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Forecasting Crude Oil Prices: A Comparison Between Artificial Neural Networks and Vector Autoregressive Models
16 references·9 citing·45 second hop
Forecasting Crude Oil Prices: A Comparison Between Artificial Neural Networks and Vector Autoregressive Models (2017)Forecasting Crude Oil Prices: A Compariso…THE ROLE OF INVENTORIES AND SPECULATIVE TRADING IN THE GLOBAL MARKET FOR CRUDE OIL (2013)THE ROLE OF INVENTORIES AND S…Real-Time Forecasts of the Real Price of Oil (2012)Real-Time Forecasts of the Re…What do we learn from the price of crude oil futures? (2010)What do we learn from the pri…Forecasting crude oil market volatility: Further evidence using GARCH-class models (2010)Forecasting crude oil market …The effects of crude oil shocks on stock market shifts behaviour: A regime switching approach (2009)The effects of crude oil shoc…Understanding Crude Oil Prices (2008)Understanding Crude Oil PricesOil price shocks and stock markets in the U.S. and 13 European countries (2008)Oil price shocks and stock ma…Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm (2008)Forecasting crude oil price w…Market-Based Measures of Monetary Policy Expectations (2007)Market-Based Measures of Mone…In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use? (2005)In-Sample or Out-of-Sample Te…A quantitative analysis of oil-price shocks, systematic monetary policy, and economic downturns (2004)A quantitative analysis of oi…Why is it so difficult to beat the random walk forecast of exchange rates? (2003)Why is it so difficult to bea…Exchange Rates and Oil Prices (1998)Exchange Rates and Oil PricesMonopoly and the Rate of Extraction of Exhaustible Resources (1976)Monopoly and the Rate of Extr…Fitting autoregressive models for prediction (1969)Fitting autoregressive models…The Economics of Exhaustible Resources (1931)The Economics of Exhaustible …On the forecasting of multivariate financial time series using hybridization of DCC-GARCH model and multivariate ANNs (2022)On the forecasting of multiva…Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network (2022)Nonlinearity in forecasting e…Investor sentiment and machine learning: Predicting the price of China's crude oil futures market (2022)Investor sentiment and machin…Forecasting crude oil price using LSTM neural networks (2022)Forecasting crude oil price u…Volatility forecasting for crude oil based on text information and deep learning PSO‐LSTM model (2021)Volatility forecasting for cr…Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market (2020)Daily Crude Oil Price Forecas…Improving Adaptive Neuro-Fuzzy Inference System Based on a Modified Salp Swarm Algorithm Using Genetic Algorithm to Forecast Crude Oil Price (2019)Improving Adaptive Neuro-Fuzz…Forecasting stock volatility process using improved least square support vector machine approach (2019)Forecasting stock volatility …Forecasting crude oil prices by a semiparametric Markov switching model: OPEC, WTI, and Brent cases (2018)Forecasting crude oil prices …Time Series Analysis Forecasting and Control. (1971)Time Series Analysis: Forecasting and Control (1977)Time series prediction: Forecasting the future and understanding the past (1994)Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold (2013)Machine learning in energy economics and finance: A review (2019)A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series (2020)Testing the null hypothesis of stationarity against the alternative of a unit root (1992)What is an Oil Shock? (2000)Causes and Consequences of the Oil Shock of 2007-08 (2009)Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries (2011)Financialization of Commodity Markets (2013)Staggered prices in a utility-maximizing framework (1983)Staggered Prices in a Utility-Maximizing Framework (1996)Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory* (2000)Does the Fed Respond to Oil Price Shocks? (2011)Oil price shocks and stock market returns: New evidence from the United States and China (2014)Oil Price Shocks: Causes and Consequences (2014)What are the effects of monetary policy on output? Results from an agnostic identification procedure (2005)Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market (2009)THE IMPACT OF OIL PRICE SHOCKS ON THE U.S. STOCK MARKET* (2009)Speculators, commodities and cross-market linkages (2013)Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks (2019)Expectations and Exchange Rate Dynamics (1976)Comparing Predictive Accuracy (1994)Investor Flows and the 2008 Boom/Bust in Oil Prices (2013)Oil price shocks and stock market activities: Evidence from oil-importing and oil-exporting countries (2013)“Options, Futures, and Other Derivatives” (2016)Forty Years of Oil Price Fluctuations: Why the Price of Oil May Still Surprise Us (2016)Testing for a Unit Root in Time Series Regression (1988)MAXIMUM LIKELIHOOD ESTIMATION AND INFERENCE ON COINTEGRATION — WITH APPLICATIONS TO THE DEMAND FOR MONEY (1990)Renewable energy consumption, CO2 emissions and oil prices in the G7 countries (2009)Oil prices, exchange rates and emerging stock markets (2011)Empirical exchange rate models of the seventies (1983)The Purchasing Power Parity Puzzle (1996)Exchange Rates and Fundamentals (2004)Exchange Rates and Fundamentals (2005)Dividend yields and expected stock returns (1988)The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors (1988)International Stock Return Predictability: What Is the Role of the United States? (2013)Manager sentiment and stock returns (2018)Balanced-budget redistribution as the outcome of political competition (1987)RCP4.5: a pathway for stabilization of radiative forcing by 2100 (2011)Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models (2001)Tourism as a long-run economic growth factor: the Spanish case (2002)Estimation of Panel Vector Autoregression in Stata (2016)
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References

  • 1931The Economics of Exhaustible Resources4,535 cited
  • 1969Fitting autoregressive models for prediction2,483 cited
  • 2013THE ROLE OF INVENTORIES AND SPECULATIVE TRADING IN THE GLOBAL MARKET FOR CRUDE OIL1,439 cited
  • 2008Understanding Crude Oil Prices1,270 cited
  • 2008Oil price shocks and stock markets in the U.S. and 13 European countries1,264 cited
  • 2003Why is it so difficult to beat the random walk forecast of exchange rates?784 cited
  • 2005In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?756 cited
  • 2010What do we learn from the price of crude oil futures?744 cited
  • 2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm705 cited
  • 2004A quantitative analysis of oil-price shocks, systematic monetary policy, and economic downturns392 cited
  • 2010Forecasting crude oil market volatility: Further evidence using GARCH-class models374 cited
  • 2007Market-Based Measures of Monetary Policy Expectations373 cited
  • 1976Monopoly and the Rate of Extraction of Exhaustible Resources360 cited
  • 2012Real-Time Forecasts of the Real Price of Oil356 cited
  • 1998Exchange Rates and Oil Prices

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303 cited
  • 2009The effects of crude oil shocks on stock market shifts behaviour: A regime switching approach260 cited
  • Cited by

    • 2022Investor sentiment and machine learning: Predicting the price of China's crude oil futures market74 cited
    • 2019Improving Adaptive Neuro-Fuzzy Inference System Based on a Modified Salp Swarm Algorithm Using Genetic Algorithm to Forecast Crude Oil Price62 cited
    • 2018Forecasting crude oil prices by a semiparametric Markov switching model: OPEC, WTI, and Brent cases60 cited
    • 2021Volatility forecasting for crude oil based on text information and deep learning PSO‐LSTM model42 cited
    • 2022Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network39 cited
    • 2019Forecasting stock volatility process using improved least square support vector machine approach38 cited
    • 2020Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market35 cited
    • 2022Forecasting crude oil price using LSTM neural networks34 cited
    • 2022On the forecasting of multivariate financial time series using hybridization of DCC-GARCH model and multivariate ANNs27 cited