SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Oil futures volatility predictability: New evidence based on machine learning models
16 references·2 citing·40 second hop
Oil futures volatility predictability: New evidence based on machine learning models (2022)Oil futures volatility predictability: Ne…Empirical Asset Pricing via Machine Learning (2020)Empirical Asset Pricing via M…A CNN–LSTM model for gold price time-series forecasting (2020)A CNN–LSTM model for gold pri…Machine learning in energy economics and finance: A review (2019)Machine learning in energy ec…The M4 Competition: Results, findings, conclusion and way forward (2018)The M4 Competition: Results, …Forecasting the Equity Risk Premium: The Role of Technical Indicators (2014)Forecasting the Equity Risk P…Stock Market Volatility and Macroeconomic Fundamentals (2012)Stock Market Volatility and M…‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables (2012)‘Déjà vol’: Predictive regres…What do we learn from the price of crude oil futures? (2010)What do we learn from the pri…Volatility forecast comparison using imperfect volatility proxies (2010)Volatility forecast compariso…THE IMPACT OF OIL PRICE SHOCKS ON THE U.S. STOCK MARKET* (2009)THE IMPACT OF OIL PRICE SHOCK…Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy (2009)Out-of-Sample Equity Premium …Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm (2008)Forecasting crude oil price w…Chapter 4 Forecast Combinations (2006)Chapter 4 Forecast Combinatio…Approximately normal tests for equal predictive accuracy in nested models (2006)Approximately normal tests fo…Combination forecasts of output growth in a seven‐country data set (2004)Combination forecasts of outp…Combining forecasts: A review and annotated bibliography (1989)Combining forecasts: A review…Enhancing cryptocurrency market volatility forecasting with daily dynamic tuning strategy (2024)Enhancing cryptocurrency mark…Nonlinear asset pricing in Chinese stock market: A deep learning approach (2023)Nonlinear asset pricing in Ch…Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts (1998)The distribution of realized stock return volatility (2001)Modeling and Forecasting Realized Volatility (2003)Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes (2012)The VIX, the variance premium and stock market volatility (2014)Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility (2015)The Combination of Forecasts (1969)The M3-Competition: results, conclusions and implications (2000)Investor Sentiment Aligned: A Powerful Predictor of Stock Returns (2014)Forecasting: theory and practice (2022)Stock Returns, Real Activity, Inflation, and Money (1981)What is an Oil Shock? (2000)Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market (2009)Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries (2011)THE ROLE OF INVENTORIES AND SPECULATIVE TRADING IN THE GLOBAL MARKET FOR CRUDE OIL (2013)Oil prices, US stock return, and the dependence between their quantiles (2015)Expert judgment: Some necessary conditions and an example. (1974)25 years of time series forecasting (2006)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)By Force of Habit: A Consumption‐Based Explanation of Aggregate Stock Market Behavior (1999)Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles (2000)Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets (2016)Climate policy uncertainty and world renewable energy index volatility forecasting (2022)Geopolitical risk and dynamic connectedness between commodity markets (2022)Forecasting Using Principal Components From a Large Number of Predictors (2002)Macroeconomic Forecasting Using Diffusion Indexes (2002)Forecasting Stock Returns (2013)Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 (2016)The General Theory of Employment, Interest and Money. (1936)Common risk factors in the returns on stocks and bonds (1993)On Persistence in Mutual Fund Performance (1997)Deep Direct Reinforcement Learning for Financial Signal Representation and Trading (2016)Short interest and aggregate stock returns (2016)Comparing Predictive Accuracy (1994)Manager sentiment and stock returns (2018)A five-factor asset pricing model (2014)Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis (2021)Machine learning advances for time series forecasting (2021)Machine learning in the Chinese stock market (2021)Policy Uncertainty and Corporate Investment (2015)
this work references cited by second hop

References

  • 2020Empirical Asset Pricing via Machine Learning2,528 cited
  • 2006Approximately normal tests for equal predictive accuracy in nested models2,513 cited
  • 1989Combining forecasts: A review and annotated bibliography2,170 cited
  • 2009THE IMPACT OF OIL PRICE SHOCKS ON THE U.S. STOCK MARKET*1,837 cited
  • 2009Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy1,684 cited
  • 2006Chapter 4 Forecast Combinations1,318 cited
  • 2010Volatility forecast comparison using imperfect volatility proxies1,281 cited
  • 2004Combination forecasts of output growth in a seven‐country data set1,138 cited
  • 2012Stock Market Volatility and Macroeconomic Fundamentals1,122 cited
  • 2014Forecasting the Equity Risk Premium: The Role of Technical Indicators1,001 cited
  • 2020A CNN–LSTM model for gold price time-series forecasting869 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
  • 2018The M4 Competition: Results, findings, conclusion and way forward634 cited
  • 2012‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

486 cited
  • 2019Machine learning in energy economics and finance: A review438 cited
  • Cited by

    • 2024Enhancing cryptocurrency market volatility forecasting with daily dynamic tuning strategy25 cited
    • 2023Nonlinear asset pricing in Chinese stock market: A deep learning approach22 cited