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One-Factor-GARCH Models for German Stocks - Estimation and Forecasting -

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  • Thomas Kaiser

    (Eberhard-Karls-Universitaet Tuebingen, Germany)

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 Asymmetric 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

  • Thomas Kaiser, 1996. "One-Factor-GARCH Models for German Stocks - Estimation and Forecasting -," Econometrics 9612007, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpem:9612007
    Note: Type of Document - Postscript/tared/gzipped; prepared on HP- UX; to print on Postscript; pages: 34 , 19 ; figures: included (seperate files). Tuebinger Diskussionsbeitraege Nr. 87 Wirtschaftswissenschaftliche Fakultaet Eberhard-Karls-Universitaet Tuebingen
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    References listed on IDEAS

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    Cited by:

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    2. 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.
    3. Wolff, Christian & Lehnert, Thorsten, 2001. "Modelling Scale-Consistent VaR with the Truncated Lévy Flight," CEPR Discussion Papers 2711, C.E.P.R. Discussion Papers.
    4. 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.
    5. G.R. Pasha & Tahira Qasim & Muhammad Aslam, 2007. "Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 12(2), pages 115-149, Jul-Dec.
    6. 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.
    7. 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.
    8. Necula Ciprian & Radu Alina-Nicoleta, 2009. "Detecting Regime Switches In The Eur/Ron Exchange Rate Volatility," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 3(1), pages 610-615, May.
    9. 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.
    10. Masud Alam, 2021. "Time Varying Risk in U.S. Housing Sector and Real Estate Investment Trusts Equity Return," Papers 2107.10455, arXiv.org.
    11. 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.

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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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