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Non Linear Moving-Average Conditional Heteroskedasticity

Author

Listed:
  • Ventosa-Santaulària, Daniel
  • Mendoza V., Alfonso

Abstract

Ever since the appearance of the ARCH model (Engle 1982a), an impressive array of variance specifications belonging to the same class of models has emerged. Despite numerous succesful developments, several studies seem to show their performance is not always satisfactory see Boulier (1994). In this paper a new alternative to model conditional heteroskedastic variance is proposed: the Non-Linear Moving Average Conditional Heteroskedasticity: (NLMACH). While NLMACH properties are similar to those of the ARCH-class specifications this new proposal represents a convenient alternative to modeling conditional volatility through a non-linear moving average process. The NLMACH performance is investigated using a Monte Carlo experiment and modeling exchange rate returns. It is found that NLMACH outperforms GARCHs forecasts whereas the application to exchange rates provides mixed evidence.

Suggested Citation

  • Ventosa-Santaulària, Daniel & Mendoza V., Alfonso, 2005. "Non Linear Moving-Average Conditional Heteroskedasticity," MPRA Paper 58769, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:58769
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    References listed on IDEAS

    as
    1. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, January.
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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