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Research Article

Combination forecast based on financial stress categories for global equity market volatility: the evidence during the COVID-19 and the global financial crisis periods

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References

  • Bekaert, G., and M. Hoerova. 2014. “The VIX, the Variance Premium and Stock Market Volatility.” Journal of Econometrics 183 (2): 181–192. doi:10.1016/j.jeconom.2014.05.008.
  • Ben Omrane, W., and T. Savaşer. 2017. “Exchange Rate Volatility Response to Macroeconomic News During the Global Financial Crisis.” International Review of Financial Analysis 52: 130–143. doi:10.1016/j.irfa.2017.05.006.
  • Blot, C., J. Creel, P. Hubert, F. Labondance, and F. Saraceno. 2015. “Assessing the Link Between Price and Financial Stability.” Journal of Financial Stability 16: 71–88. doi:10.1016/j.jfs.2014.12.003.
  • Bollerslev, T., A. J. Patton, and R. Quaedvlieg. 2016. “Exploiting the Errors: A Simple Approach for Improved Volatility Forecasting.” Journal of Econometrics 192 (1): 1–18. doi:10.1016/j.jeconom.2015.10.007.
  • Byun, S. J., B. Frijns, and T. Y. Roh. 2018. “A Comprehensive Look at the Return Predictability of Variance Risk Premia.” Journal of Futures Markets 38 (4): 425–445. doi:10.1002/fut.21882.
  • Campbell, J. Y., and S. B. Thompson. 2008. “Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?” The Review of Financial Studies 21 (4): 1509–1531. doi:10.1093/rfs/hhm055.
  • Catania, L., and T. Proietti. 2020. “Forecasting Volatility with Time-Varying Leverage and Volatility of Volatility Effects.” International Journal of Forecasting 36 (4): 1301–1317. doi:10.1016/j.ijforecast.2020.01.003.
  • Cevik, E. I., S. Dibooglu, and A. M. Kutan. 2013. “Measuring Financial Stress in Transition Economies.” Journal of Financial Stability 9 (4): 597–611. doi:10.1016/j.jfs.2012.10.001.
  • Choudhry, T., F. I. Papadimitriou, and S. Shabi. 2016. “Stock Market Volatility and Business Cycle: Evidence from Linear and Nonlinear Causality Tests.” Journal of Banking & Finance 66: 89–101. doi:10.1016/j.jbankfin.2016.02.005.
  • Claeskens, G., J. R. Magnus, A. L. Vasnev, and W. Wang. 2016. “The Forecast Combination Puzzle: A Simple Theoretical Explanation.” International Journal of Forecasting 32 (3): 754–762. doi:10.1016/j.ijforecast.2015.12.005.
  • Clark, T. E., and K. D. West. 2007. “Approximately Normal Tests for Equal Predictive Accuracy in Nested Models.” Journal of Econometrics 138 (1): 291–311. doi:10.1016/j.jeconom.2006.05.023.
  • Corsi, F. 2009. “A Simple Approximate Long-Memory Model of Realized Volatility.” Journal of Financial Econometrics 7 (2): 174–196. doi:10.1093/jjfinec/nbp001.
  • Corsi, F., and R. Renò. 2012. “Discrete-Time Volatility Forecasting with Persistent Leverage Effect and the Link with Continuous-Time Volatility Modeling.” Journal of Business & Economic Statistics 30 (3): 368–380. doi:10.1080/07350015.2012.663261.
  • Das, D., D. Maitra, A. Dutta, and S. Basu. 2022. “Financial Stress and Crude Oil Implied Volatility: New Evidence from Continuous Wavelet Transformation Framework.” Energy Economics 115: 106388. doi:10.1016/j.eneco.2022.106388.
  • Degiannakis, S., and G. Filis. 2017. “Forecasting Oil Price Realized Volatility Using Information Channels from Other Asset Classes.” Journal of International Money & Finance 76: 28–49. doi:10.1016/j.jimonfin.2017.05.006.
  • Elgammal, M. M., W. M. A. Ahmed, and A. Alshami. 2021. “Price and Volatility Spillovers Between Global Equity, Gold, and Energy Markets Prior to and During the COVID-19 Pandemic.” Resources Policy 74: 102334. doi:10.1016/j.resourpol.2021.102334.
  • Gkillas, K., R. Gupta, and C. Pierdzioch. 2020. “Forecasting Realized Oil-Price Volatility: The Role of Financial Stress and Asymmetric Loss.” Journal of International Money & Finance 104: 102137. doi:10.1016/j.jimonfin.2020.102137.
  • Graefe, A., J. S. Armstrong, R. J. Jones, and A. G. Cuzán. 2014. “Combining Forecasts: An Application to Elections.” International Journal of Forecasting 30 (1): 43–54. doi:10.1016/j.ijforecast.2013.02.005.
  • Hsu, Y. -L., and L. Tang. 2022. “Effects of Investor Sentiment and Country Governance on Unexpected Conditional Volatility During the COVID-19 Pandemic: Evidence from Global Stock Markets.” International Review of Financial Analysis 82: 102186. doi:10.1016/j.irfa.2022.102186.
  • Huang, D., F. Jiang, G. Tong, and G. Zhou. 2019. “Scaled PCA: A New Approach to Dimension Reduction.” SSRN Electronic Journal. doi:10.2139/ssrn.3358911.
  • Li, Y., C. Liang, F. Ma, and J. Wang. 2020. “The Role of the IDEMV in Predicting European Stock Market Volatility During the COVID-19 Pandemic.” Finance Research Letters 36: 101749. doi:10.1016/j.frl.2020.101749.
  • Liang, C., Y. Li, F. Ma, and Y. Wei. 2021. “Global Equity Market Volatilities Forecasting: A Comparison of Leverage Effects, Jumps, and Overnight Information.” International Review of Financial Analysis 75: 101750. doi:10.1016/j.irfa.2021.101750.
  • Melvin, M., and M. P. Taylor. 2009. “The Crisis in the Foreign Exchange Market.” Journal of International Money & Finance 28 (8): 1317–1330. doi:10.1016/j.jimonfin.2009.08.006.
  • Nazlioglu, S., U. Soytas, and R. Gupta. 2015. “Oil Prices and Financial Stress: A Volatility Spillover Analysis.” Energy Policy 82: 278–288. doi:10.1016/j.enpol.2015.01.003.
  • Pang, D., F. Ma, M. I. M. Wahab, and B. Zhu. 2023. “Financial Stress and Oil Market Volatility: New Evidence.” Applied Economics Letters 30 (1): 1–6. doi:10.1080/13504851.2021.1969333.
  • Patton, A. J., and K. Sheppard. 2015. “Good Volatility, Bad Volatility: Signed Jumps and the Persistence of Volatility.” The Review of Economics and Statistics 97 (3): 683–697. doi:10.1162/REST_a_00503.
  • Pesaran, M. H., and A. Timmermann. 1992. “A Simple Nonparametric Test of Predictive Performance.” Journal of Business & Economic Statistics 10 (4): 461–465. doi:10.1080/07350015.1992.10509922.
  • Proaño, C. R., C. Schoder, and W. Semmler. 2014. “Financial Stress, Sovereign Debt and Economic Activity in Industrialized Countries: Evidence from Dynamic Threshold Regressions.” Journal of International Money & Finance 45: 17–37. doi:10.1016/j.jimonfin.2014.02.005.
  • Rapach, D. E., J. K. Strauss, and G. Zhou. 2010. “Out-Of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy.” The Review of Financial Studies 23 (2): 821–862. doi:10.1093/rfs/hhp063.
  • Rapach, D. E., and G. Zhou. 2020. “Time-Series and Cross-Sectional Stock Return Forecasting: New Machine Learning Methods.” Machine Learning for Asset Management 1–33.
  • Smith, J., and K. F. Wallis. 2009. “A Simple Explanation of the Forecast Combination Puzzle*.” Oxford Bulletin of Economics and Statistics 71 (3): 331–355. doi:10.1111/j.1468-0084.2008.00541.x.
  • Stock, J. H., and M. W. Watson. 2004. “Combination Forecasts of Output Growth in a Seven-Country Data Set.” Journal of Forecasting 23 (6): 405–430. doi:10.1002/for.928.
  • Wang, L., F. Ma, J. Hao, and X. Gao. 2021. “Forecasting Crude Oil Volatility with Geopolitical Risk: Do Time-Varying Switching Probabilities Play a Role?” International Review of Financial Analysis 76: 101756. doi:10.1016/j.irfa.2021.101756.
  • Weng, F., H. Zhang, and C. Yang. 2021. “Volatility Forecasting of Crude Oil Futures Based on a Genetic Algorithm Regularization Online Extreme Learning Machine with a Forgetting Factor: The Role of News During the COVID-19 Pandemic.” Resources Policy 73: 102148. doi:10.1016/j.resourpol.2021.102148.
  • Xiao, L. L., V. Boasson, S. Shishlenin, and V. Makushina. 2018. “Volatility Forecasting: Combinations of Realized Volatility Measures and Forecasting Models.” Applied Economics 50 (13): 1428–1441. doi:10.1080/00036846.2017.1363863.
  • Yang, K., F. P. Tian, L. N. Chen, and S. Li. 2017. “Realized Volatility Forecast of Agricultural Futures Using the HAR Models with Bagging and Combination Approaches.” International Review of Economics & Finance 49: 276–291. doi:10.1016/j.iref.2017.01.030.
  • Zeng, Q., X. Lu, T. Li, and L. Wu. 2022. “Jumps and Stock Market Variance During the COVID-19 Pandemic: Evidence from International Stock Markets.” Finance Research Letters 48: 102896. doi:10.1016/j.frl.2022.102896.
  • Zhang, Y., F. Ma, and Y. Liao. 2020. “Forecasting Global Equity Market Volatilities.” International Journal of Forecasting 36 (4): 1454–1475. doi:10.1016/j.ijforecast.2020.02.007.

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