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Journal of Risk and Financial Management is published by MDPI from Volume 6 Issue 1 (2013). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Prof. Dr. Raymond A. K. Cox and Prof. Dr. Alan Wong.
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J. Risk Financial Manag. 2011, 4(1), 74-96; https://doi.org/10.3390/jrfm4010074

Use of Bayesian Estimates to determine the Volatility Parameter Input in the Black-Scholes and Binomial Option Pricing Models

1
The University of Auckland, Department of Statistics, Auckland, New Zealand
2
The University of Auckland, Department of Statistics, Auckland, New Zealand
3
The University of Auckland, Department of Accounting and Finance, Auckland, New Zealand
*
Author to whom correspondence should be addressed.
Published: 31 December 2011
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Abstract

The valuation of options and many other derivative instruments requires an estimation of exante or forward looking volatility. This paper adopts a Bayesian approach to estimate stock price volatility. We find evidence that overall Bayesian volatility estimates more closely approximate the implied volatility of stocks derived from traded call and put options prices compared to historical volatility estimates sourced from IVolatility.com (“IVolatility”). Our evidence suggests use of the Bayesian approach to estimate volatility can provide a more accurate measure of ex-ante stock price volatility and will be useful in the pricing of derivative securities where the implied stock price volatility cannot be observed. View Full-Text
Keywords: Option pricing; volatility estimate; bayesian statistics Option pricing; volatility estimate; bayesian statistics
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Ho, S.W.; Lee, A.; Marsden, A. Use of Bayesian Estimates to determine the Volatility Parameter Input in the Black-Scholes and Binomial Option Pricing Models. J. Risk Financial Manag. 2011, 4, 74-96.

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