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A Better Asymmetric Model of Changing Volatility in Stock and Exchange Rate Returns: Trend-GARCH

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  • Christian Bauer

Abstract

The impact of short run price trending on the conditional volatility is tested empirically. A new family of conditionally heteroscedastic models with a trend-dependent conditional variance equation: The Trend-GARCH model is described. Modern microeconomic theory often suggests the connection between the past behaviour of time series, the subsequent reaction of market individuals, and thereon changes in the future characteristics of the time series. Results reveal important properties of these models, which are consistent with stylized facts found in financial data sets. They can also be employed for model identification, estimation, and testing. The empirical analysis supports the existence of trend effects. The Trend-GARCH model proves to be superior to alternative models such as EGARCH, AGARCH, TGARCH OR GARCH-in-Mean in replicating the leverage effect in the conditional variance, in fitting the news impact curve and in fitting the volatility estimates from high frequency data. In addition, we show that the leverage effect is dependent on the current trend, i.e. it differentiates between bullish and bearish markets. Furthermore, trend effects can account for a significant part of the long memory property of asset price volatilities.

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File URL: http://www.tandfonline.com/doi/abs/10.1080/13518470600763752
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal The European Journal of Finance.

Volume (Year): 13 (2007)
Issue (Month): 1 ()
Pages: 65-87

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Handle: RePEc:taf:eurjfi:v:13:y:2007:i:1:p:65-87

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Related research

Keywords: GARCH; trend; volatility; news impact curve; leverage effect; persistence;

References

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  1. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  2. Menkhoff, L., 1998. "The noise trading approach -- questionnaire evidence from foreign exchange," Journal of International Money and Finance, Elsevier, vol. 17(3), pages 547-564, June.
  3. Domowitz, Ian & Hakkio, Craig S., 1985. "Conditional variance and the risk premium in the foreign exchange market," Journal of International Economics, Elsevier, vol. 19(1-2), pages 47-66, August.
  4. Lux, T. & M. Marchesi, . "Volatility Clustering in Financial Markets: A Micro-Simulation of Interacting Agents," Discussion Paper Serie B 437, University of Bonn, Germany, revised Jul 1998.
  5. Pierre Giot, 2005. "Market risk models for intraday data," The European Journal of Finance, Taylor & Francis Journals, vol. 11(4), pages 309-324.
  6. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
  7. Markku Lanne & Saikkonen Pentti, 2007. "Modeling Conditional Skewness in Stock Returns," The European Journal of Finance, Taylor & Francis Journals, vol. 13(8), pages 691-704.
  8. Bauer, Christian & Herz, Bernhard, 2005. "Technical trading, monetary policy, and exchange rate regimes," Global Finance Journal, Elsevier, vol. 15(3), pages 281-302, February.
  9. Lehar, Alfred & Scheicher, Martin & Schittenkopf, Christian, 2002. "GARCH vs. stochastic volatility: Option pricing and risk management," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 323-345, March.
  10. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
  11. repec:uba:hadfwe:techtrademonpol_2003-07 is not listed on IDEAS
  12. M. Angeles Carnero, 2004. "Persistence and Kurtosis in GARCH and Stochastic Volatility Models," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(2), pages 319-342.
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