Department of Economics and Business Economics

Option Pricing using Realized Volatility

Research output: Working paperResearch

  • Lars Peter Stentoft, Denmark
  • School of Economics and Management
In the present paper we suggest to model Realized Volatility, an estimate of daily volatility based on
high frequency data, as an Inverse Gaussian distributed variable with time varying mean, and we examine
the joint properties of Realized Volatility and asset returns. We derive the appropriate dynamics to be
used for option pricing purposes in this framework, and we show that our model explains some of the
mispricings found when using traditional option pricing models based on interdaily data. We then show
explicitly that a Generalized Autoregressive Conditional Heteroskedastic model with Normal Inverse
Gaussian distributed innovations is the corresponding benchmark model when only daily data is used.
Finally, we perform an empirical analysis using stock options for three large American companies, and we
show that in all cases our model performs significantly better than the corresponding benchmark model
estimated on return data alone. Hence the paper provides evidence on the value of using high frequency
data for option pricing purposes.
Original languageEnglish
Place of publicationAarhus
PublisherInstitut for Økonomi, Aarhus Universitet
Number of pages38
Publication statusPublished - 2008

    Research areas

  • Option Pricing, Realized Volatility, Stochastic Volatility, GARCH

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ID: 10662236