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How does patent litigation influence dynamic risk for market competitors?

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Title: How does patent litigation influence dynamic risk for market competitors?
Authors: Wang, Yi-Hsien1, Lin, Wan-Rung1, Lin, Shu-Shian2, Hung, Jui-Cheng1
Source: Technological & Economic Development of Economy. Sep2017, Vol. 23 Issue 5, p780-793. 14p.
Subjects: Patent suits, Economic competition, Financial risk, Patent infringement, Assets (Accounting)
Abstract: Many recent studies have discussed the appropriateness of various patent measurement indicators, as well as the differences in the positioning of patented technologies, while there is little discussion on the risk transmission of enterprises when faced with infringement litigation. This study used the bivariate EGARCH (Exponential Generalized Autoregressive Conditional Heteroskedasticity) model with DCC (Dynamic Conditional Correlations) to investigate the dynamic risk transmission of patent litigation between market competitors in the smartphone industry. Empirical results revealed that when facing lawsuits from market challengers, the market leader faces fewer risks when handling patent infringement litigations. In addition, the risk reactions of competitors during patent wars may widely differ. Investors should consider the patent infringement litigations when measuring the dynamic risks of share prices, and determining the optimal configuration of asset portfolios in response. [ABSTRACT FROM AUTHOR]
Copyright of Technological & Economic Development of Economy is the property of Vilnius Gediminas Technical University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Technological+%26+Economic+Development+of+Economy%22">Technological & Economic Development of Economy</searchLink>. Sep2017, Vol. 23 Issue 5, p780-793. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Patent+suits%22">Patent suits</searchLink><br /><searchLink fieldCode="DE" term="%22Economic+competition%22">Economic competition</searchLink><br /><searchLink fieldCode="DE" term="%22Financial+risk%22">Financial risk</searchLink><br /><searchLink fieldCode="DE" term="%22Patent+infringement%22">Patent infringement</searchLink><br /><searchLink fieldCode="DE" term="%22Assets+%28Accounting%29%22">Assets (Accounting)</searchLink>
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  Data: Many recent studies have discussed the appropriateness of various patent measurement indicators, as well as the differences in the positioning of patented technologies, while there is little discussion on the risk transmission of enterprises when faced with infringement litigation. This study used the bivariate EGARCH (Exponential Generalized Autoregressive Conditional Heteroskedasticity) model with DCC (Dynamic Conditional Correlations) to investigate the dynamic risk transmission of patent litigation between market competitors in the smartphone industry. Empirical results revealed that when facing lawsuits from market challengers, the market leader faces fewer risks when handling patent infringement litigations. In addition, the risk reactions of competitors during patent wars may widely differ. Investors should consider the patent infringement litigations when measuring the dynamic risks of share prices, and determining the optimal configuration of asset portfolios in response. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Technological & Economic Development of Economy is the property of Vilnius Gediminas Technical University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Sep2017
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