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VIII-5
the simulation include:
All calculations are performed from 1 January 1995. Potash extraction activity in
the three areas of interest is treated as a capital project that was evaluated (and
undenaken) for three scenarios; either 1 January 1996, 1 January 1997, or 1
January 1999. However, the decision to go forward with any potash mining and
development venture was considered from the perspective of 1 January 1995.
Mine shaft and/or new plant capital expenditures are recovered using a 10 year
Accelerated Cost Recovery System depreciation method (Stermo!e and Stermole,
1993).
Revenues are treated as if realized monthly, and taxes and royalties are treated as
if they are paid on a quanerly basis.
Simulations were run for each year from 1995 to 2030. Each simulation run,
summarized as an individual data set in the attached appendix, consisted of 48
simulated potash price and cost "paths" from 1995 to 2030. The Monte Carlo
simulations generated numbers for each year for the present value of the market
value of the reserves (PV Rev) and the present value of the total cash flow for
each simulation run (PV CFlow). As is standard practice in fInancial analysis (for
example see Levy and Samat, 1994), cash flows attributable to the decision to
mine potash are the sum of income after taxes, depreciation, and depletion.
Summary data are also presented in the attached appendix for the present value of
severance-tax flows (pV SevTax), state corporate-tax flows (PV StateTax), federal
corporate-tax flows (PV CorpTax), and royalty payment flows (PV Royal).
All present values and NPVs are expected values, and are averages of many
simulation runs. Standard deviations, maximum values, minimum values, and
median values are also prOVided in the attachments for each individual simulation.
SpecifIcs regarding simulation.input variables are provided below.
Market prices
Annual prices per ton for both potash products under the various scenarios were
generated using a random-walk method known as a Wiener process. Historical,
confidential langbeinite and sylvite prices were analyzed using time-series techniques to
show that these historical prices may be modeled as a random process. An estimate for
the end of 1994 market price was obtained from area operators, and was used as the 1995
market price and the point of depanure for the price simulations.
Use of a Wiener process is attractive in situations such as this, particularly in the
case of a commodity like potash, because the uncertainty associated with the commodity
market price estimate in a given year is an increasing function of the forecast time
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