Volatility of stock price as predicted by patent data: An MGARCH perspective

William W. Chow; King Fai Fung

doi:10.1016/j.jempfin.2006.10.003

Volatility of stock price as predicted by patent data: An MGARCH perspective

William W. Chow, King Fai Fung

Research output: Journal article publication › Journal article › Academic research › peer-review

6 Citations (Scopus)

Abstract

This paper proposes to model stock price volatility and variations in innovation effort using a Multivariate GARCH structure designed to extract information for risk prediction. The salient feature is that the model order, alongside other parameters, is endogenously determined by the estimation procedures. Using stock prices of U.S. computer firms, it is found that the model can pick up the correlation between the two variables and aid in producing accurate Value-at-Risk estimates.

Original language	English
Pages (from-to)	64-79
Number of pages	16
Journal	Journal of Empirical Finance
Volume	15
Issue number	1
DOIs	https://doi.org/10.1016/j.jempfin.2006.10.003
Publication status	Published - 1 Jan 2008

Keywords

Innovation, Patents
Multivariate GARCH
Reversible jump MCMC
Value-at-Risk

ASJC Scopus subject areas

Finance
Economics and Econometrics

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10.1016/j.jempfin.2006.10.003

Cite this

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KW - Value-at-Risk

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Volatility of stock price as predicted by patent data: An MGARCH perspective

Abstract

Keywords

ASJC Scopus subject areas

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