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One-factor-Garch models for German stocks: Estimation and forecasting

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  • Kaiser, Thomas

Abstract

This paper presents theoretical models and their empirical results for the return and variance dynamics of German stocks. A factor structure is used in order to allow for a parsimonious modeling of the first two moments of returns. Dynamic factor models with GARCH dynamics (GARCH(1,1)-M, IGARCH(1,1)-M, Nonlinear Asymmetrie GARCH(1,1)-M and Glosten-Jagannathan-Runkle GARCH(1,1)-M) and three different distributions for the disturbances (Normal, Student's t and Generalized Error Distribution) are considered, Out-of-sample forecasts for the stock returns based upon these models are computed. These forecasts are compared with forecasts based on individual GARCH(1,1)-M models, static factor models, naive, random walk and exponential smoothing forecasts.

Suggested Citation

  • Kaiser, Thomas, 1996. "One-factor-Garch models for German stocks: Estimation and forecasting," Tübinger Diskussionsbeiträge 87, University of Tübingen, School of Business and Economics.
    • Handle: RePEc:zbw:tuedps:87
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    References listed on IDEAS

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    2. Imran Riaz MALIK* & Attaullah SHAH*, 2014. "Market Varying Conditional Risk-Return Relationship," Pakistan Journal of Applied Economics, Applied Economics Research Centre, vol. 24(2), pages 121-142.
    3. Boldanov, Rustam & Degiannakis, Stavros & Filis, George, 2016. "Time-varying correlation between oil and stock market volatilities: Evidence from oil-importing and oil-exporting countries," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 209-220.
    4. Masud Alam, 2024. "Volatility in U.S. Housing Sector and the REIT Equity Return," The Journal of Real Estate Finance and Economics, Springer, vol. 69(3), pages 505-544, October.
    5. Wolff, Christian & Lehnert, Thorsten, 2001. "Modelling Scale-Consistent VaR with the Truncated Lévy Flight," CEPR Discussion Papers 2711, C.E.P.R. Discussion Papers.
    6. Hualing Lin & Qiubi Sun, 2020. "Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks," Energies, MDPI, vol. 13(7), pages 1-21, March.
    7. Palmitesta Paola & Provasi Corrado, 2004. "GARCH-type Models with Generalized Secant Hyperbolic Innovations," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-19, May.
    8. Masud Alam, 2021. "Time Varying Risk in U.S. Housing Sector and Real Estate Investment Trusts Equity Return," Papers 2107.10455, arXiv.org.
    9. Mittnik, Stefan & Paolella, Marc S. & Rachev, Svetlozar T., 2000. "Diagnosing and treating the fat tails in financial returns data," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 389-416, November.
    10. Elie BOURI, 2011. "An Attempt to Capture Leptokurtic of Returns and to Model Its Volatility: The Case of Beirut Stock Exchange," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 8, pages 259-271, December.
    11. Eskandar A. Tooma, 2003. "Modeling and Forecasting Egyptian Stock Market Volatility Before and After Price Limits," Working Papers 0310, Economic Research Forum, revised Apr 2003.

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    More about this item

    Keywords

    Dynamic Factors; GARCH; Asset Pricing; Forecasting;
    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
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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