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Asymmetry and Long Memory in Volatility Modelling

A wide variety of conditional and stochastic variance models has been used to estimate latent volatility (or risk). In this paper, we propose a new long memory asymmetric volatility model which captures more flexible asymmetric patterns as compared with several existing models. We extend the new specification to realized volatility by taking account of measurement errors, and use the Efficient Importance Sampling technique to estimate the model. As an empirical example, we apply the new model to the realized volatility of S&P500 to show that the new specification of asymmetry significantly improves the goodness of fit, and that the out-of-sample forecasts and Value-at-Risk (VaR) thresholds are satisfactory. Overall, the results of the out-of-sample forecasts show the adequacy of the new asymmetric and long memory volatility model for the period including the global financial crisis.

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File URL: http://eprints.ucm.es/13215/1/1129.pdf
File Function: revised augost 2011
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Paper provided by Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico in its series Documentos de Trabajo del ICAE with number 2011-29.

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Length: 29 pages
Date of creation: 2011
Date of revision:
Handle: RePEc:ucm:doicae:1129
Note: The authors are most grateful to a Co-Editor, Associate Editor and two referees for very helpful comments and suggestions, and Marcel Scharth for efficient research assistance. For financial support, the first author acknowledges the Japan Ministry of Education, Culture, Sports, Science and Technology, Japan Society for the Promotion of Science, and Australian Academy of Science, the second author is most grateful to the Australian Research Council, National Science Council, Taiwan, and Japan Society for the Promotion of Science, and the third author wishes to acknowledge CNPq, Brazil.
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