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Monte Carlo Pricing of Derivative Securities and Uncertainty in Volatility Estimation

Joplin, George A.
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Joplin, George A.
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Text, Honors papers, Mathematics and Computer Science, Department of, Economics, Department of, Student research
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Joplin_George_Honors_2011[1].pdf
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http://hdl.handle.net/10267/13670
Abstract
In the standard time-inhomogeneous di usion model, estimation of the volatility function is far more important for Monte Carlo pricing than estimation of the drift function (due to a standard application of Girsanov's Theorem). As such, we study the distribution of option prices under the uncertainty of volatility function estimation. First, we run Monte Carlo simulations to price a variety of options using a xed estimate of the volatility function. Then, we run Monte Carlo simulations to price a variety of options using a bootstrapped re-estimation of volatility function in each Monte Carlo trial. The di erences in the resulting distributions of option prices may have implications for thinking about the bid-ask spread on an option price, and can be compared to historical data to gain a more complete perspective on the acceptability of various American-style option prices. Description: George Joplin granted permission for the digitization of this paper. It was submitted by CD.
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George A. Joplin granted permission for his paper to be published in DLynx. He submitted at PDF copy of his paper.