PRICE CV GUIDANCE

Washed Cashmere

2.000% 5.000% 4.000% 10.000% Problematic: cashmere is subordinate to weather conditions; Demand driven luxury item.

Combed Cashmere


Tourist 11.400% 20.000% 10.000% 0.003% GoM aims to achieve 1 million tourists in 2020; this growth achieves 700,000

Other 2.000% 0.100% 2.000% 0.005% *Unknown plug* to balance the books: agricultural products, finished textiles,



foodstuffs, potentially smuggled gold, and other contraband = 109 Million $USD

Unique Parameters: Aggressive Model Other parameters for the moderate forecast include:

TABLE 9F: PARAMETRIC BOUNDARIES MINERALS CHANGES IN VOLUME SOLD

VOLUME CV GUIDANCE

Mineral: Au 10.000% Mongolia Economic Update29, Nov. 2015 (p. 6, 37)

Mineral: Calcite 25.000% Low-value minerals; hard to forecast

Mineral: Cu 10.000% Mongolia Economic Update, Nov. 2015

Mineral: Fe 25.000% Mongolia Economic Update, Nov. 2015

Mineral: Mo 15.000%

Mineral: Zn 25.000%

Fossil Fuel: Coal 25.000% Mongolia Economic Update, Nov. 2015


10.000%

TABLE 9G: NON MINERALS PARAMETRIC BOUNDARIES (VOLUME AND PRICE)

COMMODITY

VOLUME CV

PRICE CV GUIDANCE

Washed Cashmere


Combed Cashmere


Tourist 19.640% 20.000% 10.000% 0.003% GoM aims for 1 million tourists by 2020; this growth rate achieves 1,000,000

Other 2.000% 0.100% 2.000% 0.005%

*Unknown plug* to balance the books: agricultural products, finished textiles, foodstuffs, potentially smuggled gold, and other contraband = 109 Million $USD

29 Mongolia Economic Update, (World Bank Group) forecasts a diminishing share of gold contribution to commodities export. Furthermore, 2013 Oyu Tolgoi Technical Report (March 2013) projects OT gold production for the productive life of the mine (pp. 392-394) also IDP 2010 p. 419. Given that OT gold production is a substantial contributor to Mongolia gold production, the variability in OT’s forecast may bear upon this forecast of export sales volume. See appendix --- for breakdown.



Simulation Results and Analysis

The Conservative Model

Table 10 and Chart 10 catalog the conservative model forecast real USD values for Mongolia’s near-term exports. While the forecast statistics indicate slightly positive skew, suggesting greater upside; the mean forecasts for the

nation’s commodities driven export real earnings remain wholly anemic. These data project a mean growth rate of 1.67% (year over year); and an

equivalent 1.66% CAGR (geometric growth rate) from initial year to terminal. This model clearly shows the disruptive downside of an economy built almost

exclusively upon minerals commodities export, and with only one major trading partner.

Table 10: Trials=250,000 ‘000 USD

Export Contribution to GDP30 2015 2016 2017 2018 2019 2020

Mean 4,067,000 4,670,049 4,742,997 4,820,086 4,900,774 4,985,861

Median 4,669,904 4,742,043 4,818,146 4,897,945 4,982,129

Standard Deviation 76,880 109,440 134,675 156,275 175,337

Coeff. of Variability 0.0165 0.0231 0.0279 0.0319 0.0352

Minimum 4,338,003 4,270,568 4,162,684 4,217,808 4,258,595

Maximum 5,008,121 5,245,978 5,485,247 5,641,600 5,797,746

Distribution Fit Normal Lognormal Lognormal Lognormal Lognormal

Skewness 0.0044 0.0508 0.0872 0.1153 0.135

Kurtosis 2.99 2.99 3.02 3.02 3.04

30 Oracle Crystal Ball produced this forecast on a trial 250,000 iterations of the variables: changes in real

minerals prices attached to conservative projections of commodities volumes sold. Note that these are real

monetary values in ‘000 USD, forecast on the basis of 2015 actual figures.

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

7,000,000

8,000,000

2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Chart 10: Forecast of Export Revenue (Real '000 USD)

Actual Mean Minimum Maximum



The data also suggests the degree to which terminal earnings (Forecast

FY2025) is sensitive to period exports. Earnings are heavily sensitive to Copper sales (75.55%), followed by Gold (5.55%), and Oil (5.55%). Other

exports (including cashmere, tourism, and others) accounted for the remaining 13.35% of influence upon forecast FY2025 performance. Notably,

the model shows that changes in commodities pricing and volumes during certain years had more influence upon terminal performance. For instance, this iteration set showed that copper price changes in FY2017 has a stronger

bearing on FY2025 performance than copper price changes in 2025.31 Not only does the model highlight the danger of Dutch disease. The model also

exposes the positive and negative effects geometric growth imposes upon earnings. Prior trends in changes in price and volume compound with time to

have substantial impact upon future export earnings.

31 The model randomly adjusts each subsequent year’s commodities price based upon the historical real price

change vector applied to the prior year’s forecast price. Because of geometric growth, a random change in a

certain year may have a more significant effect on some future year’s performance.



The Moderate Model Table 11 and Chart 11 catalog the moderate model forecast real USD



The Optimistic Model Table 11 and Chart 11 catalog the optimistic model forecast real USD



Part 5:

Conclusion



Part 6:

References

Bank of Mongolia 2014 Annual Report

Kaliski, Burton S. (ed.), Encyclopedia of Business and Finance, Second Edition, p. 519;

Money article by Denise Woodbury

Embassy of the United States; Ulaanbaatar, Mongolia; Reports on Mongolia: 2015 Investment

Climate Statement, May 2015 (http://mongolia.usembassy.gov/mobile//ics2015.html)

Business Dictionary dot com: http://www.businessdictionary.com/definition/economic-trend.html#ixzz425qPyb29





Part 7:

Appendices

Economy & Finance

Model of Mongolian Tugrik Foreign Exchange