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Volatility forecasting and microstructure noise

Citations

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Cited by:

  1. Georgiana-Denisa Banulescu & Bertrand Candelon & Christophe Hurlin & Sébastien Laurent, 2014. "Do We Need Ultra-High Frequency Data to Forecast Variances?," Working Papers halshs-01078158, HAL.
  2. Demirer, Riza & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2019. "Time-varying risk aversion and realized gold volatility," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
  3. Yen-Ju Hsu & Yang-Cheng Lu & J. Jimmy Yang, 2021. "News sentiment and stock market volatility," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 1093-1122, October.
  4. Christos Floros & Konstantinos Gkillas & Christoforos Konstantatos & Athanasios Tsagkanos, 2020. "Realized Measures to Explain Volatility Changes over Time," JRFM, MDPI, vol. 13(6), pages 1-19, June.
  5. Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023. "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 144-164.
  6. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2016. "Exploiting the errors: A simple approach for improved volatility forecasting," Journal of Econometrics, Elsevier, vol. 192(1), pages 1-18.
  7. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 824-839.
  8. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
  9. Mykland, Per Aslak, 2019. "Combining statistical intervals and market prices: The worst case state price distribution," Journal of Econometrics, Elsevier, vol. 212(1), pages 272-285.
  10. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2018. "Forecasting (Good and Bad) Realized Exchange-Rate Volatility: Is there a Role for Realized Skewness and Kurtosis?," Working Papers 201879, University of Pretoria, Department of Economics.
  11. Denisa Banulescu-Radu & Christophe Hurlin & Bertrand Candelon & Sébastien Laurent, 2016. "Do We Need High Frequency Data to Forecast Variances?," Annals of Economics and Statistics, GENES, issue 123-124, pages 135-174.
  12. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
  13. Valenzuela, Marcela & Zer, Ilknur & Fryzlewicz, Piotr & Rheinländer, Thorsten, 2015. "Relative liquidity and future volatility," Journal of Financial Markets, Elsevier, vol. 24(C), pages 25-48.
  14. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
  15. Shirota, Shinichiro & Hizu, Takayuki & Omori, Yasuhiro, 2014. "Realized stochastic volatility with leverage and long memory," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 618-641.
  16. Thomas Dimpfl & Stephan Jank, 2016. "Can Internet Search Queries Help to Predict Stock Market Volatility?," European Financial Management, European Financial Management Association, vol. 22(2), pages 171-192, March.
  17. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
  18. Horta, Eduardo & Ziegelmann, Flavio, 2018. "Dynamics of financial returns densities: A functional approach applied to the Bovespa intraday index," International Journal of Forecasting, Elsevier, vol. 34(1), pages 75-88.
  19. Xu, Yanyan & Liu, Jing & Ma, Feng & Chu, Jielei, 2024. "Liquidity and realized volatility prediction in Chinese stock market: A time-varying transitional dynamic perspective," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 543-560.
  20. Amendola, Alessandra & Braione, Manuela & Candila, Vincenzo & Storti, Giuseppe, 2020. "A Model Confidence Set approach to the combination of multivariate volatility forecasts," International Journal of Forecasting, Elsevier, vol. 36(3), pages 873-891.
  21. O-Chia Chuang & Chenxu Yang, 2022. "Identifying the Determinants of Crude Oil Market Volatility by the Multivariate GARCH-MIDAS Model," Energies, MDPI, vol. 15(8), pages 1-14, April.
  22. Tong Fang & Deyu Miao & Zhi Su & Libo Yin, 2023. "Uncertainty‐driven oil volatility risk premium and international stock market volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 872-904, July.
  23. Siem Jan Koopman & Marcel Scharth, 2012. "The Analysis of Stochastic Volatility in the Presence of Daily Realized Measures," Journal of Financial Econometrics, Oxford University Press, vol. 11(1), pages 76-115, December.
  24. Bannouh, Karim & Martens, Martin & van Dijk, Dick, 2013. "Forecasting volatility with the realized range in the presence of noise and non-trading," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 535-551.
  25. Xu, Qifa & Zhuo, Xingxuan & Jiang, Cuixia & Liu, Xi & Liu, Yezheng, 2018. "Group penalized unrestricted mixed data sampling model with application to forecasting US GDP growth," Economic Modelling, Elsevier, vol. 75(C), pages 221-236.
  26. Su, Zhi & Liu, Peng & Fang, Tong, 2022. "Pandemic-induced fear and stock market returns: Evidence from China," Global Finance Journal, Elsevier, vol. 54(C).
  27. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yuan, Jing, 2018. "Measuring systemic risk of the banking industry in China: A DCC-MIDAS-t approach," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 13-31.
  28. Tianlun Fei & Xiaoquan Liu & Conghua Wen, 2023. "Forecasting stock return volatility: Realized volatility‐type or duration‐based estimators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1594-1621, November.
  29. Weijia Peng & Chun Yao, 2022. "Co-Jumps, Co-Jump Tests, and Volatility Forecasting: Monte Carlo and Empirical Evidence," JRFM, MDPI, vol. 15(8), pages 1-21, July.
  30. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.
  31. Lu, Botao & Ma, Feng & Wang, Jiqian & Ding, Hui & Wahab, M.I.M., 2021. "Harnessing the decomposed realized measures for volatility forecasting: Evidence from the US stock market," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 672-689.
  32. Valenzuela, Marcela & Zer, Ilknur & Fryzlewicz, Piotr & Rheinlander, Thorsten, 2015. "Relative liquidity and future volatility," LSE Research Online Documents on Economics 62181, London School of Economics and Political Science, LSE Library.
  33. Jiang, Cuixia & Xiong, Wei & Xu, Qifa & Liu, Yezheng, 2021. "Predicting default of listed companies in mainland China via U-MIDAS Logit model with group lasso penalty," Finance Research Letters, Elsevier, vol. 38(C).
  34. Cheng, Mingmian & Swanson, Norman R. & Yang, Xiye, 2021. "Forecasting volatility using double shrinkage methods," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 46-61.
  35. Piotr Fryzlewicz & Thorsten Rheinlander & Marcela Valenzuela & Ilknur Zer, 2014. "Relative Liquidity and Future Volatility," Finance and Economics Discussion Series 2014-45, Board of Governors of the Federal Reserve System (U.S.).
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