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Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice

Citations

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Cited by:

  1. Han, Chulwoo & Park, Frank C., 2022. "A geometric framework for covariance dynamics," Journal of Banking & Finance, Elsevier, vol. 134(C).
  2. Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
  3. Jiayuan Zhou & Feiyu Jiang & Ke Zhu & Wai Keung Li, 2019. "Time series models for realized covariance matrices based on the matrix-F distribution," Papers 1903.12077, arXiv.org, revised Jul 2020.
  4. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2019. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Papers 1910.13960, arXiv.org, revised Oct 2020.
  5. Zhang, Hua & Chen, Jinyu & Shao, Liuguo, 2021. "Dynamic spillovers between energy and stock markets and their implications in the context of COVID-19," International Review of Financial Analysis, Elsevier, vol. 77(C).
  6. Elena Ivona Dumitrescu & Georgiana-Denisa Banulescu, 2019. "Do High-frequency-based Measures Improve Conditional Covariance Forecasts?," Post-Print hal-03331122, HAL.
  7. Vassallo, Danilo & Buccheri, Giuseppe & Corsi, Fulvio, 2021. "A DCC-type approach for realized covariance modeling with score-driven dynamics," International Journal of Forecasting, Elsevier, vol. 37(2), pages 569-586.
  8. Luo, Jiawen & Ji, Qiang & Klein, Tony & Todorova, Neda & Zhang, Dayong, 2020. "On realized volatility of crude oil futures markets: Forecasting with exogenous predictors under structural breaks," Energy Economics, Elsevier, vol. 89(C).
  9. Bucci, Andrea & Palomba, Giulio & Rossi, Eduardo, 2023. "The role of uncertainty in forecasting volatility comovements across stock markets," Economic Modelling, Elsevier, vol. 125(C).
  10. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
  11. Ekaterina Seregina, 2020. "A Basket Half Full: Sparse Portfolios," Papers 2011.04278, arXiv.org, revised Apr 2021.
  12. Luo, Jiawen & Klein, Tony & Ji, Qiang & Hou, Chenghan, 2022. "Forecasting realized volatility of agricultural commodity futures with infinite Hidden Markov HAR models," International Journal of Forecasting, Elsevier, vol. 38(1), pages 51-73.
  13. Fabrizio Cipollini & Giampiero Gallo & Alessandro Palandri, 2020. "A Dynamic Conditional Approach to Portfolio Weights Forecasting," Econometrics Working Papers Archive 2020_06, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
  14. Luo, Jiawen & Demirer, Riza & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil and gold volatilities with sentiment indicators under structural breaks," Energy Economics, Elsevier, vol. 105(C).
  15. Xin Jin & John M. Maheu & Qiao Yang, 2019. "Bayesian parametric and semiparametric factor models for large realized covariance matrices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 641-660, August.
  16. Hartkopf, Jan Patrick & Reh, Laura, 2023. "Challenging golden standards in EWMA smoothing parameter calibration based on realized covariance measures," Finance Research Letters, Elsevier, vol. 56(C).
  17. Geert Dhaene & Piet Sercu & Jianbin Wu, 2022. "Volatility spillovers: A sparse multivariate GARCH approach with an application to commodity markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(5), pages 868-887, May.
  18. Hardik A. Marfatia & Qiang Ji & Jiawen Luo, 2022. "Forecasting the volatility of agricultural commodity futures: The role of co‐volatility and oil volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 383-404, March.
  19. Vo, Long Hai & Le, Thai-Ha, 2021. "Eatery, energy, environment and economic system, 1970–2017: Understanding volatility spillover patterns in a global sample," Energy Economics, Elsevier, vol. 100(C).
  20. Chao Zhang & Xingyue Pu & Mihai Cucuringu & Xiaowen Dong, 2023. "Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects," Papers 2308.01419, arXiv.org.
  21. Afees A. Salisu & Riza Demirer & Rangan Gupta, 2023. "Technological Shocks and Stock Market Volatility Over a Century: A GARCH-MIDAS Approach," Working Papers 202308, University of Pretoria, Department of Economics.
  22. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combination," Working Papers 202024, University of California at Riverside, Department of Economics.
  23. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
  24. Jian, Zhihong & Deng, Pingjun & Zhu, Zhican, 2018. "High-dimensional covariance forecasting based on principal component analysis of high-frequency data," Economic Modelling, Elsevier, vol. 75(C), pages 422-431.
  25. Luo, Jiawen & Chen, Langnan, 2020. "Realized volatility forecast with the Bayesian random compressed multivariate HAR model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 781-799.
  26. Qu, Hui & Zhang, Yi, 2022. "Asymmetric multivariate HAR models for realized covariance matrix: A study based on volatility timing strategies," Economic Modelling, Elsevier, vol. 106(C).
  27. Duan, Xiaoping & Xiao, Ya & Ren, Xiaohang & Taghizadeh-Hesary, Farhad & Duan, Kun, 2023. "Dynamic spillover between traditional energy markets and emerging green markets: Implications for sustainable development," Resources Policy, Elsevier, vol. 82(C).
  28. Rafael Alves & Diego S. de Brito & Marcelo C. Medeiros & Ruy M. Ribeiro, 2023. "Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage," Papers 2303.16151, arXiv.org.
  29. Vogler, Jan & Golosnoy, Vasyl, 2023. "Unrestricted maximum likelihood estimation of multivariate realized volatility models," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1063-1074.
  30. Golosnoy, Vasyl & Gribisch, Bastian, 2022. "Modeling and forecasting realized portfolio weights," Journal of Banking & Finance, Elsevier, vol. 138(C).
  31. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.
  32. Andre Lucas & Anne Opschoor & Luca Rossini, 2021. "Tail Heterogeneity for Dynamic Covariance Matrices: the F-Riesz Distribution," Tinbergen Institute Discussion Papers 21-010/III, Tinbergen Institute, revised 11 Jul 2023.
  33. Jan Patrick Hartkopf, 2023. "Composite forecasting of vast-dimensional realized covariance matrices using factor state-space models," Empirical Economics, Springer, vol. 64(1), pages 393-436, January.
  34. Luca Barbaglia & Christophe Croux & Ines Wilms, 2017. "Volatility spillovers and heavy tails: a large t-Vector AutoRegressive approach," Working Papers of Department of Decision Sciences and Information Management, Leuven 590528, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
  35. Iason Kynigakis & Ekaterini Panopoulou, 2022. "Does model complexity add value to asset allocation? Evidence from machine learning forecasting models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 603-639, April.
  36. Gribisch, Bastian & Hartkopf, Jan Patrick, 2023. "Modeling realized covariance measures with heterogeneous liquidity: A generalized matrix-variate Wishart state-space model," Journal of Econometrics, Elsevier, vol. 235(1), pages 43-64.
  37. Vasyl Golosnoy & Benno Hildebrandt & Steffen Köhler, 2019. "Modeling and Forecasting Realized Portfolio Diversification Benefits," JRFM, MDPI, vol. 12(3), pages 1-16, July.
  38. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2021. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(3), pages 309-352, September.
  39. Jiawen Luo & Qun Zhang, 2024. "Air pollution, weather factors, and realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(2), pages 151-217, February.
  40. Gribisch, Bastian & Hartkopf, Jan Patrick & Liesenfeld, Roman, 2020. "Factor state–space models for high-dimensional realized covariance matrices of asset returns," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 1-20.
  41. Jiawen Luo & Oguzhan Cepni & Riza Demirer & Rangan Gupta, 2022. "Forecasting Multivariate Volatilities with Exogenous Predictors: An Application to Industry Diversification Strategies," Working Papers 202258, University of Pretoria, Department of Economics.
  42. Tingting Lan & Liuguo Shao & Hua Zhang & Caijun Yuan, 2023. "The impact of pandemic on dynamic volatility spillover network of international stock markets," Empirical Economics, Springer, vol. 65(5), pages 2115-2144, November.
  43. Cipollini, Fabrizio & Gallo, Giampiero M. & Palandri, Alessandro, 2021. "A dynamic conditional approach to forecasting portfolio weights," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1111-1126.
  44. Barbaglia, Luca & Croux, Christophe & Wilms, Ines, 2020. "Volatility spillovers in commodity markets: A large t-vector autoregressive approach," Energy Economics, Elsevier, vol. 85(C).
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