Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Fractionally integrated time varying GARCH model

  • Original Article
  • Published:
Statistical Methods & Applications Aims and scope Submit manuscript

Abstract

This paper introduces the new FITVGARCH model to describe both long memory and structural change behaviour in the volatility process by allowing for time varying dynamic structure in the conditional variance. The parameters of the conditional variance in the FIGARCH model are allowed to change smoothly over time. We derive an LM-type test for parameter constancy of the FIGARCH model against the alternative of time dependent parameters. Simulation analysis shows that both empirical size and power of the constancy test are quite good. An empirical application to the stock market volatility indicates that this new class of model seems to outperform the FIGARCH model in the description of the daily NASDAQ composite index returns.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Ajmi AN, Ben Nasr A, Boutahar M (2008) Seasonal nonlinear long memory model for the US inflation rates. Comput Econ 31: 243–254

    Article  MATH  Google Scholar 

  • Amado C, Teräsvirta T (2008) Modelling conditional and unconditional heteroskedasticity with smoothly time-varying structure. SSE/EFI Working Paper Series in Economics and Finance No. 691

  • Andersen TG, Bollerslev T (1997) Heterogeneous information arrivals and return volatility dynamics: uncovering the long-run in high frequency returns. J Finance 52: 975–1005

    Article  Google Scholar 

  • Andreou E, Ghysels E (2002) Detecting multiple breaks in financial market volatility dynamics. J Appl Econom 17: 579–600

    Article  Google Scholar 

  • Anderson HM, Nam K, Vahid F (1999) Asymmetric nonlinear smooth transition GARCH models. In: Rothman P (eds) Nonlinear time series analysis of economic and financial data. Kluwer, Boston, pp 191–207

    Google Scholar 

  • Baillie RT, Morana C (2009) Modeling long memory and structural breaks in conditional variances: an adaptive FIGARCH approach. J Econ Dyn Control 33: 1577–1592

    Article  MATH  MathSciNet  Google Scholar 

  • Baillie RT, Bollerslev T, Mikkelsen H (1996) Fractionally integrated generalized autoregressive conditional heteroskedasticity. J Econom 74: 3–30

    Article  MATH  MathSciNet  Google Scholar 

  • Baillie RT, Han YW, Kwon T (2002) Further long memory properties of inflationary shocks. South Econ J 68: 496–510

    Article  Google Scholar 

  • Beine M, Laurent S (2001) Structural changes and long memory in volatility: new evidence from daily exchange rates. In: Dunis C, Timmerman A, Moody J (eds) Developments in forecast combination and portfolio choice, Wiley series in quantitative analysis, chap. 6. Wiley, pp 145–157

  • Berkes I, Horvath L, Kokoszka P (2003) GARCH processes: structure and estimation. Bernoulli 9: 201–228

    Article  MATH  MathSciNet  Google Scholar 

  • Berndt E, Hall B, Hall R, Hausman J (1974) Modelling the persistence of conditional variances. Ann Econ Soc Meas 3: 653–665

    Google Scholar 

  • Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econom 31: 307–327

    Article  MATH  MathSciNet  Google Scholar 

  • Bollerslev T, Engle RF (1993) Common persistence in conditional variances. Econometrica 61: 167–186

    Article  MATH  MathSciNet  Google Scholar 

  • Bollerslev T, Mikkelsen HO (1996) Modelling and pricing long memory in stock market volatility. J Econom 73: 151–184

    Article  MATH  MathSciNet  Google Scholar 

  • Bollerslev T, Wooldridge JM (1992) Quasi maximum likelihood estimation and inference in dynamic models with time varying covariances. Econom Rev 11: 143–172

    Article  MATH  MathSciNet  Google Scholar 

  • Bos CS, Franses PH, Ooms M (1999) Long memory and level shifts: re-analyzing inflation rates. Empir Econ 24: 427–449

    Article  Google Scholar 

  • Breidt FJ, Hsu NJ (2002) A class of nearly long-memory time series models. Int J Forecast 18: 265–281

    Article  Google Scholar 

  • Breidt FJ, Crato N, de Lima P (1998) Modeling long memory stochastic volatility. J Econom 83: 325–348

    Article  MATH  Google Scholar 

  • Brown RL, Durbin J, Evans JM (1975) Techniques for testing the constancy of regression relationships over time with comments. J R Stat Soc B37: 149–192

    MathSciNet  Google Scholar 

  • Chung CF (1999) Estimating the fractionally integrated GARCH model. National Taiwan University, Taipei

    Google Scholar 

  • Cai J (1994) A Markov model of regime-switching ARCH. J Bus Econ Stat 12: 309–316

    Article  Google Scholar 

  • Conrad C, Haag BR (2006) Inequality constraints in the fractionally integrated GARCH model. J Financ Econom 4: 413–449

    Article  Google Scholar 

  • Davidson J (2004) Moment and memory properties of linear conditional heteroscedasticity models, and a new model. J Bus Econ Stat 22: 16–29

    Article  Google Scholar 

  • Diebold FX (1986) Comment on “Modeling the persistence of conditional variance” by Engle R, Bollerslev T. Econom Rev 5: 51–56

    Article  Google Scholar 

  • Ding Z, Granger CWJ, Engle RF (1993) A long memory property of stock market returns and a new model. J Empir Finance 1: 83–106

    Article  Google Scholar 

  • Dueker MJ (1997) Markov switching in GARCH processes and mean-reverting stock market volatility. J Bus Econ Stat 12: 309–316

    Google Scholar 

  • Efron B, Hinkley D (1978) Assessing the accuracy of the maximum likelihood estimator: observed versus expected Fisher information. Biometrika 65: 457–482

    Article  MATH  MathSciNet  Google Scholar 

  • Eitrheim Ø, Teräsvirta T (1996) Testing the adequacy of smooth transition autoregressive models. J Econom 74: 59–75

    Article  MATH  Google Scholar 

  • Engle RF (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation. Econometrica 50: 987–1008

    Article  MATH  MathSciNet  Google Scholar 

  • Engle RF, Bollerslev T (1986) Modeling the persistence of conditional variances. Econom Rev 5: 1–50

    Article  MATH  MathSciNet  Google Scholar 

  • Engle RF, Rangel JG (2008) The spline-GARCH model for low frequency volatility and its global macroeconomic causes. Rev Financ Stud 21: 1187–1222

    Article  Google Scholar 

  • Gonzalez-Rivera G (1998) Smooth transition GARCH models. Stud Nonlinear Dyn Econom 3: 161–178

    Google Scholar 

  • Granger CWJ (1981) Some properties of time series data and their use in econometric model specification. J Econom 16: 121–130

    Article  Google Scholar 

  • Granger CWJ, Hyung N (2004) Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns. J Empir Finance 11: 399–421

    Article  Google Scholar 

  • Granger CWJ, Joyeux R (1980) An introduction to long-memory time series and fractional differencing. J Time Ser Anal 1: 15–39

    Article  MATH  MathSciNet  Google Scholar 

  • Hagerud G (1997) A new non-linear GARCH model. EFI Economic Research Institute, Stockholm

    Google Scholar 

  • Hamilton JD, Susmel R (1994) Autoregressive conditional heteroscedasticity and changes in regime. J Econom 64: 307–333

    Article  MATH  Google Scholar 

  • Jensen ST, Rahbek A (2004) Asymptotic inference for nonstationary GARCH. Econom Theory 20: 1203–1226

    Article  MATH  MathSciNet  Google Scholar 

  • Karanasos M, Psaradakis Z, Sola M (2004) On the autocorrelation properties of long-memory GARCH processes. J Time Ser Anal 25: 265–281

    Article  MATH  MathSciNet  Google Scholar 

  • Lamoureux CG, Lastrapes WD (1990) Persistence in variance, structural change and the GARCH model. J Bus Econ Stat 8: 225–234

    Article  Google Scholar 

  • Lee SW, Hansen BE (1994) Asymptotic theory for the GARCH(1,1) quasi-maximum likelihood estimator. Econom Theory 10: 29–52

    Article  MATH  MathSciNet  Google Scholar 

  • Lin CFJ, Teräsvirta T (1994) Testing the constancy of regression parameters against continuous structural change. J Econom 62: 211–228

    Article  MATH  Google Scholar 

  • Lobato IN, Savin NE (1998) Real and spurious long memory properties of stock market data. J Bus Econ Stat 16: 261–268

    Article  MathSciNet  Google Scholar 

  • Lumsdaine RL (1996) Consistency and asymptotic normality of the quasi-maximum likelihood estimator in IGARCH(1,1) and covariance stationary GARCH(1,1) models. Econometrica 64: 575–596

    Article  MATH  MathSciNet  Google Scholar 

  • Lundbergh S, Teräsvirta T (2002) Evaluating GARCH models. J Econ 110: 417–435

    MATH  Google Scholar 

  • Luukkonen R, Saikkonen P, Teräsvirta T (1988) Testing linearity against smooth transition autoregressive models. Biometrika 75: 491–499

    Article  MATH  MathSciNet  Google Scholar 

  • Martens M, van Dijk D, de Pooter M (2004) Modeling and forecasting S&P 500 volatility: long memory, structural breaks and nonlinearity. Tinbergen Institute Discussion Paper 04-067/4

  • Mikosch T, Stărică C (2004) Changes of structure in financial time series and the GARCH model. Revstat Stat J 2(1): 41–73

    MATH  Google Scholar 

  • Morana C, Beltratti A (2004) Structural change and long-range dependence in volatility of exchange rates: either, neither or both?. J Empir Finance 11: 629–658

    Article  Google Scholar 

  • Nelson DB (1991) Conditional heteroskedasticity in asset returns: a new approach. Econometrica 59: 347–370

    Article  MATH  MathSciNet  Google Scholar 

  • Nelson DB, Cao CQ (1992) Inequality constraints in the univariate GARCH model. J Bus Econ Stat 10: 229–235

    Article  Google Scholar 

  • Perron P, Qu Z (2007) An analytical evaluation of the log-periodogram estimate in the presence of level shifts. Unpublished Manuscript, Department of Economics, Boston University

  • Perron P, Qu Z (2009) Long-memory and level shifts in the volatility of stock market return indices. J Bus Econ Stat (forthcoming)

  • Robinson PM (1991) Testingfor strong serial correlation and dynamic conditional heteroskedasticity in multiple regression. J Econom 47: 67–84

    Article  MATH  Google Scholar 

  • So MKP, Lam K, Li NK (1998) A stochastic volatility model with Markov switching. J Bus Econ Stat 16(2): 244–253

    Article  MathSciNet  Google Scholar 

  • Stărică C, Granger C (2005) Nonstationarities in stock returns. Rev Econ Stat 87: 503–522

    Article  Google Scholar 

  • Teyssière G (1997) Double long-memory financial time series. Paper presented at the ESEM, Toulouse

  • Tsai H, Chan KS (2008) A note on inequality constraints in the GARCH model. Econom Theory 24: 823–828

    Article  MATH  MathSciNet  Google Scholar 

  • Tse YK (1998) The conditional heteroskedasticity of the Yen-Dollar exchange rate. J Appl Econom 13: 49–55

    Article  Google Scholar 

  • van Dijk D, Franses PH, Paap R (2002) A nonlinear long memory model for US unemployment. J Econom 102: 135–165

    Google Scholar 

  • White H (1982) Maximum likelihood estimation of misspecified models. Econometrica 50: 1–25

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Adnen Ben Nasr.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ben Nasr, A., Boutahar, M. & Trabelsi, A. Fractionally integrated time varying GARCH model. Stat Methods Appl 19, 399–430 (2010). https://doi.org/10.1007/s10260-010-0131-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10260-010-0131-2

Keywords