We examine the problem of modeling and forecasting European day-ahead month-ahead natural gas prices. For this, we propose two distinct probabilistic models that can be utilized in risk portfolio management. use daily pricing data ranging from 2011 to 2020. Extensive descriptive analysis shows both time series feature heavy tails conditional heteroscedasticity show asymmetric behavior their dif...

This paper proposes a new approach to hybrid forecasting methodology, characterized as the statistical recalibration of forecasts from fundamental market price formation models. Such hybrid methods based upon fundamentals are particularly appropriate to medium term forecasting and in this paper the application is to month-ahead, hourly prediction of electricity wholesale prices in Spain. The re...

Businesses are exploring the use of prediction markets to assist with forecasting. In this study, we focus on the ability of prediction markets to reflect the consensus of trader forecasts as well as the dispersion of these forecasts. Using a real-money, computerized, anonymous double-auction market mechanism, we examine a series of markets forecasting a real-life outcome, i.e., movie box offic...

Many economists expect inflation to rise in 1995. These expectations are based on various approaches to forecasting inflation. One approach is based on the standard economic theory that inflation rises when slack is eliminated from the economy and production exceeds capacity constraints. According to this view, measures of economic slack such as unemployment and capacity utilization provide use...

Many researchers have attempted to forecast the values of different cryptocurrencies, but few studies analyzed Monero price trends. ranks first in terms privacy features, and its demand is expected grow future. This paper can be classified as use PATSOS model prices According findings, accurately forecasted future with a very low error rate. Moreover, investors withstand market volatility avoid...

â â â â â â â  this paper proposes a new forecasting model for investigating relationship between the price of crude oil, as an important energy source and gdp of the us, as the largest oil consumer, and the uk, as the oil producer. gmdh neural network and mlff neural network approaches, which are both non-linear models, are employed to forecast gdp responses to the oil price changes. the resul...

With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models; however, models are compared to each other ...

A large part of the decision-making problems actors of the power system are facing on a daily basis requires scenarios for day-ahead electricity market prices. These scenarios are most likely to be generated based on marginal predictive densities for such prices, then enhanced with a temporal dependence structure. A semi-parametric methodology for generating such densities is presented: it incl...