Oil-Processing Facility
An interesting question is: how to determine the magnitude of a individual geopolitical
factor's influence to global crude oil price? This question is not NP-hard,
but a very difficult one if precise quantitative results are to be derived.
In a gross level, a practical approach could be using short-term events of a
specific geopolitical factor to gauge the corresponding factor's magnitude of
influencing power. An example is shown below, about the world's biggest oil
exporter: Saudi Arabia. On February 24, 2006, Islamic extremists took a bold
daytime attack on the world's largest oil-processing facility, called Abaci
close to Saudi Arabia's main export terminals on the Gulf coast. Although the
attack was defeated at the security road lock and did not affect the oil-
processing facility's daily production at all, future contract 1 of WTI (to
be delivered in March 2006) had a 3.4% price increase the next day. This is
a terrific example of the magnitude of Saudi oil's influencing power to the
global market. One hedge fund manager anticipated that the fall of the House
of Saud would generate a 262 per barrel price in the year of 2006.
Market Expectations
Market expectations could have radical influences on the price. Intuitively
one of its mechanisms could be described as follows. Before the release of key
actual statistics in each period, each player takes action to maximize his expected
profit according to her expectation of price. When players' expectations are
highly correlated, the collective action of these players can practically change
the actual determinants of the price. Therefore neglecting the market expectation
could lead to non-ignorable mistakes in a certain price prediction model. It
seems reasonable that the market expectations during a given time period may
be more relevant to determining prices during that period than are the actual
statistics that only become know much later. There are ways described to incorporate
expectation in a model:
(1) Price is a function of estimates for concurrent-season statistics. Expectation
is used for concurrent-season data.
(2) Price is a function of concurrent-season actual statistics and expectation
for the following season.
(3) Price is a function of concurrent-season estimates and expectation for
the following season. Expectations are used for both concurrent- and following-season
data.
It is important to realize that expectations for a coming season can often
have a stronger price impact than do prevailing fundamentals. This is particularly
true during the later half of a season when the fundamentals for the given
season are well defined due to players' action results and not subject to
significant variation. Players foresee the trend of the market in the next
period clearly and take effective action to protect his / her next period's
profit. Under some circumstances expectation for the following period plays
the dominant role in Price-determination.
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