Skip to main content

Fractionally Integrated Models for Volatility: A Review

  • Chapter

Abstract

The main motivation to use fractionally integrated I(d) models is that the propagation of shocks in these processes occurs at a slow hyperbolic rate of decay, as opposed to the exponential decay associated with the I(0) stationary and invertible ARMA class of processes, or the infinite persistence resulting from an I(1) process. In this regard, many empirical studies have showed the extreme degree of persistence of shocks to the conditional variance process. Therefore, fractionally integrated models allow for a proper modelling of the long-run dependencies in the modelling of the conditional variance.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Baillie, R. T., Bollerslev, T., and H. O. Mikkelsen (1996) “Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 74(1): 3–30.

    Article  Google Scholar 

  • Bollerslev, T. (1986) “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 31(3): 307–327.

    Article  Google Scholar 

  • Bollerslev, T., and H. O. Mikkelsen (1996) “Modeling and Pricing Long-Memory in Stock Market Volatility,” Journal of Econometrics, 73(1): 151–184.

    Article  Google Scholar 

  • Chung, C.F. (1999) “Estimating the Fractionally Integrated GARCH Model,” Working paper, National Taiwan University, Taiwan.

    Google Scholar 

  • Conrad, C. (2010) “Non-negativity conditions for the hyperbolic GARCH model,” Journal of Econometrics, 157(2): 441–457.

    Article  Google Scholar 

  • Davidson, J. (2004) “Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a new model,” Journal of Business and Economics Statistics, 22(1): 16–29

    Article  Google Scholar 

  • Ding, Z., Granger, C., and R. F. Engle (1993) “A Long Memory Property of Stock Market Returns and a New Model,” Journal of Empirical Finance, 1(1): 83–106.

    Article  Google Scholar 

  • Engle, R. F., and V. K. Ng (1993) “Measuring and Testing the Impact of News on Volatility,” Journal of Finance, 48(5): 1749–1778.

    Article  Google Scholar 

  • Engle, R. F., and T. Bollerslev (1986). “Modeling the Persistence of Conditional Variances,” Econometric Reviews 5(1): 1–50.

    Article  Google Scholar 

  • Fantazzini, D. (2009) “Market Risk Management for Emerging Markets: Evidence from Russian Stock Market,” in G. Gregoriou (Ed.), Emerging Markets: Performance, Analysis and Innovation, Chapman & Hall/CRC Finance Series: London.

    Google Scholar 

  • Fernández, C., and M. F. J. Steel (1998) “On Bayesian Modelling of Fat Tails and Skewness,” Journal of the American Statistical Association, 93(441): 359–371.

    Google Scholar 

  • G@RCH Help Manual, available at http://www.core.ucl.ac.be/~laurent/G@RCH/site/default.htm

  • Glosten, L. R., Jagannathan, R., and D. E. Runkle (1993) “On the Relation Between Expected Value and the Volatility of the Nominal Excess Return on Stocks,” Journal of Finance, 48(5): 1779–1801.

    Article  Google Scholar 

  • Hansen, B. E. (1994) “Autoregressive Conditional Density Estimation,” International Economic Review, 35(3): 705–730.

    Article  Google Scholar 

  • He, C., and T. Teräsvirta (1999) “Higher-order Dependence in the General Power ARCH Process and a Special Case,”. Working Paper Series in Economics and Finance, No. 315, Stockholm School of Economics.

    Google Scholar 

  • König, H., and W. Gaab (1982) The Advanced Theory of Statistics, vol. 2 of Inference and Relationships. Haffner: New York.

    Google Scholar 

  • Laurent, P. and S. Lambert (2001) “Modelling Financial Time Series Using GARCH-Type Models and a Skewed Student Density,” Working paper, Université de Liège.

    Google Scholar 

  • Nelson, D. B. (1991) “Conditional Heteroskedasticity in Asset Returns: a New Approach,” Econometrica, 59(2): 349–370.

    Article  Google Scholar 

  • Nyblom, J. (1989) “Testing for the Constancy of Parameters Over Time,” Journal of the American Statistical Association, 84(405): 223–230.

    Article  Google Scholar 

  • Palm, F. C., and P. J. G. Vlaar (1997) “Simple Diagnostics Procedures for Modelling Financial Time Series,” Allgemeines Statistisches Archiv, 81: 85–101.

    Google Scholar 

  • Rossi, E. (2010) “Univariate GARCH Models: A Survey,” Quantile, 8: 1–67.

    Google Scholar 

  • Schwert, W. (1990) “Stock volatility and the crash of ’87,” Review of financial Studies, 3(1): 77–102.

    Article  Google Scholar 

  • Taylor, S. J. (1986) Modelling Financial Time Series. Wiley: New York.

    Google Scholar 

  • Tse, Y. K. (1998) “The Conditional Heteroscedasticity of the Yen-Dollar Exchange Rate,” Journal of Applied Econometrics, 13(1): 49–55.

    Article  Google Scholar 

  • Tse, Y. K. and A. Tsui (2002). “A Multivariate GARCH Model with Time-Varying Correlations,” Journal of Business and Economic Statistics, 20(3): 351–362.

    Article  Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Copyright information

© 2011 Dean Fantazzini

About this chapter

Cite this chapter

Fantazzini, D. (2011). Fractionally Integrated Models for Volatility: A Review. In: Gregoriou, G.N., Pascalau, R. (eds) Nonlinear Financial Econometrics: Markov Switching Models, Persistence and Nonlinear Cointegration. Palgrave Macmillan, London. https://doi.org/10.1057/9780230295216_5

Download citation

Publish with us

Policies and ethics