Crude Oil Price Forecast
Oct 15th, 2009 | By Aditya | Category: Technical Analysis, US StockCrude Oil Price Forecast
West Texas Intermediate Spot Price. USD/bbl. Average of Month.
| Month | Date | Forecast Value |
50% Correct +/- |
80% Correct +/- |
| 0 | Sep 2009 | 69.5 | 0 | 0 |
| 1 | Oct 2009 | 71 | 6 | 13 |
| 2 | Nov 2009 | 72 | 7 | 16 |
| 3 | Dec 2009 | 73 | 8 | 18 |
| 4 | Jan 2010 | 69 | 9 | 20 |
| 5 | Feb 2010 | 64 | 9 | 21 |
| 6 | Mar 2010 | 61 | 10 | 22 |
| 7 | Apr 2010 | 65 | 10 | 23 |
| 8 | May 2010 | 76 | 11 | 24 |
Updated Sunday, October 11, 2009
All forecasts are provided AS IS, and FFC disclaims any and all warranties, whether express or implied, including (without limitation) any implied warranties of merchantability or fitness for a particular purpose.
Crude Oil Prices
Past Trend Present Value & Future Projection
West Texas Intermediate. US Dollars per barrel.
Noted:
Percent Correct
At the 50% Correct value, there is a 50/50 chance the forecast value will be within this margin of error.
At the 80% Correct value, there is a 80% chance the forecast value will be within this margin of error.
The potential range of a forecast’s value is found by taking the published forecast value and both adding and subtracting the % Correct Values.
The % Correct (or error) values published are based on prior forecast performance.
For Example:
Forecast Value = 100
50% Correct Value = 10
80% Correct Value = 15
There is a 50% Chance the actual value will be between 110 and 90.
There is a 80% Chance the actual value will be between 115 and 85.
Technical Note: The Financial Forecast Center has moved away from publishing standard deviations of the forecast’s performance in recognition that the distribution of value movements in the financial markets follow Levy or Cauchy distributions, not Gaussian or normal distributions. Likewise, the forecast model’s errors follow similar distributions.
A Gaussian distribution significantly underestimates the probability of a large price or rate movement. A Gaussian distribution may underestimate the probabilty of a 3 sigma price movement by a factor of 10. In other words, the chance of a 3 sigma movement is potentially 10 times greater than that predicted by a Gaussian probability curve.
The above change in error reporting enables a more accurate depiction of a forecast model’s potential performance.




