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An algorithm for nonparametric GARCH modelling

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  • Buhlmann, Peter
  • McNeil, Alexander J.
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    File URL: http://www.sciencedirect.com/science/article/B6V8V-45FGYCN-3/2/3fc3043671fa1153b7c9bc77b8bb2c84
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 40 (2002)
    Issue (Month): 4 (October)
    Pages: 665-683

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    Handle: RePEc:eee:csdana:v:40:y:2002:i:4:p:665-683

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    Web page: http://www.elsevier.com/locate/csda

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    1. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    2. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
    3. L. YANG & Wolfgang HÄRDLE, 1996. "Nonparametric Autoregression with Multiplicative Volatility and Additive Mean," SFB 373 Discussion Papers 1996,62, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    4. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    5. Engle, Robert F & Gonzalez-Rivera, Gloria, 1991. "Semiparametric ARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(4), pages 345-59, October.
    6. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
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    Cited by:
    1. Véronique Delouille & Rainer Sachs, 2005. "Estimation of nonlinear autoregressive models using design-adapted wavelets," Annals of the Institute of Statistical Mathematics, Springer, vol. 57(2), pages 235-253, June.
    2. Borovkova, Svetlana & Permana, Ferry J., 2009. "Implied volatility in oil markets," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2022-2039, April.
    3. Cosma, Antonio & Galli, Fausto, 2014. "A non parametric ACD model," MPRA Paper 53990, University Library of Munich, Germany.
    4. repec:pdn:wpaper:69 is not listed on IDEAS
    5. Patrick Saart & Jiti Gao, 2012. "Semiparametric Methods in Nonlinear Time Series Analysis: A Selective Review," Monash Econometrics and Business Statistics Working Papers 21/12, Monash University, Department of Econometrics and Business Statistics.
    6. Hou, Aijun & Suardi, Sandy, 2012. "A nonparametric GARCH model of crude oil price return volatility," Energy Economics, Elsevier, vol. 34(2), pages 618-626.
    7. Yuanhua Feng & Lixin Sun, 2013. "A semi-APARCH approach for comparing long-term and short-term risk in Chinese financial market and in mature financial markets," Working Papers CIE 69, University of Paderborn, CIE Center for International Economics.
    8. Hou, Ai Jun & Suardi, Sandy, 2011. "Modelling and forecasting short-term interest rate volatility: A semiparametric approach," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 692-710, September.
    9. repec:clg:wpaper:2014-39 is not listed on IDEAS

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