Stochastic Model For Energy Spot Price Analysis

This could indicate the presence of two markets, one very price sensitive and a much less price sensitive ­ high end niche but smaller market. After taking the derivative, the P = ½ MC + 3560. However, due to the shape of the graph and its poor fit to the linear regression, I split the data in two portions. According to the market analysis, the more price sensitive (MAS) end of the market is the largest segment and a price of $4400 produces the largest UCM (Exhibit 1.) I repeated the regression again with prices from $3750 to $5000 only. The trend line is now a much closer fit than previously and the new derivative equation is P = ½ MC + 2895.

4) Use cubic regression to determine an equation for the data (or lwh where (12 – x) represents the sides and (x) represents the height of the box).

27. The following equation describes a 2nd order system: . The system may be described as:

This paper comprises an appreciation of data representation, its visualization, an outline description of behavior, plus an indication of the use of the equation in engineering.

5 = a + b*0 //from this we see that a=5, and we plug that value into the second equation

The givens and assumptions are all of the values included in the above equation. The equation above also highlights what we needed to find, the discrete dynamical system, and the plan was to make assumptions, analyze the givens, and substitute all the given values into the general discrete dynamical system equation.

By definition, a regression equation is an equation used in statistics to find out what relationship, if any, exists between sets of data. This is most useful for predicting future data or estimating data that exists in between. To find a regression equation, a scatter plot must first be

The plan I made first was to figure out the two equation given to me in the problem. Next I would use the substitution method to figure out the y-intercept of both equations . Lastly I went to a website called Desmos where I would put the two equation and see if my y-intercept was correct.

This regression equation can be graphed as follows assuming β0 as the intercept and β1 as the slope:

Regression line model is an approach to model the relationship between a scalar dependent variables

There are various reasons that prediction of natural gas price is imperative to producers, suppliers, traders, market makers, and bankers involved in natural gas exploration, production, transportation and trading as well as consumers involved in utilization of natural gas (Mishra, 2012). The prices of energy commodity including natural gas price are very volatile that make the parties engaged in a high risk and uncertain activities. More accurate forecast of the price, help them to select an appropriate strategy to lower the uncertainty and hedge the risk. This research attempt to find the best way to model and forecast natural gas price among different approaches and choose the optimum practice to use.

Abstract This paper uses Augmented Dickey-Fuller and Phillips-Perron technique for determining whether individual crude oil prices (West Texas Intermediate, Brent, Japan crude cocktail) and natural gas prices- Henry Hub (HH), National Balancing Point (NBP), European and Japanese liquefied natural gas (LNG) prices are stationary or non-stationary. It then applies Johansen and Juselius cointegration technique for establishing long-run correlation between respective oil prices and natural gas prices. The paper concludes that all individual series pertaining to oil and natural gas prices are non-stationary and indeed having long-run relationship, despite

The following graph demonstrate the demand curve of how many items of a product or service a consumer would like to purchase at different prices. Now by having the product at a lower price, the more a consumer is likely to buy. For that same reason it can be concluded that the price is one major factor of the product demand.

–x3 – x5 + x13 + x14 + x22 + x23 + x24 = 0

To examine the effect of monetary policy on determination of coal prices in the U.S., we rely on a dynamic framework of cointegrated vector autoregression (CVAR). The prerequisite requirement for the application of the CVAR approach is that the selected variables must be nonstationary (i.e., I(1) series). The presence of a unit root in the variables is thus tested using the Dickey Fuller generalized least squares (DF-GLS) test (Elliott et al., 1996). Panel A of Table 1 reports the results of the DF-GLS test. Since the null hypothesis of a unit root cannot (can) be rejected for any of the levels (first differences) of the three variables at the 5% level, all the series are found to be nonstationary I(1) processes.