Titlebook: Energy Risk Modeling; Applied Modeling Met Nigel Costa Lewis Book 2005 Palgrave Macmillan, a division of Macmillan Publishers Limited 2005

浅滩

Emery Balint M.C.E.(Melb.), M.I.C.E.eneral these models fall into three categories, Exponentially Weighted Moving Average models, Generalized Autoregressive Conditional Hetroscedasticity models, and Stochastic Volatility Differential Equations. In this chapter we introduce the first two of these modeling approaches.

教义

Inferential Statistical Methods for Energy Risk Managers population values or it may indicate that the conjecture is untenable. Hypothesis testing is a formal mechanism by which we can make and test inferential statements about the characteristics of a population. It uses the information contained in a sample to assess the validity of a conjecture about a specific population parameter.

dandruff

陈列

dowagers-hump

Descriptive Statistics of Energy Prices and Returnsstics is to reduce the original sample into a handful of more understandable metrics without distorting or losing too much of the valuable information contained in the individual observations. We begin by collecting . observations {.., ..,…,..} on the price return random variable .. These measuremen

lanugo

Inferential Statistical Methods for Energy Risk Managerscollected from a sample. Such generalizations are about an unobserved population. A population consists of all values (past and future) of the random variable of interest. In most circumstances the exact value of a population parameter such as the mean or variance will be unknown, and we will have t

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滔滔不绝地讲

Correlation Analysisor characteristic of a sample. Another important element of applied statistical modeling of energy risks concerns the relationship between two or more price or other variables. Generally, a risk manager will be interested in whether above (below) average values of one variable tend to be associated

聪明

multiply

Multiple Regression and Predictionct more than one independent variable to influence the dependent variable. It allows us to explore the relationship between several independent and a single dependent variable. We also discuss multivariate regression which arises when we have several dependent variables dependent on the same (or som