IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/64503.html
   My bibliography  Save this paper

Volatility forecasting using global stochastic financial trends extracted from non-synchronous data

Author

Listed:
  • Grigoryeva, Lyudmila
  • Ortega, Juan-Pablo
  • Peresetsky, Anatoly

Abstract

This paper introduces a method based on the use of various linear and nonlinear state space models that uses non-synchronous data to extract global stochastic financial trends (GST). These models are specifically constructed to take advantage of the intraday arrival of closing information coming from different international markets in order to improve the quality of volatility description and forecasting performances. A set of three major asynchronous international stock market indices is used in order to empirically show that this forecasting scheme is capable of significant performance improvements when compared with those obtained with standard models like the dynamic conditional correlation (DCC) family.

Suggested Citation

  • Grigoryeva, Lyudmila & Ortega, Juan-Pablo & Peresetsky, Anatoly, 2015. "Volatility forecasting using global stochastic financial trends extracted from non-synchronous data," MPRA Paper 64503, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:64503
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/64503/1/MPRA_paper_64503.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Anatoly A. Peresetsky & Ruslan I. Yakubov, 2017. "Autocorrelation in an unobservable global trend: does it help to forecast market returns?," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 152-169.
    2. Jeon, Bang Nam & Chiang, Thomas C., 1991. "A system of stock prices in world stock exchanges: Common stochastic trends for 1975-1990," Journal of Economics and Business, Elsevier, vol. 43(4), pages 329-338, November.
    3. repec:zbw:bofitp:2013_015 is not listed on IDEAS
    4. Lucey, Brian M. & Muckley, Cal, 2011. "Robust global stock market interdependencies," International Review of Financial Analysis, Elsevier, vol. 20(4), pages 215-224, August.
    5. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    6. Felices, Guillermo & Wieladek, Tomasz, 2012. "Are emerging market indicators of vulnerability to financial crises decoupling from global factors?," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 321-331.
    7. Cartea, Álvaro & Karyampas, Dimitrios, 2011. "Volatility and covariation of financial assets: A high-frequency analysis," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3319-3334.
    8. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    9. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the Best Volatility Models: The Model Confidence Set Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 839-861, December.
    10. Kasa, Kenneth, 1992. "Common stochastic trends in international stock markets," Journal of Monetary Economics, Elsevier, vol. 29(1), pages 95-124, February.
    11. Gian Piero Aielli, 2013. "Dynamic Conditional Correlation: On Properties and Estimation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 282-299, July.
    12. Bentes, Sónia R., 2015. "On the integration of financial markets: How strong is the evidence from five international stock markets?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 205-214.
    13. Neelabh Rohan & T. V. Ramanathan, 2013. "Nonparametric estimation of a time-varying GARCH model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(1), pages 33-52, March.
    14. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    15. Durdyev, Ruslan & Peresetsky, Anatoly, 2014. "Autocorrelation in the global stochastic trend," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 35(3), pages 39-58.
    16. Chung, Pin J. & Liu, Donald J., 1994. "Common stochastic trends in pacific rim stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 34(3), pages 241-259.
    17. Jesper Rangvid & Carsten Sørensen, 2002. "Convergence in the ERM and Declining Numbers of Common Stochastic Trends," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 1(2), pages 183-213, September.
    18. Bauwens, Luc & Grigoryeva, Lyudmila & Ortega, Juan-Pablo, 2016. "Estimation and empirical performance of non-scalar dynamic conditional correlation models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 17-36.
    19. Chang, Yoosoon & Isaac Miller, J. & Park, Joon Y., 2009. "Extracting a common stochastic trend: Theory with some applications," Journal of Econometrics, Elsevier, vol. 150(2), pages 231-247, June.
    20. Byung Yoon Bae & Dong Heon Kim, 2011. "Global and Regional Yield Curve Dynamics and Interactions: The Case of Some Asian Countries," International Economic Journal, Taylor & Francis Journals, vol. 25(4), pages 717-738, December.
    21. Choudhry, Taufiq & Lu, Lin & Peng, Ke, 2007. "Common stochastic trends among Far East stock prices: Effects of the Asian financial crisis," International Review of Financial Analysis, Elsevier, vol. 16(3), pages 242-261.
    22. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    23. Chrétien, Stéphane & Ortega, Juan-Pablo, 2014. "Multivariate GARCH estimation via a Bregman-proximal trust-region method," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 210-236.
    24. Phengpis, Chanwit & Apilado, Vince P., 2004. "Economic interdependence and common stochastic trends: A comparative analysis between EMU and non-EMU stock markets," International Review of Financial Analysis, Elsevier, vol. 13(3), pages 245-263.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2018. "On the determinants of bitcoin returns: A LASSO approach," Finance Research Letters, Elsevier, vol. 27(C), pages 235-240.
    2. Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.
    3. Григорьев Р.А., 2019. "Одновременные Эффекты Несинхронных Временных Рядов: Проблемы Var-Модели," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 55(2), pages 118-129, апрель.
    4. Conlon, Thomas & Cotter, John & Gençay, Ramazan, 2018. "Long-run wavelet-based correlation for financial time series," European Journal of Operational Research, Elsevier, vol. 271(2), pages 676-696.
    5. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2019. "The effects of markets, uncertainty and search intensity on bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anatoly A. Peresetsky & Ruslan I. Yakubov, 2017. "Autocorrelation in an unobservable global trend: does it help to forecast market returns?," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 152-169.
    2. Bank for International Settlements, 2008. "Integration of India's stock market with global and major regional markets," BIS Papers chapters, in: Bank for International Settlements (ed.), Regional financial integration in Asia: present and future, volume 42, pages 202-236, Bank for International Settlements.
    3. Bank for International Settlements & Hong Kong Institute for Monetary Research, 2008. "Regional financial integration in Asia: present and future," BIS Papers, Bank for International Settlements, number 42.
    4. Bauwens, Luc & Grigoryeva, Lyudmila & Ortega, Juan-Pablo, 2016. "Estimation and empirical performance of non-scalar dynamic conditional correlation models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 17-36.
    5. Chien, Mei-Se & Lee, Chien-Chiang & Hu, Te-Chung & Hu, Hui-Ting, 2015. "Dynamic Asian stock market convergence: Evidence from dynamic cointegration analysis among China and ASEAN-5," Economic Modelling, Elsevier, vol. 51(C), pages 84-98.
    6. Fiszeder, Piotr & Fałdziński, Marcin, 2019. "Improving forecasts with the co-range dynamic conditional correlation model," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
    7. 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.
    8. 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.
    9. Darolles, Serge & Francq, Christian & Laurent, Sébastien, 2018. "Asymptotics of Cholesky GARCH models and time-varying conditional betas," Journal of Econometrics, Elsevier, vol. 204(2), pages 223-247.
    10. 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.
    11. Durdyev, Ruslan & Peresetsky, Anatoly, 2014. "Autocorrelation in the global stochastic trend," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 35(3), pages 39-58.
    12. L. Bauwens & E. Otranto, 2020. "Modelling Realized Covariance Matrices: a Class of Hadamard Exponential Models," Working Paper CRENoS 202007, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    13. Gu, Huaying & Liu, Zhixue & Weng, Yingliang, 2017. "Time-varying correlations in global real estate markets: A multivariate GARCH with spatial effects approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 460-472.
    14. Masih, Rumi & Peters, Sanjay & De Mello, Lurion, 2011. "Oil price volatility and stock price fluctuations in an emerging market: Evidence from South Korea," Energy Economics, Elsevier, vol. 33(5), pages 975-986, September.
    15. Григорьев Р.А., 2019. "Одновременные Эффекты Несинхронных Временных Рядов: Проблемы Var-Модели," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 55(2), pages 118-129, апрель.
    16. Choudhry, Taufiq & Lu, Lin & Peng, Ke, 2007. "Common stochastic trends among Far East stock prices: Effects of the Asian financial crisis," International Review of Financial Analysis, Elsevier, vol. 16(3), pages 242-261.
    17. Adrian, Tobias & Franzoni, Francesco, 2009. "Learning about beta: Time-varying factor loadings, expected returns, and the conditional CAPM," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 537-556, September.
    18. Benjamin Poignard & Manabu Asai, 2023. "High‐dimensional sparse multivariate stochastic volatility models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(1), pages 4-22, January.
    19. Fortin, Alain-Philippe & Simonato, Jean-Guy & Dionne, Georges, 2023. "Forecasting expected shortfall: Should we use a multivariate model for stock market factors?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 314-331.
    20. Bauwens, Luc & Otranto, Edoardo, 2020. "Nonlinearities and regimes in conditional correlations with different dynamics," Journal of Econometrics, Elsevier, vol. 217(2), pages 496-522.

    More about this item

    Keywords

    multivariate volatility modeling and forecasting; global stochastic trend; extended Kalman filter; CAPM; dynamic conditional correlations (DCC); non-synchronous data;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:64503. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.