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Modelling the Asymmetric Volatility of Electronics Patents in the USA

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Author Info
Felix Chan (Department of Economics, University of Western Australia)
Dora Marinova (Institute for Sustainability and Technology Policy, Murdoch University)
Michael McAleer (Department of Economics, University of Western Australia)

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Abstract

Since the 1970s, electronics and associated electrical equipment (henceforth "electronics") has been one of the most dominant industries in the developed countries, with its geographical centre firmly rooted in the USA. The overall presence of electronics patents in the USA is considerable, with the share of electronics reaching 31% of all US patents in 1996 and total electronics patents reaching close to 170,000 in 1997. For the empirical analysis, the time-varying nature of volatility in the electronics patent share, namely the ratio of US electronics patents to total US patents, is examined using monthly data from January 1975 to December 1997. As negative and positive movements in the patent share may have different impacts on innovative activity, and hence on volatility, both symmetric and asymmetric models of volatility are estimated. The estimated models are the symmetric AR(1)-GARCH(1,1), the asymmetric AR(1)-GJR(1,1), and asymmetric AR(1)-EGARCH(1,1). Of these, the asymmetric AR(1)-GJR(1,1) model is found to be suitable for modelling the electronics patent share in the USA.

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Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-208.

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Length: 24 pages
Date of creation: Mar 2003
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Handle: RePEc:tky:fseres:2003cf208

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  2. McAleer, Michael & Chan, Felix & Marinova, Dora, 2007. "An econometric analysis of asymmetric volatility: Theory and application to patents," Journal of Econometrics, Elsevier, vol. 127(2), pages 259-284, August. [Downloadable!] (restricted)
  3. Griliches, Zvi, 1990. "Patent Statistics as Economic Indicators: A Survey," Journal of Economic Literature, American Economic Association, vol. 28(4), pages 1661-1707, December. [Downloadable!] (restricted)
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  4. Neil Shephard, 2005. "Stochastic Volatility," Economics Papers 2005-W17, Economics Group, Nuffield College, University of Oxford. [Downloadable!]
  5. Felix Chan & Michael McAleer, 2003. "Estimating smooth transition autoregressive models with GARCH errors in the presence of extreme observations and outliers," Applied Financial Economics, Taylor and Francis Journals, vol. 13(8), pages 581-592, January. [Downloadable!] (restricted)
  6. 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. [Downloadable!] (restricted)
  7. Ng, Hock Guan & McAleer, Michael, 2004. "Recursive modelling of symmetric and asymmetric volatility in the presence of extreme observations," International Journal of Forecasting, Elsevier, vol. 20(1), pages 115-129. [Downloadable!] (restricted)
  8. repec:fth:harver:1473 is not listed on IDEAS
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  12. Li, W K & Ling, Shiqing & McAleer, Michael, 2002. " Recent Theoretical Results for Time Series Models with GARCH Errors," Journal of Economic Surveys, Blackwell Publishing, vol. 16(3), pages 245-69, July. [Downloadable!] (restricted)
  13. 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. [Downloadable!] (restricted)
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