Journal ArticleDOI
A semiparametric approach to short-term oil price forecasting
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TLDR
In this article, the authors used the GARCH properties of oil price changes to forecast the oil price distribution over short-term horizons, based on the bootstrap approach, and the results of an out-of-sample forecasting exercise, carried out using the Brent oil price series, suggest that the forecasting approach can be used to obtain a performance measure for the forward price, in addition to compute interval forecasts for the oil prices.About:
This article is published in Energy Economics.The article was published on 2001-05-01. It has received 248 citations till now. The article focuses on the topics: Forward price & Brent Crude.read more
Citations
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ARIMA models to predict next-day electricity prices
TL;DR: In this article, a method to predict next-day electricity prices based on the ARIMA methodology is presented, which is used to analyze time series and have been mainly used for load forecasting, due to their accuracy and mathematical soundness.
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Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm
TL;DR: In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price forecasting and empirical results obtained demonstrate attractiveness of the proposed EMD-based neural networksemble learning paradigm.
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Forecasting electricity prices for a day-ahead pool-based electric energy market
TL;DR: In this article, forecasting techniques to predict the 24 market-clearing prices of a day-ahead electric energy market were considered, including time series analysis, neural networks and wavelets, and extensive analysis was conducted using data from the PJM Interconnection.
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Modeling the Price Dynamics of Co2 Emission Allowances
Eva Benz,Stefan Trück +1 more
TL;DR: In this paper, the short-term spot price behavior of CO2 emission allowances of the new EU-wide CO2 emissions trading system (EU-ETS) was analyzed and several approaches for modeling the returns of emission allowances were investigated.
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Speculation and Volatility Spillover in the Crude Oil and Agricultural Commodity Markets: A Bayesian Analysis
TL;DR: The authors assesses factors that potentially influence the volatility of crude oil prices and the possible linkage between this volatility and agricultural commodity markets, finding evidence of volatility spillover among crude oil, corn, and wheat markets after the fall of 2006.
References
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Distribution of the Estimators for Autoregressive Time Series with a Unit Root
David A. Dickey,Wayne A. Fuller +1 more
TL;DR: In this article, the limit distributions of the estimator of p and of the regression t test are derived under the assumption that p = ± 1, where p is a fixed constant and t is a sequence of independent normal random variables.
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Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
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Conditional heteroskedasticity in asset returns: a new approach
TL;DR: In this article, an exponential ARCH model is proposed to study volatility changes and the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987, which is an improvement over the widely-used GARCH model.
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On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks
TL;DR: In this article, a modified GARCH-M model was used to find a negative relation between conditional expected monthly return and conditional variance of monthly return, using seasonal patterns in volatility and nominal interest rates to predict conditional variance.
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Measuring and Testing the Impact of News on Volatility
Robert F. Engle,Victor K. Ng +1 more
TL;DR: This paper defined the news impact curve which measures how new information is incorporated into volatility estimates and compared various ARCH models including a partially nonparametric one with daily Japanese stock return data.