Forecasting crude oil price volatility via a HM-EGARCH model

Yu, Lin; Xiao, Yang; Li, Fuxing

doi:10.1016/j.eneco.2020.104693

Cited by 50 publications

(27 citation statements)

References 41 publications

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“…This suggestion is based on the fact that GED is heavy tailed compared to normal distribution and does not have an issue of possible infinite unconditional moments, as Student's t distribution does. Some research (e.g., [5][6][7][8]) however consider a normal or Student's t distribution when applying the volatility model of [4] that was based on GED. In order to take into account the feature of heavy tails, Reference [9] assumed that model residuals follow Student's t distribution, even if the empirical distribution of residuals had a higher peak than in the fitted normal distribution.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling and Estimating Volatility of Day-Ahead Electricity Prices

Tashpulatov

2021

Mathematics

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We model day-ahead electricity prices of the UK power market using skew generalized error distribution. This distribution allows us to take into account the features of asymmetry, heavy tails, and a peak higher than in normal or Student’s t distributions. The adequacy of the estimated volatility model is verified using various tests and criteria. A correctly specified volatility model can be used for analyzing the impact of reforms or other events. We find that, after the start of the COVID-19 pandemic, price level and volatility increased.

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Section: Literature Reviewmentioning

confidence: 99%

Modeling and Estimating Volatility of Day-Ahead Electricity Prices

Tashpulatov

2021

Mathematics

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“…Therefore, GARCH-class models have been investigated (Y. Lin et al, 2020;Marchese et al, 2020). Comparatively, the combination models are superior to the individual models.…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Gold Futures Prices at the Shanghai Futures Exchange Based on the MEEMD-CS-Elman Model

Wang

2021

SAGE Open

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The Gold futures market is a complex nonlinear system with the prediction of the futures prices of gold, one of the core issues faced by investors. Compared with more traditional approaches, empirical mode decomposition (EMD) and artificial neural network are the more powerful tools with which to deal with nonlinear and nonstationary price problems. By introducing mirroring extension (ME), EMD, Cuckoo Search (CS) algorithm, and Elman neural network, this article constructs the mirroring extension empirical mode decomposition (MEEMD)-CS-Elman model to forecast the price of gold futures using gold future AU0 price data from August 29, 2013, to October 18, 2018, at the Shanghai Futures Exchange (SFE) in China. Empirical results show that Elman combined with EMD is superior to single Elman in performance. Moreover, there exists an obvious endpoint effect by applying EMD to the price of AU0. By introducing the ME method, the endpoint effect can be dealt with better. Furthermore, by introducing the CS algorithm to optimize the initial weights and biases for Elman, the constructed MEEMD-CS-Elman model achieves far more accurate prediction results compared with either the EMD-Elman or the MEEMD-Elman model in terms of performance criterion: mean absolute difference (MAD), mean absolute percentage error (MAPE), root-mean-square error (RMSE), and directional symmetry (DS). In particular, the DS indicator, which reflects rising and falling prices, tends to be more attractive for investors. The value of the DS indicator in the MEEMD-CS-Elman model reaches 0.75207, meaning that the proposed model predicts the directions of increasing and falling prices quite precisely. Hence, by applying the proposed model, investors can make more scientific and accurate decisions and better reduce their investment risks.

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“…Moreover, GED does not have an issue of possible infinite unconditional moments as does Student's t distribution. Some research (e.g., [15][16][17][18]) though consider normal or Student's t distribution when applying the volatility model of [14] that was originally based on GED.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Regulatory Reforms on Demand Weighted Average Prices

Tashpulatov

2021

Mathematics

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Average prices are popularly used in the literature on price modeling. Calculating daily or weekly prices as an average over hourly or half-hourly trading periods assumes the same weight ignoring demand or traded volumes during those periods. Analyzing demand weighted average prices is important if producers may affect prices by decreasing them during low-demand periods and increasing them during high-demand periods within a day. The prediction of this price manipulation might have motivated the regulatory authority to introduce price caps not only on annual average prices but also on annual demand weighted average prices in the England and Wales wholesale electricity market. The dynamics of demand weighted average prices of electricity has been analyzed little in the literature. We show that skew generalized error distribution (SGED) is the appropriate assumption for model residuals. The estimated volatility model is used for evaluating the impact of regulatory reforms on demand weighted average prices during the complete history of the England and Wales wholesale electricity market.

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Forecasting crude oil price volatility via a HM-EGARCH model

Cited by 50 publications

References 41 publications

Modeling and Estimating Volatility of Day-Ahead Electricity Prices

Modeling and Estimating Volatility of Day-Ahead Electricity Prices

The Prediction of Gold Futures Prices at the Shanghai Futures Exchange Based on the MEEMD-CS-Elman Model

The Impact of Regulatory Reforms on Demand Weighted Average Prices

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