Predicting energy futures high-frequency volatility using technical indicators: The role of interaction

Aiolfi, 2006, Persistence in forecasting performance and conditional combination strategies, J. Econometrics, 135, 31, 10.1016/j.jeconom.2005.07.015 Andersen, 2001, The distribution of realized stock return volatility, J. Financ. Econ., 61, 43, 10.1016/S0304-405X(01)00055-1 Andersen, 2011, A reduced form framework for modeling volatility of speculative prices based on realized variation measures, J. Econometrics, 160, 176, 10.1016/j.jeconom.2010.03.029 Baker, 2016, Measuring economic policy uncertainty, Q. J. Econ., 131, 1593, 10.1093/qje/qjw024 Barut, 2016, Conditional sure independence screening, J. Amer. Statist. Assoc., 111, 1266, 10.1080/01621459.2015.1092974 Bates, 1969, The combination of forecasts, J. Oper. Res. Soc., 20, 451, 10.1057/jors.1969.103 Borovkova, 2015, News, volatility and jumps: The case of natural gas futures, Quant. Finance, 15, 1217, 10.1080/14697688.2014.986513 Brock, 1992, Simple technical trading rules and the stochastic properties of stock returns, J. Finance, 47, 1731, 10.1111/j.1540-6261.1992.tb04681.x Campbell, 2008, Predicting excess stock returns out of sample: Can anything beat the historical average?, Rev. Financ. Stud., 21, 1509, 10.1093/rfs/hhm055 Catania, 2020, Forecasting volatility with time-varying leverage and volatility of volatility effects, Int. J. Forecast., 36, 1301, 10.1016/j.ijforecast.2020.01.003 Cheung, 1995, Lag order and critical values of the augmented Dickey–Fuller test, J. Bus. Econom. Statist., 13, 277 Christiansen, 2012, A comprehensive look at financial volatility prediction by economic variables, J. Appl. Econometrics, 27, 956, 10.1002/jae.2298 Clark, 2007, Approximately normal tests for equal predictive accuracy in nested models, J. Econometrics, 138, 291, 10.1016/j.jeconom.2006.05.023 Cochrane, 2005, Financial markets and the real economy, 1 Conrad, 1998, An anatomy of trading strategies, Rev. Financ. Stud., 11, 489, 10.1093/rfs/11.3.489 Corsi, 2009, A simple approximate long-memory model of realized volatility, J. Financ. Econom., 7, 174 Cortina, 1993, Interaction, nonlinearity, and multicollinearity: Implications for multiple regression, J. Manag., 19, 915 Da, 2011, In search of attention, J. Finance, 66, 1461, 10.1111/j.1540-6261.2011.01679.x Das, 2007, Yahoo! for amazon: Sentiment extraction from small talk on the Web, Manage. Sci., 53, 1375, 10.1287/mnsc.1070.0704 Deeney, 2015, Sentiment in oil markets, Int. Rev. Financ. Anal., 39, 179, 10.1016/j.irfa.2015.01.005 Degiannakis, 2022, Oil price volatility forecasts: What do investors need to know?, J. Int. Money Finance, 123, 10.1016/j.jimonfin.2021.102594 Ergen, 2016, Asymmetric impacts of fundamentals on the natural gas futures volatility: An augmented GARCH approach, Energy Econ., 56, 64, 10.1016/j.eneco.2016.02.022 Fan, 2008, Sure independence screening for ultrahigh dimensional feature space, J. R. Stat. Soc. Ser. B Stat. Methodol., 70, 849, 10.1111/j.1467-9868.2008.00674.x Gong, 2022, Investor sentiment and stock volatility: New evidence, Int. Rev. Financ. Anal., 80, 10.1016/j.irfa.2022.102028 Gong, 2022, Uncertainty index and stock volatility prediction: evidence from international markets, Financial Innov., 8, 1, 10.1186/s40854-022-00361-6 Gu, 2020, Empirical asset pricing via machine learning, Rev. Financ. Stud., 33, 2223, 10.1093/rfs/hhaa009 Hansen, 2011, The model confidence set, Econometrica, 79, 453, 10.3982/ECTA5771 Hargens, 2009, Product-variable models of interaction effects and causal mechanisms, Soc. Sci. Res., 38, 19, 10.1016/j.ssresearch.2008.05.003 He, 2021, Forecasting crude oil prices: A scaled PCA approach, Energy Econ., 97, 10.1016/j.eneco.2021.105189 Hong, 1999, A unified theory of underreaction, momentum trading, and overreaction in asset markets, J. Finance, 54, 2143, 10.1111/0022-1082.00184 Huang, 2015, Investor sentiment aligned: A powerful predictor of stock returns, Rev. Financ. Stud., 28, 791, 10.1093/rfs/hhu080 Hudson, 2021, Technical trading and cryptocurrencies, Ann. Oper. Res., 297, 191, 10.1007/s10479-019-03357-1 Jarque, 1987, A test for normality of observations and regression residuals, Internat. Statist. Rev., 55, 163, 10.2307/1403192 Kelly, 2015, The three-pass regression filter: A new approach to forecasting using many predictors, J. Econometrics, 186, 294, 10.1016/j.jeconom.2015.02.011 Li, 2019, Investor attention and crude oil prices: Evidence from nonlinear Granger causality tests, Energy Econ., 84, 10.1016/j.eneco.2019.104494 Lin, 2018, Forecasting corporate bond returns with a large set of predictors: An iterated combination approach, Manage. Sci., 64, 4218, 10.1287/mnsc.2017.2734 Liu, 2020, Forecasting stock market volatility: The role of technical variables, Econ. Model., 84, 55, 10.1016/j.econmod.2019.03.007 Neely, 2014, Forecasting the equity risk premium: The role of technical indicators, Manage. Sci., 60, 1772, 10.1287/mnsc.2013.1838 Newey, 1987, A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix, Econometrica, 55, 703, 10.2307/1913610 Panopoulou, 2019, The role of technical indicators in exchange rate forecasting, J. Empir. Financ., 53, 197, 10.1016/j.jempfin.2019.07.004 Paye, 2012, ‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables, J. Financ. Econ., 106, 527, 10.1016/j.jfineco.2012.06.005 Qadan, 2018, Investor sentiment and the price of oil, Energy Econ., 69, 42, 10.1016/j.eneco.2017.10.035 Saunders, 1956, Moderator variables in prediction, Educ. Psychol. Meas., 16, 209, 10.1177/001316445601600205 Sévi, 2014, Forecasting the volatility of crude oil futures using intraday data, European J. Oper. Res., 235, 643, 10.1016/j.ejor.2014.01.019 Stambaugh, 2012, The short of it: Investor sentiment and anomalies, J. Financ. Econ., 104, 288, 10.1016/j.jfineco.2011.12.001 Tibshirani, 1996, Regression shrinkage and selection via the lasso, J. R. Stat. Soc. Ser. B Stat. Methodol., 58, 267 Timmermann, 2006, Chapter 4 forecast combinations, Handb. Econ. Forecast., 1, 135, 10.1016/S1574-0706(05)01004-9 Wang, 2020, Forecasting commodity prices out-of-sample: Can technical indicators help?, Int. J. Forecast., 36, 666, 10.1016/j.ijforecast.2019.08.004 Wang, 2016, Forecasting realized volatility in a changing world: A dynamic model averaging approach, J. Bank. Financ., 64, 136, 10.1016/j.jbankfin.2015.12.010 Weiss, 2018, Forecast combinations in R using the ForecastComb package, R Journal, 10, 10.32614/RJ-2018-052 Welch, 2008, A comprehensive look at the empirical performance of equity premium prediction, Rev. Financ. Stud., 21, 1455, 10.1093/rfs/hhm014 Xiu, 2010, Quasi-maximum likelihood estimation of volatility with high frequency data, J. Econometrics, 159, 235, 10.1016/j.jeconom.2010.07.002 Yang, 2019, Volatility forecasting of crude oil futures: The role of investor sentiment and leverage effect, Resour. Policy, 61, 548, 10.1016/j.resourpol.2018.05.012 Yin, 2016, Predicting the oil prices: Do technical indicators help?, Energy Econ., 56, 338, 10.1016/j.eneco.2016.03.017 Zhang, 2021, Predicting stock market volatility based on textual sentiment: A nonlinear analysis, J. Forecast., 40, 1479, 10.1002/for.2777 Zhang, 2019, Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?, J. Empir. Financ., 54, 97, 10.1016/j.jempfin.2019.08.007 Zhang, 2011, The dynamic influence of advanced stock market risk on international crude oil returns: An empirical analysis, Quant. Finance, 11, 967, 10.1080/14697688.2010.538712 Zhao, 2021, A novel method for online real-time forecasting of crude oil price, Appl. Energy, 303, 10.1016/j.apenergy.2021.117588 Zivot, 2003, Rolling analysis of time series, 299 Zou, 2005, Regularization and variable selection via the elastic net, J. R. Stat. Soc. Ser. B Stat. Methodol., 67, 301, 10.1111/j.1467-9868.2005.00503.x