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Giuseppe Storti

Personal Details

First Name:Giuseppe
Middle Name:
Last Name:Storti
Suffix:
RePEc Short-ID:pst454
[This author has chosen not to make the email address public]
https://docenti.unisa.it/005005/en/home

Affiliation

Dipartimento di Scienze Economiche e Statistiche (DISES)
Università degli Studi di Salerno

Fisciano, Italy
http://www.dises.unisa.it/
RePEc:edi:dssalit (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Chapters

Working papers

  1. Giuseppe Storti & Chao Wang, 2022. "A semi-parametric marginalized dynamic conditional correlation framework," Papers 2207.04595, arXiv.org, revised Jul 2024.
  2. Giuseppe Storti & Chao Wang, 2021. "Modelling uncertainty in financial tail risk: a forecast combination and weighted quantile approach," Papers 2104.04918, arXiv.org, revised Jul 2021.
  3. Giuseppe Storti & Chao Wang, 2020. "Nonparametric Expected Shortfall Forecasting Incorporating Weighted Quantiles," Papers 2005.04868, arXiv.org, revised Mar 2021.
  4. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," MPRA Paper 93802, University Library of Munich, Germany.
  5. Gerlach, Richard & Naimoli, Antonio & Storti, Giuseppe, 2018. "Time Varying Heteroskedastic Realized GARCH models for tracking measurement error bias in volatility forecasting," MPRA Paper 83893, University Library of Munich, Germany.
  6. PREMINGER Arie & STORTI Giuseppe, 2017. "Least squares estimation for GARCH (1,1) model with heavy tailed errors," LIDAM Discussion Papers CORE 2017015, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  7. BAUWENS, Luc & BRAIONE, Manuela & STORTI, Giuseppe, 2016. "A dynamic component model for forecasting high-dimensional realized covariance matrices," LIDAM Discussion Papers CORE 2016001, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  8. BAUWENS, Luc & BRAIONE, Manuela & STORTI, Giuseppe, 2016. "Multiplicative Conditional Correlation Models for Realized Covariance Matrices," LIDAM Discussion Papers CORE 2016041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  9. BAUWENS, Luc & BRAIONE, Manuela & STORTI, Giuseppe, 2014. "Forecasting comparison of long term component dynamic models for realized covariance matrices," LIDAM Discussion Papers CORE 2014053, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  10. BAUWENS, Luc & STORTI, Giuseppe, 2012. "Computationally efficient inference procedures for vast dimensional realized covariance models," LIDAM Discussion Papers CORE 2012028, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  11. BAUWENS, Luc & STORTI, Giuseppe & VIOLANTE, Francesco, 2012. "Dynamic conditional correlation models for realized covariance matrices," LIDAM Discussion Papers CORE 2012060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  12. Amendola, Alessandra & Storti, Giuseppe, 2009. "Combination of multivariate volatility forecasts," SFB 649 Discussion Papers 2009-007, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  13. Amendola, Alessandra & Storti, Giuseppe, 2009. "Combination of multivariate volatility forecasts," SFB 649 Discussion Papers 2009-007, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  14. BAUWENS, Luc & STORTI, Giuseppe, 2007. "A component GARCH model with time varying weights," LIDAM Discussion Papers CORE 2007019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  15. Alessandra Amendola & Giuseppe Storti, 2006. "The combination of volatility forecasts," Computing in Economics and Finance 2006 496, Society for Computational Economics.
  16. PREMINGER, Arie & STORTI, Giuseppe, 2006. "A GARCH (1,1) estimator with (almost) no moment conditions on the error term," LIDAM Discussion Papers CORE 2006068, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  17. Destefanis, Sergio & Storti, Giuseppe, 2005. "Evaluating Business Incentives Through DEA. An Analysis on Capitalia Firm Data," MPRA Paper 62336, University Library of Munich, Germany.
  18. Giuseppe Storti & Alessandra Amendola, 2000. "A Non Linear Time Series Approach To Modelling Asymmetry In Stock Market Indexes," Computing in Economics and Finance 2000 97, Society for Computational Economics.

Articles

  1. Naimoli, Antonio & Gerlach, Richard & Storti, Giuseppe, 2022. "Improving the accuracy of tail risk forecasting models by combining several realized volatility estimators," Economic Modelling, Elsevier, vol. 107(C).
  2. Storti, Giuseppe & Wang, Chao, 2022. "Nonparametric expected shortfall forecasting incorporating weighted quantiles," International Journal of Forecasting, Elsevier, vol. 38(1), pages 224-239.
  3. Antonio Naimoli & Giuseppe Storti, 2021. "Forecasting Volatility and Tail Risk in Electricity Markets," JRFM, MDPI, vol. 14(7), pages 1-17, June.
  4. 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.
  5. Alessandra Amendola & Vincenzo Candila & Luca Sensini & Giuseppe Storti, 2020. "Corporate Governance, Investment, Profitability and Insolvency Risk: Evidence from Italy," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 10(4), pages 1-10.
  6. Richard Gerlach & Antonio Naimoli & Giuseppe Storti, 2020. "Time-varying parameters realized GARCH models for tracking attenuation bias in volatility dynamics," Quantitative Finance, Taylor & Francis Journals, vol. 20(11), pages 1849-1878, November.
  7. Massimiliano Caporin & Giuseppe Storti, 2020. "Financial Time Series: Methods and Models," JRFM, MDPI, vol. 13(5), pages 1-3, April.
  8. Pietro Coretto & Michele La Rocca & Giuseppe Storti, 2020. "Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters," JRFM, MDPI, vol. 13(4), pages 1-23, March.
  9. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1332-1355.
  10. Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
  11. Arie Preminger & Giuseppe Storti, 2017. "Least‐squares estimation of GARCH(1,1) models with heavy‐tailed errors," Econometrics Journal, Royal Economic Society, vol. 20(2), pages 221-258, June.
  12. Luc Bauwens & Manuela Braione & Giuseppe Storti, 2016. "Forecasting Comparison of Long Term Component Dynamic Models for Realized Covariance Matrices," Annals of Economics and Statistics, GENES, issue 123-124, pages 103-134.
  13. Alessandra Amendola & Giuseppe Storti, 2015. "Model Uncertainty and Forecast Combination in High‐Dimensional Multivariate Volatility Prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 83-91, March.
  14. Bauwens Luc & Storti Giuseppe, 2009. "A Component GARCH Model with Time Varying Weights," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(2), pages 1-33, May.
  15. Amendola, Alessandra & Storti, Giuseppe, 2008. "A GMM procedure for combining volatility forecasts," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3047-3060, February.
  16. Storti, G., 2006. "Minimum distance estimation of GARCH(1,1) models," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1803-1821, December.
  17. Giuseppe Storti & Cosimo Vitale, 2003. "Likelihood inference in BL-GARCH models," Computational Statistics, Springer, vol. 18(3), pages 387-400, September.
  18. Giuseppe Storti & Cosimo Vitale, 2003. "BL-GARCH models and asymmetries in volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(1), pages 19-39, February.
  19. Alessandra Amendola & Giuseppe Storti, 2002. "A non-linear time series approach to modelling asymmetry in stock market indexes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(2), pages 201-216, June.
  20. Sergio Destefanis & Giuseppe Storti, 2002. "Measuring cross-country technological catch-up through variable-parameter FDH," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(1), pages 109-125, February.

Chapters

  1. Pietro Coretto & Michele La Rocca & Giuseppe Storti, 2021. "A GARCH-Type Model with Cross-Sectional Volatility Clusters," Springer Books, in: Marco Corazza & Manfred Gilli & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 169-174, Springer.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Giuseppe Storti & Chao Wang, 2020. "Nonparametric Expected Shortfall Forecasting Incorporating Weighted Quantiles," Papers 2005.04868, arXiv.org, revised Mar 2021.

    Cited by:

    1. Zaevski, Tsvetelin S. & Nedeltchev, Dragomir C., 2023. "From BASEL III to BASEL IV and beyond: Expected shortfall and expectile risk measures," International Review of Financial Analysis, Elsevier, vol. 87(C).
    2. Lazar, Emese & Pan, Jingqi & Wang, Shixuan, 2024. "On the estimation of Value-at-Risk and Expected Shortfall at extreme levels," Journal of Commodity Markets, Elsevier, vol. 34(C).
    3. Alessandra Amendola & Vincenzo Candila & Antonio Naimoli & Giuseppe Storti, 2024. "Adaptive combinations of tail-risk forecasts," Papers 2406.06235, arXiv.org.
    4. Giuseppe Storti & Chao Wang, 2021. "Modelling uncertainty in financial tail risk: a forecast combination and weighted quantile approach," Papers 2104.04918, arXiv.org, revised Jul 2021.
    5. Max van der Lecq & Gary van Vuuren, 2024. "Estimating Value at Risk and Expected Shortfall: A Kalman Filter Approach," International Journal of Economics and Financial Issues, Econjournals, vol. 14(1), pages 1-14, January.
    6. Giuseppe Storti & Chao Wang, 2023. "Modeling uncertainty in financial tail risk: A forecast combination and weighted quantile approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1648-1663, November.

  2. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," MPRA Paper 93802, University Library of Munich, Germany.

    Cited by:

    1. Clements, Adam & Hurn, Stan & Volkov, Vladimir, 2021. "A simple linear alternative to multiplicative error models with an application to trading volume," Working Papers 2021-06, University of Tasmania, Tasmanian School of Business and Economics.

  3. BAUWENS, Luc & BRAIONE, Manuela & STORTI, Giuseppe, 2016. "A dynamic component model for forecasting high-dimensional realized covariance matrices," LIDAM Discussion Papers CORE 2016001, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. 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.
    2. BRAIONE, Manuela, 2016. "A time-varying long run HEAVY model," LIDAM Discussion Papers CORE 2016002, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1332-1355.
    4. 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.
    5. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2020. "Multivariate leverage effects and realized semicovariance GARCH models," Journal of Econometrics, Elsevier, vol. 217(2), pages 411-430.
    6. 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.
    7. Javed, Farrukh & Loperfido, Nicola & Mazur, Stepan, 2020. "Edgeworth Expansions for Multivariate Random Sums," Working Papers 2020:9, Örebro University, School of Business.
    8. Golosnoy, Vasyl & Gribisch, Bastian, 2022. "Modeling and forecasting realized portfolio weights," Journal of Banking & Finance, Elsevier, vol. 138(C).
    9. Amendola, Alessandra & Candila, Vincenzo & Gallo, Giampiero M., 2021. "Choosing the frequency of volatility components within the Double Asymmetric GARCH–MIDAS–X model," Econometrics and Statistics, Elsevier, vol. 20(C), pages 12-28.
    10. 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.
    11. Diego Fresoli, 2022. "Bootstrap VAR forecasts: The effect of model uncertainties," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 279-293, March.
    12. 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.
    13. Conrad, Christian & Stuermer, Karin, 2017. "On the economic determinants of optimal stock-bond portfolios: international evidence," Working Papers 0636, University of Heidelberg, Department of Economics.
    14. Qifa Xu & Junqing Zuo & Cuixia Jiang & Yaoyao He, 2021. "A large constrained time‐varying portfolio selection model with DCC‐MIDAS: Evidence from Chinese stock market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3417-3435, July.
    15. Amendola, Alessandra & Candila, Vincenzo & Gallo, Giampiero M., 2019. "On the asymmetric impact of macro–variables on volatility," Economic Modelling, Elsevier, vol. 76(C), pages 135-152.
    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).

  4. BAUWENS, Luc & BRAIONE, Manuela & STORTI, Giuseppe, 2016. "Multiplicative Conditional Correlation Models for Realized Covariance Matrices," LIDAM Discussion Papers CORE 2016041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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).

  5. BAUWENS, Luc & BRAIONE, Manuela & STORTI, Giuseppe, 2014. "Forecasting comparison of long term component dynamic models for realized covariance matrices," LIDAM Discussion Papers CORE 2014053, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Bauwens, Luc & Xu, Yongdeng, 2023. "DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations," International Journal of Forecasting, Elsevier, vol. 39(2), pages 938-955.
    2. Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
    3. BRAIONE, Manuela, 2016. "A time-varying long run HEAVY model," LIDAM Discussion Papers CORE 2016002, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1332-1355.
    5. 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.
    6. Luc Bauwens & Edoardo Otranto, 2023. "Modeling Realized Covariance Matrices: A Class of Hadamard Exponential Models," Journal of Financial Econometrics, Oxford University Press, vol. 21(4), pages 1376-1401.
    7. Xin Jin & John M. Maheu, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," Working Paper series 34_14, Rimini Centre for Economic Analysis.
    8. 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.
    9. Conrad, Christian & Stuermer, Karin, 2017. "On the economic determinants of optimal stock-bond portfolios: international evidence," Working Papers 0636, University of Heidelberg, Department of Economics.
    10. BAUWENS Luc, & XU Yongdeng,, 2019. "DCC-HEAVY: A multivariate GARCH model based on realized variances and correlations," LIDAM Discussion Papers CORE 2019025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    11. 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.
    12. Marchese, Malvina & Kyriakou, Ioannis & Tamvakis, Michael & Di Iorio, Francesca, 2020. "Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models," Energy Economics, Elsevier, vol. 88(C).
    13. 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.
    14. 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).
    15. Harry Vander Elst & David Veredas, 2017. "Smoothing it Out: Empirical and Simulation Results for Disentangled Realized Covariances," Journal of Financial Econometrics, Oxford University Press, vol. 15(1), pages 106-138.

  6. BAUWENS, Luc & STORTI, Giuseppe, 2012. "Computationally efficient inference procedures for vast dimensional realized covariance models," LIDAM Discussion Papers CORE 2012028, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. BAUWENS, Luc & BRAIONE, Manuela & STORTI, Giuseppe, 2016. "Multiplicative Conditional Correlation Models for Realized Covariance Matrices," LIDAM Discussion Papers CORE 2016041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Barigozzi, Matteo & Brownlees, Christian & Gallo, Giampiero M. & Veredas, David, 2014. "Disentangling systematic and idiosyncratic dynamics in panels of volatility measures," Journal of Econometrics, Elsevier, vol. 182(2), pages 364-384.
    3. DUFAYS, Arnaud, 2012. "Infinite-state Markov-switching for dynamic volatility and correlation models," LIDAM Discussion Papers CORE 2012043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. CREMER, Helmuth & PESTIEAU, Pierre & PONTHIERE, Grégory, 2012. "The economics of long-term care: a survey," LIDAM Reprints CORE 2466, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

  7. BAUWENS, Luc & STORTI, Giuseppe & VIOLANTE, Francesco, 2012. "Dynamic conditional correlation models for realized covariance matrices," LIDAM Discussion Papers CORE 2012060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Bauwens, Luc & Xu, Yongdeng, 2023. "DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations," International Journal of Forecasting, Elsevier, vol. 39(2), pages 938-955.
    2. Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
    3. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2020. "Multivariate leverage effects and realized semicovariance GARCH models," Journal of Econometrics, Elsevier, vol. 217(2), pages 411-430.
    4. Weigand, Roland, 2014. "Matrix Box-Cox Models for Multivariate Realized Volatility," University of Regensburg Working Papers in Business, Economics and Management Information Systems 478, University of Regensburg, Department of Economics.
    5. Bastian Gribisch, 2018. "A latent dynamic factor approach to forecasting multivariate stock market volatility," Empirical Economics, Springer, vol. 55(2), pages 621-651, September.
    6. Luc Bauwens & Manuela Braione & Giuseppe Storti, 2016. "Forecasting Comparison of Long Term Component Dynamic Models for Realized Covariance Matrices," Annals of Economics and Statistics, GENES, issue 123-124, pages 103-134.
    7. Tobias Hartl & Roland Weigand, 2018. "Multivariate Fractional Components Analysis," Papers 1812.09149, arXiv.org, revised Jan 2019.
    8. Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, vol. 5(2), pages 1-31, June.
    9. Luc Bauwens & Edoardo Otranto, 2023. "Modeling Realized Covariance Matrices: A Class of Hadamard Exponential Models," Journal of Financial Econometrics, Oxford University Press, vol. 21(4), pages 1376-1401.
    10. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg & Orimar Sauri, 2017. "Positive semidefinite integrated covariance estimation, factorizations and asynchronicity," Post-Print hal-01505775, HAL.
    11. Ilya Archakov & Peter Reinhard Hansen, 2020. "A New Parametrization of Correlation Matrices," Papers 2012.02395, arXiv.org.
    12. 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.
    13. Roxana Halbleib & Valeri Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," CREATES Research Papers 2011-03, Department of Economics and Business Economics, Aarhus University.
    14. 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.
    15. BAUWENS Luc, & XU Yongdeng,, 2019. "DCC-HEAVY: A multivariate GARCH model based on realized variances and correlations," LIDAM Discussion Papers CORE 2019025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    16. BAUWENS, Luc & STORTI, Giuseppe, 2013. "Computationally efficient inference procedures for vast dimensional realized covariance models," LIDAM Reprints CORE 2469, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    17. 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.
    18. Roxana Halbleib & Valeri Voev, 2016. "Forecasting Covariance Matrices: A Mixed Approach," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 383-417.
    19. Harry Vander Elst & David Veredas, 2017. "Smoothing it Out: Empirical and Simulation Results for Disentangled Realized Covariances," Journal of Financial Econometrics, Oxford University Press, vol. 15(1), pages 106-138.

  8. Amendola, Alessandra & Storti, Giuseppe, 2009. "Combination of multivariate volatility forecasts," SFB 649 Discussion Papers 2009-007, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Roland Strausz, 2010. "The Political Economy of Regulatory Risk," CESifo Working Paper Series 2953, CESifo.
    2. Grajek, Michał & Röller, Lars-Hendrik, 2009. "Regulation and investment in network industries: Evidence from European telecoms," SFB 649 Discussion Papers 2009-039, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Grith, Maria & Härdle, Wolfgang Karl & Park, Juhyun, 2009. "Shape invariant modelling pricing kernels and risk aversion," SFB 649 Discussion Papers 2009-041, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Michael McAleer & Massimiliano Caporin, 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," KIER Working Papers 815, Kyoto University, Institute of Economic Research.
    5. Choroś, Barbara & Härdle, Wolfgang Karl & Okhrin, Ostap, 2009. "CDO and HAC," SFB 649 Discussion Papers 2009-038, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Caporin, M. & McAleer, M.J., 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Econometric Institute Research Papers EI 2011-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Moawia Alghalith & Christos Floros & Konstantinos Gkillas, 2020. "Estimating Stochastic Volatility under the Assumption of Stochastic Volatility of Volatility," Risks, MDPI, vol. 8(2), pages 1-15, April.
    8. A Clements & M Doolan, 2018. "Combining Multivariate Volatility Forecasts using Weighted Losses," NCER Working Paper Series 119, National Centre for Econometric Research.

  9. Amendola, Alessandra & Storti, Giuseppe, 2009. "Combination of multivariate volatility forecasts," SFB 649 Discussion Papers 2009-007, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Roland Strausz, 2010. "The Political Economy of Regulatory Risk," CESifo Working Paper Series 2953, CESifo.
    2. Grajek, Michał & Röller, Lars-Hendrik, 2009. "Regulation and investment in network industries: Evidence from European telecoms," SFB 649 Discussion Papers 2009-039, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Grith, Maria & Härdle, Wolfgang Karl & Park, Juhyun, 2009. "Shape invariant modelling pricing kernels and risk aversion," SFB 649 Discussion Papers 2009-041, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Michael McAleer & Massimiliano Caporin, 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," KIER Working Papers 815, Kyoto University, Institute of Economic Research.
    5. Choroś, Barbara & Härdle, Wolfgang Karl & Okhrin, Ostap, 2009. "CDO and HAC," SFB 649 Discussion Papers 2009-038, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Caporin, M. & McAleer, M.J., 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Econometric Institute Research Papers EI 2011-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Moawia Alghalith & Christos Floros & Konstantinos Gkillas, 2020. "Estimating Stochastic Volatility under the Assumption of Stochastic Volatility of Volatility," Risks, MDPI, vol. 8(2), pages 1-15, April.
    8. A Clements & M Doolan, 2018. "Combining Multivariate Volatility Forecasts using Weighted Losses," NCER Working Paper Series 119, National Centre for Econometric Research.

  10. BAUWENS, Luc & STORTI, Giuseppe, 2007. "A component GARCH model with time varying weights," LIDAM Discussion Papers CORE 2007019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    2. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2008. "Asymmetric multivariate normal mixture GARCH," CFS Working Paper Series 2008/07, Center for Financial Studies (CFS).
    3. Simon A. BRODA & Markus HAAS & Jochen KRAUSE & Marc S. PAOLELLA & Sven C. STEUDE, 2011. "Stable Mixture GARCH Models," Swiss Finance Institute Research Paper Series 11-39, Swiss Finance Institute.
    4. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou, 2014. "Macro-Finance Determinants of the Long-Run Stock-Bond Correlation: The DCC-MIDAS Specification," CREATES Research Papers 2014-13, Department of Economics and Business Economics, Aarhus University.
    5. Jamal Bouoiyour & Refk Selmi, 2014. "Commodity price uncertainty and manufactured exports in Morocco and Tunisia: Some insights from a novel GARCH model," Post-Print hal-01879687, HAL.
    6. Boudt, Kris & Daníelsson, Jón & Laurent, Sébastien, 2013. "Robust forecasting of dynamic conditional correlation GARCH models," International Journal of Forecasting, Elsevier, vol. 29(2), pages 244-257.
    7. Boudt, Kris & Croux, Christophe, 2010. "Robust M-estimation of multivariate GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2459-2469, November.
    8. Bouoiyour, Jamal & Selmi, Refk, 2013. "The controversial link between exchange rate volatility and exports: Evidence from Tunisian case," MPRA Paper 49133, University Library of Munich, Germany, revised Mar 2013.
    9. 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.
    10. Bouoiyour, Jamal & Selmi, Refk, 2013. "Nonlinearities and the nexus between inflation and inflation uncertainty in Egypt: New evidence from wavelets transform framework," MPRA Paper 52414, University Library of Munich, Germany.
    11. Jamal Bouoiyour & Refk Selmi, 2016. "Bitcoin: a beginning of a new phase?," Economics Bulletin, AccessEcon, vol. 36(3), pages 1430-1440.
    12. Henryk Gurgul & Roland Mestel & Robert Syrek, 2017. "MIDAS models in banking sector – systemic risk comparison," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 18(2), pages 165-181.
    13. Bouoiyour, Jamal & Miftah, Amal & Selmi, Refk, 2014. "Do Financial Flows raise or reduce Economic growth Volatility? Some Lessons from Moroccan case," MPRA Paper 57258, University Library of Munich, Germany.
    14. Alessandra Amendola & Vincenzo Candila & Antonio Scognamillo, 2017. "On the influence of US monetary policy on crude oil price volatility," Empirical Economics, Springer, vol. 52(1), pages 155-178, February.
    15. Mobarek, Asma & Muradoglu, Gulnur & Mollah, Sabur & Hou, Ai Jun, 2016. "Determinants of time varying co-movements among international stock markets during crisis and non-crisis periods," Journal of Financial Stability, Elsevier, vol. 24(C), pages 1-11.
    16. Bauwens, L. & Hafner, C. & Laurent, S., 2012. "Volatility Models," LIDAM Reprints ISBA 2012028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    17. Piotr Fiszeder & Marta Ma³ecka, 2022. "Forecasting volatility during the outbreak of Russian invasion of Ukraine: application to commodities, stock indices, currencies, and cryptocurrencies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(4), pages 939-967, December.
    18. BAUWENS, Luc & HAFNER, Christian M. & PIERRET, Diane, 2013. "Multivariate volatility modeling of electricity futures," LIDAM Reprints CORE 2526, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    19. Becker Ralf & Clements Adam E & Hurn Stan, 2011. "Semi-Parametric Forecasting of Realized Volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(3), pages 1-23, May.
    20. Bouoiyour, Jamal & Selmi, Refk, 2015. "Bitcoin Price: Is it really that New Round of Volatility can be on way?," MPRA Paper 65580, University Library of Munich, Germany.
    21. Pietro Coretto & Michele La Rocca & Giuseppe Storti, 2020. "Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters," JRFM, MDPI, vol. 13(4), pages 1-23, March.
    22. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It’s all about volatility (of volatility): evidence from a two-factor stochastic volatility model," CREATES Research Papers 2013-03, Department of Economics and Business Economics, Aarhus University.
    23. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    24. N. Alemohammad & S. Rezakhah & S. H. Alizadeh, 2020. "Markov switching asymmetric GARCH model: stability and forecasting," Statistical Papers, Springer, vol. 61(3), pages 1309-1333, June.
    25. Amendola, Alessandra & Candila, Vincenzo & Gallo, Giampiero M., 2019. "On the asymmetric impact of macro–variables on volatility," Economic Modelling, Elsevier, vol. 76(C), pages 135-152.

  11. PREMINGER, Arie & STORTI, Giuseppe, 2006. "A GARCH (1,1) estimator with (almost) no moment conditions on the error term," LIDAM Discussion Papers CORE 2006068, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. HAFNER, Christian & PREMINGER, Arie, 2006. "Asymptotic theory for a factor GARCH model," LIDAM Discussion Papers CORE 2006071, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

  12. Destefanis, Sergio & Storti, Giuseppe, 2005. "Evaluating Business Incentives Through DEA. An Analysis on Capitalia Firm Data," MPRA Paper 62336, University Library of Munich, Germany.

    Cited by:

    1. Fabrizio Erbetta & Carmelo Petraglia, 2008. "Drivers of regional efficiency differentials in Italy: technical inefficiency or allocative distortions?," CERIS Working Paper 200802, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.

  13. Giuseppe Storti & Alessandra Amendola, 2000. "A Non Linear Time Series Approach To Modelling Asymmetry In Stock Market Indexes," Computing in Economics and Finance 2000 97, Society for Computational Economics.

    Cited by:

    1. Mohamed Boutahar & Gilles Dufrénot & Anne Péguin-Feissolle, 2008. "A Simple Fractionally Integrated Model with a Time-varying Long Memory Parameter d t," Computational Economics, Springer;Society for Computational Economics, vol. 31(3), pages 225-241, April.
    2. Roy Cerqueti & Massimiliano Giacalone & Raffaele Mattera, 2020. "Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling," Papers 2004.11674, arXiv.org.
    3. Giuseppe Storti & Cosimo Vitale, 2003. "BL-GARCH models and asymmetries in volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(1), pages 19-39, February.
    4. Giuseppe Storti & Cosimo Vitale, 2003. "Likelihood inference in BL-GARCH models," Computational Statistics, Springer, vol. 18(3), pages 387-400, September.

Articles

  1. Naimoli, Antonio & Gerlach, Richard & Storti, Giuseppe, 2022. "Improving the accuracy of tail risk forecasting models by combining several realized volatility estimators," Economic Modelling, Elsevier, vol. 107(C).

    Cited by:

    1. Chen, Jilong & Xu, Liao & Xu, Hao, 2022. "The impact of COVID-19 on commodity options market: Evidence from China," Economic Modelling, Elsevier, vol. 116(C).
    2. Naimoli, Antonio, 2022. "The information content of sentiment indices for forecasting Value at Risk and Expected Shortfall in equity markets," MPRA Paper 112588, University Library of Munich, Germany.
    3. Alessandra Amendola & Vincenzo Candila & Antonio Naimoli & Giuseppe Storti, 2024. "Adaptive combinations of tail-risk forecasts," Papers 2406.06235, arXiv.org.
    4. Naimoli, Antonio, 2023. "The information content of sentiment indices in forecasting Value at Risk and Expected Shortfall: a Complete Realized Exponential GARCH-X approach," International Economics, Elsevier, vol. 176(C).
    5. Cui, Tianxiang & Ding, Shusheng & Jin, Huan & Zhang, Yongmin, 2023. "Portfolio constructions in cryptocurrency market: A CVaR-based deep reinforcement learning approach," Economic Modelling, Elsevier, vol. 119(C).

  2. Storti, Giuseppe & Wang, Chao, 2022. "Nonparametric expected shortfall forecasting incorporating weighted quantiles," International Journal of Forecasting, Elsevier, vol. 38(1), pages 224-239.
    See citations under working paper version above.
  3. Antonio Naimoli & Giuseppe Storti, 2021. "Forecasting Volatility and Tail Risk in Electricity Markets," JRFM, MDPI, vol. 14(7), pages 1-17, June.

    Cited by:

    1. Sergej Gričar & Štefan Bojnec, 2021. "Technical Analysis of Tourism Price Process in the Eurozone," JRFM, MDPI, vol. 14(11), pages 1-25, October.
    2. Panayotis G. Papaioannou & George P. Papaioannou & George Evangelidis & George Gavalakis, 2024. "Detecting Structural breakpoints in natural gas and electricity wholesale prices via Bayesian ensemble approach, in the era of energy prices turmoil of 2022 period: the cases of ten European markets," Papers 2410.07224, arXiv.org.
    3. Clift, Dean Holland & Stanley, Cameron & Hasan, Kazi N. & Rosengarten, Gary, 2023. "Assessment of advanced demand response value streams for water heaters in renewable-rich electricity markets," Energy, Elsevier, vol. 267(C).

  4. 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.

    Cited by:

    1. Roccazzella, Francesco & Gambetti, Paolo & Vrins, Frédéric, 2021. "Optimal and robust combination of forecasts via constrained optimization and shrinkage," LIDAM Reprints LFIN 2021014, Université catholique de Louvain, Louvain Finance (LFIN).
    2. Mila Andreani & Vincenzo Candila & Giacomo Morelli & Lea Petrella, 2021. "Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach," Risks, MDPI, vol. 9(8), pages 1-20, August.
    3. Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.
    4. Xin Jin & Jia Liu & Qiao Yang, 2021. "Does the Choice of Realized Covariance Measures Empirically Matter? A Bayesian Density Prediction Approach," Econometrics, MDPI, vol. 9(4), pages 1-22, December.
    5. Hengzhen Lu & Qiujin Gao & Ling Xiao & Gurjeet Dhesi, 2024. "Forecasting EUA futures volatility with geopolitical risk: evidence from GARCH-MIDAS models," Review of Managerial Science, Springer, vol. 18(7), pages 1917-1943, July.
    6. Robiyanto Robiyanto & Bayu Adi Nugroho & Andrian Dolfriandra Huruta & Budi Frensidy & Suyanto Suyanto, 2021. "Identifying the Role of Gold on Sustainable Investment in Indonesia: The DCC-GARCH Approach," Economies, MDPI, vol. 9(3), pages 1-14, August.
    7. Alessandra Amendola & Vincenzo Candila & Antonio Naimoli & Giuseppe Storti, 2024. "Adaptive combinations of tail-risk forecasts," Papers 2406.06235, arXiv.org.
    8. Moawia Alghalith & Christos Floros & Konstantinos Gkillas, 2020. "Estimating Stochastic Volatility under the Assumption of Stochastic Volatility of Volatility," Risks, MDPI, vol. 8(2), pages 1-15, April.
    9. Vincenzo Candila, 2021. "Multivariate Analysis of Cryptocurrencies," Econometrics, MDPI, vol. 9(3), pages 1-17, July.

  5. Alessandra Amendola & Vincenzo Candila & Luca Sensini & Giuseppe Storti, 2020. "Corporate Governance, Investment, Profitability and Insolvency Risk: Evidence from Italy," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 10(4), pages 1-10.

    Cited by:

    1. Luca Sensini & Maria Vazquez, 2023. "Effects of Working Capital Management on SME Profitability: Evidence from an Emerging Economy," International Journal of Business and Management, Canadian Center of Science and Education, vol. 16(4), pages 1-85, February.
    2. Enrique Diaz & Luca Sensini, 2020. "Entrepreneurial Orientation and Firm Performance: Evidence from Argentina," International Business Research, Canadian Center of Science and Education, vol. 13(8), pages 1-47, August.
    3. Yarong Chen & Luca Sensini & Maria Vazquez, 2021. "Determinants of Leverage in Emerging Markets: Empirical Evidence," International Journal of Economics and Financial Issues, Econjournals, vol. 11(2), pages 40-46.

  6. Richard Gerlach & Antonio Naimoli & Giuseppe Storti, 2020. "Time-varying parameters realized GARCH models for tracking attenuation bias in volatility dynamics," Quantitative Finance, Taylor & Francis Journals, vol. 20(11), pages 1849-1878, November.

    Cited by:

    1. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    2. Naimoli, Antonio, 2022. "The information content of sentiment indices for forecasting Value at Risk and Expected Shortfall in equity markets," MPRA Paper 112588, University Library of Munich, Germany.
    3. Batten, Jonathan A. & Mo, Di & Pourkhanali, Armin, 2024. "Can inflation predict energy price volatility?," Energy Economics, Elsevier, vol. 129(C).
    4. Naimoli, Antonio, 2023. "The information content of sentiment indices in forecasting Value at Risk and Expected Shortfall: a Complete Realized Exponential GARCH-X approach," International Economics, Elsevier, vol. 176(C).

  7. Pietro Coretto & Michele La Rocca & Giuseppe Storti, 2020. "Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters," JRFM, MDPI, vol. 13(4), pages 1-23, March.

    Cited by:

    1. Claudiu Vinte & Marcel Ausloos, 2022. "The Cross-Sectional Intrinsic Entropy. A Comprehensive Stock Market Volatility Estimator," Papers 2205.00104, arXiv.org.
    2. Massimiliano Caporin & Giuseppe Storti, 2020. "Financial Time Series: Methods and Models," JRFM, MDPI, vol. 13(5), pages 1-3, April.
    3. Pietro Coretto, 2022. "Estimation and computations for Gaussian mixtures with uniform noise under separation constraints," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 427-458, June.

  8. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1332-1355.
    See citations under working paper version above.
  9. Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
    See citations under working paper version above.
  10. Arie Preminger & Giuseppe Storti, 2017. "Least‐squares estimation of GARCH(1,1) models with heavy‐tailed errors," Econometrics Journal, Royal Economic Society, vol. 20(2), pages 221-258, June.

    Cited by:

    1. Stefan Bruder, 2018. "Inference for structural impulse responses in SVAR-GARCH models," ECON - Working Papers 281, Department of Economics - University of Zurich.
    2. Hang Liu & Kanchan Mukherjee, 2022. "R-estimators in GARCH models: asymptotics and applications," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 98-113.

  11. Luc Bauwens & Manuela Braione & Giuseppe Storti, 2016. "Forecasting Comparison of Long Term Component Dynamic Models for Realized Covariance Matrices," Annals of Economics and Statistics, GENES, issue 123-124, pages 103-134.
    See citations under working paper version above.
  12. Alessandra Amendola & Giuseppe Storti, 2015. "Model Uncertainty and Forecast Combination in High‐Dimensional Multivariate Volatility Prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 83-91, March.

    Cited by:

    1. João F. Caldeira & Guilherme V. Moura & Francisco J. Nogales & André A. P. Santos, 2017. "Combining Multivariate Volatility Forecasts: An Economic-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 15(2), pages 247-285.
    2. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2019. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Working Paper 2019/2, Norges Bank.
    3. Ma, Feng & Wahab, M.I.M. & Zhang, Yaojie, 2019. "Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 132-146.
    4. Yaojie Zhang & Yu Wei & Li Liu, 2019. "Improving forecasting performance of realized covariance with extensions of HAR-RCOV model: statistical significance and economic value," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1425-1438, September.
    5. 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.
    6. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    7. Wei Kuang, 2021. "Conditional covariance matrix forecast using the hybrid exponentially weighted moving average approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1398-1419, December.
    8. Ma, Feng & Li, Yu & Liu, Li & Zhang, Yaojie, 2018. "Are low-frequency data really uninformative? A forecasting combination perspective," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 92-108.
    9. Alessandra Amendola & Vincenzo Candila & Antonio Naimoli & Giuseppe Storti, 2024. "Adaptive combinations of tail-risk forecasts," Papers 2406.06235, arXiv.org.
    10. A Clements & M Doolan, 2018. "Combining Multivariate Volatility Forecasts using Weighted Losses," NCER Working Paper Series 119, National Centre for Econometric Research.

  13. Bauwens Luc & Storti Giuseppe, 2009. "A Component GARCH Model with Time Varying Weights," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(2), pages 1-33, May.
    See citations under working paper version above.
  14. Amendola, Alessandra & Storti, Giuseppe, 2008. "A GMM procedure for combining volatility forecasts," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3047-3060, February.

    Cited by:

    1. Foschi, Paolo & Pascucci, Andrea, 2009. "Calibration of a path-dependent volatility model: Empirical tests," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2219-2235, April.
    2. Ewa Ratuszny, 2015. "Risk Modeling of Commodities using CAViaR Models, the Encompassing Method and the Combined Forecasts," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 129-156.
    3. Amendola, Alessandra & Storti, Giuseppe, 2009. "Combination of multivariate volatility forecasts," SFB 649 Discussion Papers 2009-007, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. 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.
    5. Vincenzo Candila & Oguzhan Cepni & Giampiero M. Gallo & Rangan Gupta, 2024. "Influence of Local and Global Economic Policy Uncertainty on the Volatility of US State-Level Equity Returns: Evidence from a GARCH-MIDAS Approach with Shrinkage and Cluster Analysis," Working Papers 202437, University of Pretoria, Department of Economics.
    6. Degiannakis, Stavros, 2018. "Multiple Days Ahead Realized Volatility Forecasting: Single, Combined and Average Forecasts," MPRA Paper 96272, University Library of Munich, Germany.
    7. Borovkova, Svetlana & Permana, Ferry J., 2009. "Implied volatility in oil markets," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2022-2039, April.
    8. Alessandra Amendola & Vincenzo Candila & Antonio Naimoli & Giuseppe Storti, 2024. "Adaptive combinations of tail-risk forecasts," Papers 2406.06235, arXiv.org.

  15. Storti, G., 2006. "Minimum distance estimation of GARCH(1,1) models," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1803-1821, December.

    Cited by:

    1. Hafner, C. & Preminger, A., 2010. "Deciding between GARCH and Stochastic Volatility via Strong Decision Rules," LIDAM Reprints ISBA 2010032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Amendola, Alessandra & Storti, Giuseppe, 2009. "Combination of multivariate volatility forecasts," SFB 649 Discussion Papers 2009-007, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Sangyeol Lee & Junmo Song, 2009. "Minimum density power divergence estimator for GARCH models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(2), pages 316-341, August.
    4. PREMINGER, Arie & STORTI, Giuseppe, 2006. "A GARCH (1,1) estimator with (almost) no moment conditions on the error term," LIDAM Discussion Papers CORE 2006068, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Kang, Jiwon & Lee, Sangyeol, 2014. "Minimum density power divergence estimator for Poisson autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 44-56.
    6. HAFNER, Christian & PREMINGER, Arie, 2006. "Asymptotic theory for a factor GARCH model," LIDAM Discussion Papers CORE 2006071, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Takada, Teruko, 2009. "Simulated minimum Hellinger distance estimation of stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2390-2403, April.
    8. Amendola, Alessandra & Storti, Giuseppe, 2008. "A GMM procedure for combining volatility forecasts," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3047-3060, February.
    9. Oana GHERGHINESCU & Paul RINDERU, 2011. "Econometric Models for Analysing the Structural Funds Absorption at Regional Level - Case Study SW Region," Timisoara Journal of Economics, West University of Timisoara, Romania, Faculty of Economics and Business Administration, vol. 4(3(15)), pages 161-174.

  16. Giuseppe Storti & Cosimo Vitale, 2003. "Likelihood inference in BL-GARCH models," Computational Statistics, Springer, vol. 18(3), pages 387-400, September.

    Cited by:

    1. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
    2. Abdou Kâ Diongue & Dominique Guegan & Rodney C. Wolff, 2010. "BL-GARCH model with elliptical distributed innovations," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00368340, HAL.
    3. Abdou Kâ Diongue & Dominique Guegan & Rodney C. Wolff, 2008. "Exact Maximum Likelihood estimation for the BL-GARCH model under elliptical distributed innovations," Post-Print halshs-00270719, HAL.
    4. Giuseppe Storti & Cosimo Vitale, 2003. "BL-GARCH models and asymmetries in volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(1), pages 19-39, February.

  17. Giuseppe Storti & Cosimo Vitale, 2003. "BL-GARCH models and asymmetries in volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(1), pages 19-39, February.

    Cited by:

    1. Dominique Guegan & Jing Zang, 2009. "Pricing bivariate option under GARCH-GH model with dynamic copula: application for Chinese market," The European Journal of Finance, Taylor & Francis Journals, vol. 15(7-8), pages 777-795.
    2. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
    3. Abdou Kâ Diongue & Dominique Guegan & Rodney C. Wolff, 2010. "BL-GARCH model with elliptical distributed innovations," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00368340, HAL.
    4. Dominique Guegan & Jing Zhang, 2009. "Pricing bivariate option under GARCH-GH model with dynamic copula: application for Chinese market," PSE-Ecole d'économie de Paris (Postprint) halshs-00368336, HAL.
    5. Abdou Kâ Diongue & Dominique Guegan & Rodney C. Wolff, 2008. "Exact Maximum Likelihood estimation for the BL-GARCH model under elliptical distributed innovations," Post-Print halshs-00270719, HAL.
    6. Chuffart Thomas & Flachaire Emmanuel & Péguin-Feissolle Anne, 2018. "Testing for misspecification in the short-run component of GARCH-type models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-17, December.
    7. Haas, Markus, 2009. "Persistence in volatility, conditional kurtosis, and the Taylor property in absolute value GARCH processes," Statistics & Probability Letters, Elsevier, vol. 79(15), pages 1674-1683, August.
    8. Choi, M.S. & Park, J.A. & Hwang, S.Y., 2012. "Asymmetric GARCH processes featuring both threshold effect and bilinear structure," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 419-426.
    9. Dominique Guegan & Jing Zhang, 2009. "Pricing bivariate option under GARCH-GH model with dynamic copula: application for Chinese market," Post-Print halshs-00368336, HAL.
    10. Dominique Guegan & Jing Zhang, 2007. "Pricing bivariate option under GARCH-GH model with dynamic copula : application for Chinese market," Post-Print halshs-00188248, HAL.
    11. Tan, Shay-Kee & Chan, Jennifer So-Kuen & Ng, Kok-Haur, 2020. "On the speculative nature of cryptocurrencies: A study on Garman and Klass volatility measure," Finance Research Letters, Elsevier, vol. 32(C).
    12. Giuseppe Storti & Cosimo Vitale, 2003. "Likelihood inference in BL-GARCH models," Computational Statistics, Springer, vol. 18(3), pages 387-400, September.

  18. Alessandra Amendola & Giuseppe Storti, 2002. "A non-linear time series approach to modelling asymmetry in stock market indexes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(2), pages 201-216, June.
    See citations under working paper version above.
  19. Sergio Destefanis & Giuseppe Storti, 2002. "Measuring cross-country technological catch-up through variable-parameter FDH," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(1), pages 109-125, February.

    Cited by:

    1. Walter Briec & Kristiaan Kerstens, 2006. "Input, output and graph technical efficiency measures on non-convex FDH models with various scaling laws: An integrated approach based upon implicit enumeration algorithms," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(1), pages 135-166, June.
    2. Antonella Basso & Stefania Funari, 2017. "The role of fund size in the performance of mutual funds assessed with DEA models," The European Journal of Finance, Taylor & Francis Journals, vol. 23(6), pages 457-473, May.
    3. Kristof De Witte & Rui C. Marcques, 2008. "Big and beautiful? On non-parametrically measuring scale economies in non-convex technologies," Working Papers of Department of Economics, Leuven ces0822, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    4. J. Vakili & R. Sadighi Dizaji, 2021. "The closest strong efficient targets in the FDH technology: an enumeration method," Journal of Productivity Analysis, Springer, vol. 55(2), pages 91-105, April.
    5. Destefanis, Sergio & Storti, Giuseppe, 2005. "Evaluating Business Incentives Through DEA. An Analysis on Capitalia Firm Data," MPRA Paper 62336, University Library of Munich, Germany.
    6. Sergio Destefanis & Vania Sena, 2005. "Public capital and total factor productivity: New evidence from the Italian regions, 1970-98," Regional Studies, Taylor & Francis Journals, vol. 39(5), pages 603-617.

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 13 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-FOR: Forecasting (9) 2009-04-18 2015-04-11 2016-03-10 2018-02-19 2019-05-20 2019-06-24 2020-06-08 2021-04-19 2022-08-22. Author is listed
  2. NEP-ECM: Econometrics (8) 2007-08-14 2014-11-12 2015-04-11 2016-03-10 2017-02-12 2018-02-19 2019-05-20 2020-06-08. Author is listed
  3. NEP-ETS: Econometric Time Series (8) 2007-08-14 2009-04-18 2014-11-12 2016-03-10 2017-02-12 2018-01-15 2018-02-19 2019-05-20. Author is listed
  4. NEP-ORE: Operations Research (6) 2009-04-18 2015-04-11 2018-01-15 2018-02-19 2019-05-20 2019-06-24. Author is listed
  5. NEP-RMG: Risk Management (6) 2007-08-14 2018-02-19 2019-06-24 2020-06-08 2021-04-19 2022-08-22. Author is listed
  6. NEP-BEC: Business Economics (1) 2019-05-20
  7. NEP-MST: Market Microstructure (1) 2019-05-20

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