Modelling returns volatility: mixed-frequency model based on momentum of predictability

References

• Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885–905. https://doi.org/10.2307/2527343
   Web of Science ®Google Scholar
• Andersen, T. G., Dobrev, D., & Schaumburg, E. (2012). Jump-robust volatility estimation using nearest neighbor truncation. Journal of Econometrics, 169(1), 75–93. https://doi.org/10.1016/j.jeconom.2012.01.011
   Web of Science ®Google Scholar
• Andreou, E. (2016). On the use of high frequency measures of volatility in MIDAS regressions. Journal of Econometrics, 193(2), 367–389. https://doi.org/10.1016/j.jeconom.2016.04.012
   Web of Science ®Google Scholar
• Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1), 3–30. https://doi.org/10.1016/S0304-4076(95)01749-6
   Web of Science ®Google Scholar
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
   Web of Science ®Google Scholar
• Cai, G., Wu, Z., & Peng, L. (2021). Forecasting volatility with outliers in Realized GARCH models. Journal of Forecasting, 40(4), 667–685. https://doi.org/10.1002/for.2736
   Web of Science ®Google Scholar
• Catania, L., & Proietti, T. (2020). Forecasting volatility with time-varying leverage and volatility of volatility effects. International Journal of Forecasting, 36(4), 1301–1317. https://doi.org/10.1016/j.ijforecast.2020.01.003
   Web of Science ®Google Scholar
• Christensen, K., & Podolskij, M. (2007). Realized range-based estimation of integrated variance. Journal of Econometrics, 141(2), 323–349. https://doi.org/10.1016/j.jeconom.2006.06.012
   Web of Science ®Google Scholar
• Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 174–196. https://doi.org/10.1093/jjfinec/nbp001
   Web of Science ®Google Scholar
• Degiannakis, S., & Filis, G. (2022). Oil price volatility forecasts: What do investors need to know? Journal of International Money and Finance, 123, 102594. https://doi.org/10.1016/j.jimonfin.2021.102594
   Web of Science ®Google Scholar
• Hansen, B. E. (1994). Autoregressive conditional density estimation. International Economic Review, 35(3), 705–730. https://doi.org/10.2307/2527081
   Web of Science ®Google Scholar
• Hansen, P. R., Huang, Z., & Shek, H. H. (2012). Realized GARCH: A joint model for returns and realized measures of volatility. Journal of Applied Econometrics, 27(6), 877–906. https://doi.org/10.1002/jae.1234
   Web of Science ®Google Scholar
• Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: Does anything beat a GARCH(1,1). Journal of Applied Econometrics, 20(7), 873–889. https://doi.org/10.1002/jae.800
   Web of Science ®Google Scholar
• Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497.
   Web of Science ®Google Scholar
• Hongwiengjan, W., & Thongtha, D. (2021). An analytical approximation of option prices via TGARCH model. Economic Research-Ekonomska Istraživanja, 34(1), 948–969. https://doi.org/10.1080/1331677X.2020.1805636
   Web of Science ®Google Scholar
• Hung, J. C., Liu, H. C., & Yang, J. J. (2020). Improving the realized GARCH’s volatility forecast for bitcoin with jump-robust estimators. The North American Journal of Economics and Finance, 52, 101165. https://doi.org/10.1016/j.najef.2020.101165
   Web of Science ®Google Scholar
• Huang, Z., Liu, H., & Wang, T. (2016). Modeling long memory volatility using realized measures of volatility: A realized HAR GARCH model. Economic Modelling, 52, 812–821. https://doi.org/10.1016/j.econmod.2015.10.018
   Web of Science ®Google Scholar
• Jiang, W., Ruan, Q., Li, J., & Li, Y. (2018). Modeling returns volatility: Realized GARCH incorporating realized risk measure. Physica A: Statistical Mechanics and Its Applications, 500, 249–258. https://doi.org/10.1016/j.physa.2018.02.018
   Web of Science ®Google Scholar
• Kim, J. M., Kim, D. H., & Jung, H. (2021). Estimating yield spreads volatility using GARCH-type model. The North American Journal of Economics and Finance, 57, 101396. https://doi.org/10.1016/j.najef.2021.101396
   Web of Science ®Google Scholar
• Liu, L. Y., Patton, A. J., & Sheppard, K. (2015). Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes. Journal of Econometrics, 187(1), 293–311. https://doi.org/10.1016/j.jeconom.2015.02.008
   Web of Science ®Google Scholar
• Liu, Y., Li, J., & Ng, A. C. (2015). Option pricing under GARCH models with Hansen's skewed-t distributed innovations. The North American Journal of Economics and Finance, 31, 108–125. https://doi.org/10.1016/j.najef.2014.10.007
   Web of Science ®Google Scholar
• Ma, F., Lu, X., Wang, L., & Chevallier, J. (2021). Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models. Journal of Forecasting, 40(6), 1070–1085. https://doi.org/10.1002/for.2753
   Web of Science ®Google Scholar
• Mei, D., Ma, F., Liao, Y., & Wang, L. (2020). Geopolitical risk uncertainty and oil future volatility: Evidence from MIDAS models. Energy Economics, 86, 104624. https://doi.org/10.1016/j.eneco.2019.104624
   Web of Science ®Google Scholar
• Miao, H., Ramchander, S., Wang, T., & Yang, D. (2017). Role of index futures on China’s stock markets: Evidence from price discovery and volatility spillover. Pacific-Basin Finance Journal, 44, 13–26. https://doi.org/10.1016/j.pacfin.2017.05.003
   Web of Science ®Google Scholar
• Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347. https://doi.org/10.2307/2938260
   Web of Science ®Google Scholar
• Pesaran, M. H., & Timmermann, A. (2009). Testing dependence among serially correlated multicategory variables. Journal of the American Statistical Association, 104(485), 325–337. https://doi.org/10.1198/jasa.2009.0113
   Web of Science ®Google Scholar
• Qiao, G., Teng, Y., Li, W., & Liu, W. (2019). Improving volatility forecasting based on Chinese volatility index information: Evidence from CSI 300 index and futures markets. The North American Journal of Economics and Finance, 49, 133–151. https://doi.org/10.1016/j.najef.2019.04.003
   Web of Science ®Google Scholar
• Shang, Y., & Zheng, T. (2021). Mixed-frequency SV model for stock volatility and macroeconomics. Economic Modelling, 95, 462–472. https://doi.org/10.1016/j.econmod.2020.03.013
   Web of Science ®Google Scholar
• Smetanina, E. (2017). Real-time GARCH. Journal of Financial Econometrics, 15(4), 561–601. https://doi.org/10.1093/jjfinec/nbx008
   Web of Science ®Google Scholar
• Tian, S., & Hamori, S. (2015). Modeling interest rate volatility: A Realized GARCH approach. Journal of Banking & Finance, 61, 158–171. https://doi.org/10.1016/j.jbankfin.2015.09.008
   Web of Science ®Google Scholar
• Wang, L., Ma, F., Liu, J., & Yang, L. (2020). Forecasting stock price volatility: New evidence from the GARCH-MIDAS model. International Journal of Forecasting, 36(2), 684–694. https://doi.org/10.1016/j.ijforecast.2019.08.005
   Web of Science ®Google Scholar
• Wang, Y., Liu, L., Ma, F., & Diao, X. (2018). Momentum of return predictability. Journal of Empirical Finance, 45, 141–156. https://doi.org/10.1016/j.jempfin.2017.11.003
   Web of Science ®Google Scholar
• Wang, Y., & Wu, C. (2012). Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models? Energy Economics, 34(6), 2167–2181. https://doi.org/10.1016/j.eneco.2012.03.010
   Web of Science ®Google Scholar
• Wang, Y., Xiang, Y., Lei, X., & Zhou, Y. (2022). Volatility analysis based on GARCH-type models: Evidence from the Chinese stock market. Economic Research-Ekonomska Istraživanja, 35(1), 2530–2554. https://doi.org/10.1080/1331677X.2021.1967771
   Web of Science ®Google Scholar
• Wu, X., Xia, M., & Zhang, H. (2020). Forecasting VaR using realized EGARCH model with skewness and kurtosis. Finance Research Letters, 32, 101090. https://doi.org/10.1016/j.frl.2019.01.002
   Web of Science ®Google Scholar
• Yu, J. (2005). On leverage in a stochastic volatility model. Journal of Econometrics, 127(2), 165–178. https://doi.org/10.1016/j.jeconom.2004.08.002
   Web of Science ®Google Scholar
• Yu, X., & Huang, Y. (2021). The impact of economic policy uncertainty on stock volatility: Evidence from GARCH–MIDAS approach. Physica A: Statistical Mechanics and Its Applications, 570, 125794. https://doi.org/10.1016/j.physa.2021.125794
   Web of Science ®Google Scholar
• Zhang, N., Wang, A., Haq, Naveed-Ul., & Nosheen, S. (2022). The impact of COVID-19 shocks on the volatility of stock markets in technologically advanced countries. Economic Research-Ekonomska Istraživanja, 35(1), 2191–2216. https://doi.org/10.1080/1331677X.2021.1936112
   Web of Science ®Google Scholar
• Zhang, Y., Ma, F., Wang, T., & Liu, L. (2019). Out-of-sample volatility prediction: A new mixed‐frequency approach. Journal of Forecasting, 38(7), 669–680. https://doi.org/10.1002/for.2590
   Web of Science ®Google Scholar