Oil price volatility forecast with mixture memory GARCH
Tony Klein,Thomas Walther +1 more
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TLDR
In this paper, the authors compare the Mixture Memory GARCH (MMGARCH) model to other discrete volatility models (GARCH, RiskMetrics, EGARCH, APARCH, FIGARCH, HYGARCH and FIAPARCH) and find that MMGARCH outperforms the aforementioned models due to its dynamic approach in varying the volatility level and memory of the process.About:
This article is published in Energy Economics.The article was published on 2016-08-01 and is currently open access. It has received 71 citations till now. The article focuses on the topics: Volatility (finance) & Autoregressive conditional heteroskedasticity.read more
Citations
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The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market
Xu Gong,Boqiang Lin +1 more
TL;DR: In this article, the authors investigate whether investor fear gauge (IFG) contains incremental information content for forecasting the volatility of crude oil futures, and they use oil volatility index (OVX) to measure the IFG.
Journal ArticleDOI
The impact of extreme events on energy price risk
TL;DR: In this paper, the authors employed long span daily data from April 1, 1983 to December 30, 2019 and found that both natural and human extreme events significantly increase oil price risk.
Journal ArticleDOI
Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?
TL;DR: In this paper, the authors used three single-regime GARCH (GARCH, GJR-GARCH and EGARCH) and two regime-switching GARCH models (MMGARCH and MRS-Garch) to forecast crude oil price volatility.
Journal ArticleDOI
Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model
Rongda Chen,Jianjun Xu +1 more
TL;DR: In this article, a multivariate generalized autoregressive score (GAS) model was used to forecast volatility and correlation between crude oil and gold prices. But, the forecasting power of the multivariate GAS model is better than the classical Dynamic Conditional Correlation Generalized Auto-Regressive Conditional Heteroskedasticity model.
Journal ArticleDOI
Effects of structural changes on the prediction of downside volatility in futures markets
Xu Gong,Boqiang Lin +1 more
TL;DR: In this article, a heterogeneous autoregressive model with structural changes was developed to investigate the effects of structural changes on predicting downside volatility in the S&P 500 index, crude oil, gold, copper, and soybean futures markets.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
Journal ArticleDOI
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.
Journal ArticleDOI
On a measure of lack of fit in time series models
Greta M. Ljung,George E. P. Box +1 more
TL;DR: In this paper, the overall test for lack of fit in autoregressive-moving average models proposed by Box & Pierce (1970) is considered, and it is shown that a substantially improved approximation results from a simple modification of this test.
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