IDEAS home Printed from https://ideas.repec.org/a/ids/intjaf/v3y2012i3p193-206.html
   My bibliography  Save this article

Effect of increasing the forecast horizon on correlation between forecasted returns and actual returns: an empirical analysis

Author

Listed:
  • Vivek Raj Rastogi
  • Joydip Dhar

Abstract

This study investigates the influence of increasing the forecast horizon on correlation between the forecast returns and the actual returns. ARMA and EGARCH models are used in this paper to capture univariate asset returns. ARMA model is conditional mean model and EGARCH model is conditional variance model and both of them are used for forecasting, parameter estimation and simulation of time series. The persistence in the volatility of the time series is usually exemplified by a highly persistent fitted EGARCH model. Traditional stationary ARMA processes are often used to capture high degree of persistence in financial time series. In this paper, a hybrid of EGARCH and ARMA model is used to forecast stock returns based on their actual returns then we have analysed the correlation trend between actual and forecasted returns by varying the forecast horizon. Results shown in this paper are depicting that correlation between actual and forecast returns undergoes a decaying pattern on increasing forecast horizon. Stock prices of three companies CIPLA, GAIL and LIC have been taken on daily basis from April 1997 to April 2010 and prediction has been done for next 150 days.

Suggested Citation

  • Vivek Raj Rastogi & Joydip Dhar, 2012. "Effect of increasing the forecast horizon on correlation between forecasted returns and actual returns: an empirical analysis," International Journal of Accounting and Finance, Inderscience Enterprises Ltd, vol. 3(3), pages 193-206.
    • Handle: RePEc:ids:intjaf:v:3:y:2012:i:3:p:193-206
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=48498
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bowden, Nicholas & Payne, James E., 2008. "Short term forecasting of electricity prices for MISO hubs: Evidence from ARIMA-EGARCH models," Energy Economics, Elsevier, vol. 30(6), pages 3186-3197, November.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    4. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    5. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    6. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    Full references (including those not matched with items on IDEAS)

    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. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Henning Fischer & Ángela Blanco‐FERNÁndez & Peter Winker, 2016. "Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 113-146, March.
    3. Asai, Manabu & McAleer, Michael, 2015. "Leverage and feedback effects on multifactor Wishart stochastic volatility for option pricing," Journal of Econometrics, Elsevier, vol. 187(2), pages 436-446.
    4. Chia-Lin Chang & Tai-Lin Hsieh & Michael McAleer, 2016. "Connecting VIX and Stock Index ETF," Tinbergen Institute Discussion Papers 16-010/III, Tinbergen Institute, revised 23 Jan 2017.
    5. Chia-Lin Chang & Yiying Li & Michael McAleer, 2018. "Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice," Energies, MDPI, vol. 11(6), pages 1-19, June.
    6. Buccheri, Giuseppe & Corsi, Fulvio & Flandoli, Franco & Livieri, Giulia, 2021. "The continuous-time limit of score-driven volatility models," Journal of Econometrics, Elsevier, vol. 221(2), pages 655-675.
    7. Prosper Dovonon, 2013. "Conditionally Heteroskedastic Factor Models With Skewness And Leverage Effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(7), pages 1110-1137, November.
    8. Hunjra, Ahmed Imran & Azam, Muhammad & Niazi, Ghulam Shabbir Khan & Butt, Babar Zaheer & Rehman, Kashif-Ur- & Azam, Rauf i, 2010. "Risk and return relationship in stock market and commodity prices: a comprehensive study of Pakistani markets," MPRA Paper 40662, University Library of Munich, Germany.
    9. Sucarrat, Genaro & Grønneberg, Steffen & Escribano, Alvaro, 2016. "Estimation and inference in univariate and multivariate log-GARCH-X models when the conditional density is unknown," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 582-594.
    10. Esparcia, Carlos & Jareño, Francisco & Umar, Zaghum, 2022. "Revisiting the safe haven role of Gold across time and frequencies during the COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    11. E. Ramos-P'erez & P. J. Alonso-Gonz'alez & J. J. N'u~nez-Vel'azquez, 2020. "Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network," Papers 2006.16383, arXiv.org, revised Aug 2020.
    12. Cathy W. S. Chen & Richard H. Gerlach & Ann M. H. Lin, 2011. "Multi-regime nonlinear capital asset pricing models," Quantitative Finance, Taylor & Francis Journals, vol. 11(9), pages 1421-1438, April.
    13. Dang, Tam Hoang Nhat & Balli, Faruk & Balli, Hatice Ozer & Gabauer, David & Nguyen, Thi Thu Ha, 2024. "Sectoral uncertainty spillovers in emerging markets: A quantile time–frequency connectedness approach," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 121-139.
    14. Vladimir Rankovic & Mikica Drenovak & Branko Uroševic & Ranko Jelic, 2016. "Mean Univariate-GARCH VaR Portfolio Optimization: Actual Portfolio Approach," CESifo Working Paper Series 5731, CESifo.
    15. Harris, Richard D.F. & Mazibas, Murat, 2013. "Dynamic hedge fund portfolio construction: A semi-parametric approach," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 139-149.
    16. Michael McAleer, 2009. "The Ten Commandments For Optimizing Value‐At‐Risk And Daily Capital Charges," Journal of Economic Surveys, Wiley Blackwell, vol. 23(5), pages 831-849, December.
    17. Robert F. Engle & Emil N. Siriwardane, 2018. "Structural GARCH: The Volatility-Leverage Connection," The Review of Financial Studies, Society for Financial Studies, vol. 31(2), pages 449-492.
    18. Bernardi, Mauro & Catania, Leopoldo, 2018. "Portfolio optimisation under flexible dynamic dependence modelling," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 1-18.
    19. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871, January.
    20. Jochen Güntner & Benjamin Karner, 2020. "Hedging with commodity futures and the end of normal Backwardation," Economics working papers 2020-21, Department of Economics, Johannes Kepler University Linz, Austria.

    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:ids:intjaf:v:3:y:2012:i:3:p:193-206. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=231 .

    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.