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Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure

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  • Christina Amado
  • Timo Teräsvirta

    ()
    (School of Economics and Management, University of Aarhus, Denmark and CREATES)

Abstract

In this paper, we propose two parametric alternatives to the standard GARCH model. They allow the conditional variance to have a smooth time-varying structure of either ad- ditive or multiplicative type. The suggested parameterizations describe both nonlinearity and structural change in the conditional and unconditional variances where the transition between regimes over time is smooth. A modelling strategy for these new time-varying parameter GARCH models is developed. It relies on a sequence of Lagrange multiplier tests, and the adequacy of the estimated models is investigated by Lagrange multiplier type misspecification tests. Finite-sample properties of these procedures and tests are examined by simulation. An empirical application to daily stock returns and another one to daily exchange rate returns illustrate the functioning and properties of our modelling strategy in practice. The results show that the long memory type behaviour of the sample autocorrelation functions of the absolute returns can also be explained by deterministic changes in the unconditional variance.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2008-08.

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Length: 56
Date of creation: 28 Jan 2008
Date of revision:
Handle: RePEc:aah:create:2008-08

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Conditional heteroskedasticity; Structural change; Lagrange multiplier test; Misspecification test; Nonlinear time series; Time-varying parameter model;

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References

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  1. Richard T. Baillie & Claudio Morana, 2007. "Modeling Long Memory and Structural Breaks in Conditional Variances: an Adaptive FIGARCH Approach," ICER Working Papers - Applied Mathematics Series, ICER - International Centre for Economic Research 11-2007, ICER - International Centre for Economic Research.
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Citations

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Cited by:
  1. Rohan, Neelabh, 2013. "A time varying GARCH(p,q) model and related statistical inference," Statistics & Probability Letters, Elsevier, Elsevier, vol. 83(9), pages 1983-1990.
  2. Paulo Bastos & Natália P. Monteiro, 2011. "Managers and Wage Policies," Journal of Economics & Management Strategy, Wiley Blackwell, Wiley Blackwell, vol. 20(4), pages 957-984, December.
  3. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series, ICER - International Centre for Economic Research 36-2010, ICER - International Centre for Economic Research.
  4. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2014. "Disentangling Systematic and Idiosyncratic Dynamics in Panels of Volatility Measures," Econometrics Working Papers Archive, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" 2014_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
  5. Francesco Battaglia & Mattheos Protopapas, 2009. "Time-varying Multi-regime Models Fitting by Genetic Algorithms," Working Papers, COMISEF 009, COMISEF.
  6. Adnen Ben Nasr & Mohamed Boutahar & Abdelwahed Trabelsi, 2010. "Fractionally integrated time varying GARCH model," Statistical Methods and Applications, Springer, Springer, vol. 19(3), pages 399-430, August.
  7. Adnen Ben Nasr & Ahdi N. Ajmi & Rangan Gupta, 2013. "Modeling the Volatility of the Dow Jones Islamic Market World Index Using a Fractionally Integrated Time Varying GARCH (FITVGARCH) Model," Working Papers, University of Pretoria, Department of Economics 201357, University of Pretoria, Department of Economics.
  8. Frijns, Bart & Lehnert, Thorsten & Zwinkels, Remco C.J., 2011. "Modeling structural changes in the volatility process," Journal of Empirical Finance, Elsevier, Elsevier, vol. 18(3), pages 522-532, June.
  9. Cristina Amado & Timo Terasvirta, 2012. "Modelling Changes in the Unconditional Variance of Long Stock Return Series," NIPE Working Papers, NIPE - Universidade do Minho 02/2012, NIPE - Universidade do Minho.
  10. Błażej Mazur & Mateusz Pipień, 2012. "On the Empirical Importance of Periodicity in the Volatility of Financial Returns - Time Varying GARCH as a Second Order APC(2) Process," Central European Journal of Economic Modelling and Econometrics, CEJEME, CEJEME, vol. 4(2), pages 95-116, June.
  11. Fernando Alexandre & Miguel Portela & Carla Sá, 2008. "Admission conditions and graduates' employability," NIPE Working Papers, NIPE - Universidade do Minho 16/2008, NIPE - Universidade do Minho.
  12. Claudio Morana, 2013. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks: New Insights on the US OIS SPreads Term Structure," Working Papers, University of Milano-Bicocca, Department of Economics 233, University of Milano-Bicocca, Department of Economics, revised Feb 2013.
  13. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers, University of Milano-Bicocca, Department of Economics 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
  14. Chen, Cathy W.S. & Gerlach, Richard & Lin, Edward M.H., 2014. "Bayesian estimation of smoothly mixing time-varying parameter GARCH models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 76(C), pages 194-209.
  15. BAUWENS, Luc & DUFAYS, Arnaud & DE BACKER, Bruno, 2011. "Estimating and forecasting structural breaks in financial time series," CORE Discussion Papers, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) 2011055, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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