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Using low frequency information for predicting high frequency variables

Citations

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Cited by:

  1. Tang, Zhenpeng & Lin, Qiaofeng & Cai, Yi & Chen, Kaijie & Liu, Dinggao, 2024. "Harnessing the power of real-time forum opinion: Unveiling its impact on stock market dynamics using intraday high-frequency data in China," International Review of Financial Analysis, Elsevier, vol. 93(C).
  2. Das, Sonali & Demirer, Riza & Gupta, Rangan & Mangisa, Siphumlile, 2019. "The effect of global crises on stock market correlations: Evidence from scalar regressions via functional data analysis," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 132-147.
  3. Layna Mosley & Victoria Paniagua & Erik Wibbels, 2020. "Moving markets? Government bond investors and microeconomic policy changes," Economics and Politics, Wiley Blackwell, vol. 32(2), pages 197-249, July.
  4. Xu, Qifa & Li, Mengting & Jiang, Cuixia & He, Yaoyao, 2019. "Interconnectedness and systemic risk network of Chinese financial institutions: A LASSO-CoVaR approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
  5. del Barrio Castro, Tomás & Hecq, Alain, 2016. "Testing for deterministic seasonality in mixed-frequency VARs," Economics Letters, Elsevier, vol. 149(C), pages 20-24.
  6. Hongyu An & Boping Tian, 2025. "Unleashing the Potential of Mixed Frequency Data: Measuring Risk with Dynamic Tail Index Regression Model," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1567-1615, March.
  7. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
  8. Salisu, Afees A. & Gupta, Rangan & Demirer, Riza, 2022. "Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model," Energy Economics, Elsevier, vol. 108(C).
  9. Jiawen Luo & Shengjie Fu & Oguzhan Cepni & Rangan Gupta, 2025. "The Role of Uncertainty in Forecasting Realized Covariance of US State-Level Stock Returns: A Reverse-MIDAS Approach," Working Papers 202501, University of Pretoria, Department of Economics.
  10. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2023. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Economic Modelling, Elsevier, vol. 120(C).
  11. Goodness C. Aye & Christina Christou & Rangan Gupta & Christis Hassapis, 2024. "High-Frequency Contagion between Aggregate and Regional Housing Markets of the United States with Financial Assets: Evidence from Multichannel Tests," The Journal of Real Estate Finance and Economics, Springer, vol. 69(2), pages 253-276, August.
  12. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
  13. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2020. "Markov-Switching Three-Pass Regression Filter," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 285-302, April.
  14. Alexander, Carol & Rauch, Johannes, 2021. "A general property for time aggregation," European Journal of Operational Research, Elsevier, vol. 291(2), pages 536-548.
  15. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
  16. Tom Dudda & Tony Klein & Duc Khuong Nguyen & Thomas Walther, 2022. "Common Drivers of Commodity Futures?," Working Papers 2207, Utrecht School of Economics.
  17. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yu, Keming, 2020. "Mixed data sampling expectile regression with applications to measuring financial risk," Economic Modelling, Elsevier, vol. 91(C), pages 469-486.
  18. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2022. "Mixed‐frequency forecasting of crude oil volatility based on the information content of global economic conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 134-157, January.
  19. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024. "Forecasting Realized US Stock Market Volatility: Is there a Role for Economic Policy Uncertainty?," Working Papers 202408, University of Pretoria, Department of Economics.
  20. Polat, Onur & Cunado, Juncal & Cepni, Oguzhan & Gupta, Rangan, 2025. "Oil price shocks and the connectedness of US state-level financial markets," Energy Economics, Elsevier, vol. 141(C).
  21. Thach Pham & Deepa Bannigidadmath & Robert Powell, 2025. "Industry return predictability using health policy uncertainty," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-42, December.
  22. Joel Hasbrouck, 2021. "Rejoinder on: Price Discovery in High Resolution," Journal of Financial Econometrics, Oxford University Press, vol. 19(3), pages 465-471.
  23. Alain Hecq & Marie Ternes & Ines Wilms, 2025. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(6), pages 1946-1968, September.
  24. Li, Lue & Long, Jun & Yuan, Meilan, 2025. "Novel wind speed ensemble forecasting system based on the critic weighing principle of fuzzy information granulation and reverse mixed-frequency modeling," Energy, Elsevier, vol. 330(C).
  25. Elie Bouri & Rangan Gupta & Luca Rossini, 2022. "The Role of the Monthly ENSO in Forecasting the Daily Baltic Dry Index," Working Papers 202229, University of Pretoria, Department of Economics.
  26. Matteo Bonato & Oğuzhan Çepni & Rangan Gupta & Christian Pierdzioch, 2023. "El Niño, La Niña, and forecastability of the realized variance of agricultural commodity prices: Evidence from a machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 785-801, July.
  27. Mei, Xueting & Wang, Xinyu, 2024. "Forecasting stock volatility using time-distance weighting fundamental’s shocks," Finance Research Letters, Elsevier, vol. 65(C).
  28. 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.
  29. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).
  30. Çepni, Oğuzhan & Gupta, Rangan & Pienaar, Daniel & Pierdzioch, Christian, 2022. "Forecasting the realized variance of oil-price returns using machine learning: Is there a role for U.S. state-level uncertainty?," Energy Economics, Elsevier, vol. 114(C).
  31. Wenfeng Ma & Yuxuan Hong & Yuping Song, 2024. "On Stock Volatility Forecasting under Mixed-Frequency Data Based on Hybrid RR-MIDAS and CNN-LSTM Models," Mathematics, MDPI, vol. 12(10), pages 1-21, May.
  32. Rangan Gupta & Sarah Nandnaba & Wei Jiang, 2024. "Climate Change and Growth Dynamics," Working Papers 202404, University of Pretoria, Department of Economics.
  33. Le, Trung H., 2020. "Forecasting value at risk and expected shortfall with mixed data sampling," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1362-1379.
  34. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2020. "Markov-Switching Three-Pass Regression Filter," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 285-302, April.
  35. Agostino Consolo & Claudia Foroni & Catalina Martínez Hernández, 2023. "A Mixed Frequency BVAR for the Euro Area Labour Market," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(5), pages 1048-1082, October.
  36. Christian Pierdzioch & Rangan Gupta & Hossein Hassani & Emmanuel Silva, 2018. "Forecasting Changes of Economic Inequality: A Boosting Approach," Working Papers 201868, University of Pretoria, Department of Economics.
  37. You, Yu & Liu, Xiaochun, 2020. "Forecasting short-run exchange rate volatility with monetary fundamentals: A GARCH-MIDAS approach," Journal of Banking & Finance, Elsevier, vol. 116(C).
  38. Xingxuan Zhuo & Shunfei Luo & Yan Cao, 2025. "Exploring Multisource High‐Dimensional Mixed‐Frequency Risks in the Stock Market: A Group Penalized Reverse Unrestricted Mixed Data Sampling Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 459-473, March.
  39. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
  40. Consolo, Agostino & Foroni, Claudia & Martínez Hernández, Catalina, 2021. "A mixed frequency BVAR for the euro area labour market," Working Paper Series 2601, European Central Bank.
  41. Selma Toker & Nimet Özbay & Kristofer Månsson, 2022. "Mixed data sampling regression: Parameter selection of smoothed least squares estimator," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 718-751, July.
  42. Leippold, Markus & Yang, Hanlin, 2019. "Particle filtering, learning, and smoothing for mixed-frequency state-space models," Econometrics and Statistics, Elsevier, vol. 12(C), pages 25-41.
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