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

William W. Chow, King Fai Fung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 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 languageEnglish
Pages (from-to)64-79
Number of pages16
JournalJournal of Empirical Finance
Volume15
Issue number1
DOIs
Publication statusPublished - 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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