On the public citation graph

Forecasting energy prices: Quantile‐based risk models

https://doi.org/10.1002/for.2898
0 signed · 59 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/for.1040openalex-snapshot
Forecasting the price of crude oil via convenience yield predictions
inferredcites →https://doi.org/10.1002/fut.20277openalex-snapshot
Forecasting oil price movements: Exploiting the information in the futures market
inferredcites →https://doi.org/10.1002/fut.22184openalex-snapshot
Determinants of the WTI-Brent Price Spread Revisited
inferredcites →https://doi.org/10.1002/jae.1159openalex-snapshot
What do we learn from the price of crude oil futures?
inferredcites →https://doi.org/10.1002/jae.659openalex-snapshot
Computation and analysis of multiple structural change models
inferredcites →https://doi.org/10.1002/jae.951openalex-snapshot
A simple panel unit root test in the presence of cross‐section dependence
inferredcites →https://doi.org/10.1007/s11708-016-0396-8openalex-snapshot
Does oil price affect the value of firms? Evidence from Tunisian listed firms
inferredcites →https://doi.org/10.1007/s11708-020-0696-xopenalex-snapshot
Environmental, social, and economic assessment of energy utilization of crop residue in China
inferredcites →https://doi.org/10.1016/b978-0-444-53683-9.00008-6openalex-snapshot
Forecasting the Price of Oil
inferredcites →https://doi.org/10.1016/j.econmod.2015.06.007openalex-snapshot
Is there a structural change in the persistence of WTI–Brent oil price spreads in the post-2010 period?
inferredcites →https://doi.org/10.1016/j.egyr.2020.04.023openalex-snapshot
Asymmetric effect of oil price on economic growth: Panel analysis of low-income oil-importing countries
inferredcites →https://doi.org/10.1016/j.eneco.2008.06.002openalex-snapshot
The impact of oil price shocks: Evidence from the industries of six OECD countries
inferredcites →https://doi.org/10.1016/j.eneco.2008.07.008openalex-snapshot
Forecasting oil price movements with crack spread futures
inferredcites →https://doi.org/10.1016/j.eneco.2014.06.003openalex-snapshot
Are the macroeconomic effects of oil price shock symmetric?: A Factor-Augmented Vector Autoregressive approach
inferredcites →https://doi.org/10.1016/j.eneco.2014.08.008openalex-snapshot
Are there gains from pooling real-time oil price forecasts?
inferredcites →https://doi.org/10.1016/j.eneco.2015.02.018openalex-snapshot
A novel hybrid method for crude oil price forecasting
inferredcites →https://doi.org/10.1016/j.eneco.2016.02.017openalex-snapshot
Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach
inferredcites →https://doi.org/10.1016/j.eneco.2016.03.017openalex-snapshot
Predicting the oil prices: Do technical indicators help?
inferredcites →https://doi.org/10.1016/j.eneco.2018.10.026openalex-snapshot
Forecasting oil prices: High-frequency financial data are indeed useful
inferredcites →https://doi.org/10.1016/j.ijforecast.2017.11.005openalex-snapshot
Online big data-driven oil consumption forecasting with Google trends
inferredcites →https://doi.org/10.1016/j.jeca.2017.04.002openalex-snapshot
Asymmetry and break effects of oil price -macroeconomic fundamentals dynamics: The trade effect channel
inferredcites →https://doi.org/10.1016/j.jeconom.2006.05.023openalex-snapshot
Approximately normal tests for equal predictive accuracy in nested models
inferredcites →https://doi.org/10.1016/j.physa.2019.02.022openalex-snapshot
Forecasting the oil prices: What is the role of skewness risk?
inferredcites →https://doi.org/10.1017/cbo9780511754098openalex-snapshot
Quantile Regression
inferredcites →https://doi.org/10.1080/07350015.2011.648859openalex-snapshot
Real-Time Forecasts of the Real Price of Oil
inferredcites →https://doi.org/10.1080/07350015.2014.949342openalex-snapshot
Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach
inferredcites →https://doi.org/10.1093/0198296940.001.0001openalex-snapshot
Strategic Asset Allocation
inferredcites →https://doi.org/10.1093/biomet/asr052openalex-snapshot
Wild bootstrap for quantile regression
inferredcites →https://doi.org/10.1093/jjfinec/nbu021openalex-snapshot
Conditional Skewness with Quantile Regression Models: SoFiE Presidential Address and a Tribute to Hal White
inferredcites →https://doi.org/10.1093/rfs/hhp041openalex-snapshot
Expected Idiosyncratic Skewness
inferredcites →https://doi.org/10.1093/rof/rfn002openalex-snapshot
Short-Run Pain, Long-Run Gain: Financial Liberalization and Stock Market Cycles
inferredcites →https://doi.org/10.1111/0022-1082.00333openalex-snapshot
Optimal Portfolio Choice for Long‐Horizon Investors with Nontradable Labor Income
inferredcites →https://doi.org/10.1111/1468-0084.00234openalex-snapshot
Break Point Estimation and Spurious Rejections With Endogenous Unit Root Tests
inferredcites →https://doi.org/10.1111/iere.12074openalex-snapshot
WHAT CENTRAL BANKERS NEED TO KNOW ABOUT FORECASTING OIL PRICES
inferredcites →https://doi.org/10.1111/j.0022-2879.2007.00036.xopenalex-snapshot
Quantifying the Risk of Deflation
inferredcites →https://doi.org/10.1111/j.1468-0084.2011.00683.xopenalex-snapshot
Quantile Autoregressive Distributed Lag Model with an Application to House Price Returns*
inferredcites →https://doi.org/10.1111/j.1540-6261.2012.01795.xopenalex-snapshot
Ex Ante Skewness and Expected Stock Returns
inferredcites →https://doi.org/10.1111/jofi.12420openalex-snapshot
Why Invest in Emerging Markets? The Role of Conditional Return Asymmetry
inferredcites →https://doi.org/10.1111/obes.12065openalex-snapshot
Asymmetric Quantile Persistence and Predictability: The Case of U.S. Inflation
inferredcites →https://doi.org/10.1111/opec.12117openalex-snapshot
The physical market and the WTI/Brent price spread
inferredcites →https://doi.org/10.1198/016214506000000672openalex-snapshot
Quantile Autoregression
inferredcites →https://doi.org/10.1198/073500104000000370openalex-snapshot
CAViaR
inferredcites →https://doi.org/10.1257/aer.96.5.1631openalex-snapshot
Globalization and Emerging Markets: With or Without Crash?
inferredcites →https://doi.org/10.17016/ifdp.2012.1050openalex-snapshot
Do Oil Prices Help Forecast U.S. Real GDP?: The Role of Nonlinearities and Asymmetries
inferredcites →https://doi.org/10.2139/ssrn.2185958openalex-snapshot
Tails of Inflation Forecasts and Tales of Monetary Policy
inferredcites →https://doi.org/10.2139/ssrn.795386openalex-snapshot
Time-Varying Conditional Skewness and the Market Risk Premium
inferredcites →https://doi.org/10.22004/ag.econ.235728openalex-snapshot
Estimating relative price impact: The case of Brent and WTI
inferredcites →https://doi.org/10.2307/2171846openalex-snapshot
Efficient Tests for an Autoregressive Unit Root
inferredcites →https://doi.org/10.24149/gwp370openalex-snapshot
Oil Curse, Economic Growth and Trade Openness
inferredcites →https://doi.org/10.3390/ijerph19020899openalex-snapshot
Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4647090openalex-extracted
inferred← citeshttps://doi.org/10.1002/for.3272openalex-snapshot
A Novel Hybrid Nonlinear Forecasting Model for Interval‐Valued Gas Prices
inferred← citeshttps://doi.org/10.1002/for.3289openalex-snapshot
Forecasting Energy Efficiency in Manufacturing: Impact of Technological Progress in Productive Service and Commodity Trades
inferred← citeshttps://doi.org/10.1002/for.70070openalex-snapshot
DKformer: A Novel Transformer‐Based Model for Interval‐Valued Crude Oil Price Forecasting
inferred← citeshttps://doi.org/10.1016/j.jcomm.2024.100409openalex-snapshot
The asymmetric effect of G7 stock market volatility on predicting oil price volatility: Evidence from quantile autoregression model
inferred← citeshttps://doi.org/10.1016/j.net.2026.104335openalex-snapshot
Nuclear energy as a path towards coping with energy consumption risk in 29 nuclear energy-producing countries: The quantile regression approach
inferred← citeshttps://doi.org/10.1080/00036846.2025.2468937openalex-snapshot
How uncertain are oil prices? bayesian regularized distribution neural network for forecasting oil prices
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9287691/v1openalex-snapshot
An outlier-robust dynamic semiparametric model for jointly forecasting value-at-risk and expected shortfall
inferred← citeshttps://doi.org/10.26425/1816-4277-2023-3-60-70openalex-snapshot
Analysis of the state of the gas market in the context of the aggravation of the Ukrainian crisis
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.