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A time-varying long run HEAVY model. (2016). Braione, Manuela.
In: Statistics & Probability Letters.
RePEc:eee:stapro:v:119:y:2016:i:c:p:36-44.

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Cited: 2

Citations received by this document

Cites: 14

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Cocites: 19

Documents which have cited the same bibliography

Coauthors: 0

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Citations

Citations received by this document

  1. DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations. (2023). Bauwens, Luc ; Xu, Yongdeng.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:39:y:2023:i:2:p:938-955.

    Full description at Econpapers || Download paper

  2. DCC-HEAVY: A multivariate GARCH model based on realized variances and correlations. (2019). Yongdeng, XU ; Luc, BAUWENS.
    In: CORE Discussion Papers.
    RePEc:cor:louvco:2019025.

    Full description at Econpapers || Download paper

References

References cited by this document

  1. Bauwens, L. ; Braione, M. ; Storti, G. Forecasting comparison of long term component dynamic models for realized covariance matrices. 2016 Ann. Econ. Stat.. -

  2. Bauwens, L., Braione, M., Storti, G., A dynamic component model for forecasting high-dimensional realized covariance matrices, 2016. CORE discussion paper No. 1/2016.

  3. Diebold, F.X. ; Mariano, R.S. Comparing predictive accuracy. 1995 J. Bus. Econom. Statist.. 13 253-263

  4. Engle, R. Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. 2002 J. Bus. Econom. Statist.. 20 339-350

  5. Engle, R. ; Kroner, F. Multivariate simultaneous generalized ARCH. 1995 Econometric Theory. 11 122-150

  6. Ghysels, E., Valkanov, R.I., Serrano, A.R., Multi-period forecasts of volatility: Direct, iterated, and mixed-data approaches. In: EFA 2009 Bergen Meetings Paper, 2009.
    Paper not yet in RePEc: Add citation now

  7. Golosnoy, V. ; Gribisch, B. ; Liesenfeld, R. The conditional autoregressive wishart model for multivariate stock market volatility. 2012 J. Econometrics. 167 211-223

  8. Hansen, P.R. ; Lunde, A. ; Nason, J.M. The model confidence set. 2011 Econometrica. 79 453-497

  9. Marcellino, M. ; Stock, J.H. ; Watson, M.W. A comparison of direct and iterated multistep ar methods for forecasting macroeconomic time series. 2006 J. Econometrics. 135 499-526

  10. Noureldin, D. ; Shephard, N. ; Sheppard, K. Multivariate high-frequency-based volatility (heavy) models. 2012 J. Appl. Econometrics. 27 907-933

  11. Patton, A. ; Sheppard, K. Evaluating volatility and correlation forecasts. 2009 En : Andersen, T. ; Davis, R. ; Kreiss, J.-P. ; Mikosch, T. Handbook of Financial Time Series. Springer Verlag:
    Paper not yet in RePEc: Add citation now

  12. Patton, A.J. Volatility forecast comparison using imperfect volatility proxies. 2011 J. Econometrics. 160 246-256

  13. Pewsey, A., Ostrom, C., Time series analysis (regression techniques). 1991.
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  14. Proietti, T. Direct and iterated multistep ar methods for difference stationary processes. 2011 Int. J. Forecast.. 27 266-280

Cocites

Documents in RePEc which have cited the same bibliography

  1. DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations. (2023). Bauwens, Luc ; Xu, Yongdeng.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:39:y:2023:i:2:p:938-955.

    Full description at Econpapers || Download paper

  2. Challenging golden standards in EWMA smoothing parameter calibration based on realized covariance measures. (2023). Reh, Laura ; Hartkopf, Jan Patrick.
    In: Finance Research Letters.
    RePEc:eee:finlet:v:56:y:2023:i:c:s1544612323005019.

    Full description at Econpapers || Download paper

  3. Modeling realized covariance measures with heterogeneous liquidity: A generalized matrix-variate Wishart state-space model. (2023). Hartkopf, Jan Patrick ; Gribisch, Bastian.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:235:y:2023:i:1:p:43-64.

    Full description at Econpapers || Download paper

  4. A large constrained time?varying portfolio selection model with DCC?MIDAS: Evidence from Chinese stock market. (2021). He, Yaoyao ; Jiang, Cuixia ; Zuo, Junqing ; Xu, Qifa.
    In: International Journal of Finance & Economics.
    RePEc:wly:ijfiec:v:26:y:2021:i:3:p:3417-3435.

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  5. A DCC-type approach for realized covariance modeling with score-driven dynamics. (2021). Corsi, Fulvio ; Buccheri, Giuseppe ; Vassallo, Danilo.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:37:y:2021:i:2:p:569-586.

    Full description at Econpapers || Download paper

  6. Choosing the frequency of volatility components within the Double Asymmetric GARCH–MIDAS–X model. (2021). Gallo, Giampiero ; Amendola, Alessandra ; Candila, Vincenzo.
    In: Econometrics and Statistics.
    RePEc:eee:ecosta:v:20:y:2021:i:c:p:12-28.

    Full description at Econpapers || Download paper

  7. A Dynamic Component Model for Forecasting High-Dimensional Realized Covariances Matrices. (2020). Storti, Giuseppe ; Bauwens, Luc ; Braione, Manuela.
    In: Working Papers.
    RePEc:sep:wpaper:3_234.

    Full description at Econpapers || Download paper

  8. Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models. (2020). Di Iorio, Francesca ; Tamvakis, Michael ; Kyriakou, Ioannis ; Marchese, Malvina.
    In: Energy Economics.
    RePEc:eee:eneeco:v:88:y:2020:i:c:s0140988320300967.

    Full description at Econpapers || Download paper

  9. Modelling Realized Covariance Matrices: a Class of Hadamard Exponential Models. (2020). Otranto, Edoardo ; Bauwens, Luc.
    In: LIDAM Discussion Papers CORE.
    RePEc:cor:louvco:2020034.

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  10. Modelling Realized Covariance Matrices: a Class of Hadamard Exponential Models. (2020). Otranto, Edoardo ; Bauwens, L.
    In: Working Paper CRENoS.
    RePEc:cns:cnscwp:202007.

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  11. Heterogeneous component multiplicative error models for forecasting trading volumes. (2019). Storti, Giuseppe ; Naimoli, Antonio.
    In: MPRA Paper.
    RePEc:pra:mprapa:93802.

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  12. Heterogeneous component multiplicative error models for forecasting trading volumes. (2019). Storti, Giuseppe ; Naimoli, Antonio.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:35:y:2019:i:4:p:1332-1355.

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  13. On the asymmetric impact of macro–variables on volatility. (2019). Amendola, Alessandra ; Gallo, Giampiero M ; Candila, Vincenzo.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:76:y:2019:i:c:p:135-152.

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  14. DCC-HEAVY: A multivariate GARCH model based on realized variances and correlations. (2019). Yongdeng, XU ; Luc, BAUWENS.
    In: CORE Discussion Papers.
    RePEc:cor:louvco:2019025.

    Full description at Econpapers || Download paper

  15. Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices. (2018). Maheu, John ; Yang, Qiao ; Jin, Xin.
    In: Working Paper series.
    RePEc:rim:rimwps:18-02.

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  16. Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices. (2017). Maheu, John ; Yang, Qiao ; Jin, Xin.
    In: MPRA Paper.
    RePEc:pra:mprapa:81920.

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  17. A dynamic component model for forecasting high-dimensional realized covariance matrices. (2017). Bauwens, Luc ; Storti, Giuseppe ; Braione, Manuela.
    In: Econometrics and Statistics.
    RePEc:eee:ecosta:v:1:y:2017:i:c:p:40-61.

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  18. On the economic determinants of optimal stock-bond portfolios: international evidence. (2017). Conrad, Christian ; Stuermer, Karin .
    In: Working Papers.
    RePEc:awi:wpaper:0636.

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  19. A time-varying long run HEAVY model. (2016). Braione, Manuela.
    In: Statistics & Probability Letters.
    RePEc:eee:stapro:v:119:y:2016:i:c:p:36-44.

    Full description at Econpapers || Download paper

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Authors registered in RePEc who have wrote about the same topic

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