One of the main tasks to analyze and design a mining system is predicting the behavior exhibited by prices in the future. In this paper, the applications of different prediction methods are evaluated in econometrics and financial management fields, such as ARIMA, TGARCH, and stochastic differential equations, for the time-series of monthly copper prices. Moreover, the performance of these metho...

In this paper, we present an empirical analysis of deregulated electricity prices. We begin by examining the distributional and temporal properties of the price process in a non-parametric framework. This analysis is followed by comparing the forecasting ability of several different statistical models. The findings reveal several characteristics unique to electricity prices including determinis...

We address the problem of defining and calculating forward volatility implied by option prices when the underlying asset is driven by a stochastic volatility process. We examine alternative notions of forward implied volatility and the information required to extract these measures from the prices of European options at fixed maturities. We then specialize to the SABR model and show how the asy...

This paper develops a discrete operation optimization model for combined heat and powers (CHPs) in deregulated energy markets to maximize owners’ profits, where energy price forecasting is included. First, a single input and multi-output (SIMO) model for typical CHPs is established, considering the varying ratio between heat and electricity outputs at different loading levels. Then, the energy ...

This paper proposes a modified Genetic Programming method for forecasting the mobile telecommunications subscribers’ population.Themethod constitutes an expansion of the hybridGenetic Programming (hGP)method improved by the introduction of diffusion models for technological forecasting purposes in the initial population, such as the Logistic, Gompertz, and Bass, as well as the Bi-Logistic and L...

In the past two decades, many forecasting models based on the concepts of fuzzy time series have been proposed for dealing with various problem domains. In this paper, we present a novel model to forecast enrollments and the close prices of stock based on particle swarm optimization and generalized fuzzy logical relationships. After that some concepts of the generalized fuzzy logical relationsh...

We study the forecasting of future realized volatility in the foreign exchange, stock, and bond markets from variables in the information set, including implied volatility backed out from option prices. Realized volatility is separated into its continuous and jump components, and the heterogeneous autoregressive (HAR) model is applied with implied volatility as an additional forecasting variabl...

Stock market forecasting is important and interesting, because the successful prediction of stock prices may promise attractive benefits. The economy of Taiwan relies on international trade deeply, and the fluctuations of international stock markets will impact Taiwan stock market. For this reason, it is a practical way to use the fluctuations of other stock markets as forecasting factors for f...

Wages and prices are closely related. Wages are an important part of businesses’ costs and are thus tied to their pricing decisions. Similarly, people take the general level of prices into account when figuring out how much pay they deserve for their work. It is intuitive, therefore, that wage data could contain important information about prices and, in particular, might be useful for forecast...

SPRING 2000 THE JOURNAL OF DERIVATIVES 1 Option prices can reveal implied (risk-neutral) distributions, but it is not clear whether these are useful for forecasting or hedging or for revealing the current sentiment of investors. The authors estimate the implied distribution for stock index options in London as a mixture of two lognormals over the period 1987-1997 and find that this method is mu...