Modeling Autocallable Structured Products

Journal of Derivatives & Hedge Funds, Vol. 17, pp. 326-340, 2011

20 Pages Posted: 8 Jan 2012 Last revised: 1 Aug 2013

See all articles by Geng Deng

Geng Deng

Wells Fargo

Craig J. McCann

Securities Litigation and Consulting Group

Joshua Mallett

Securities and Exchange Commission (SEC)

Date Written: August 16, 2011

Abstract

Since first introduced in 2003, the number of autocallable structured products in the U.S. has increased exponentially. The autocall feature immediately converts the product if the reference asset's value rises above a pre-specified call price. Because an autocallable structured product matures immediately if it is called, the autocall feature reduces the product's duration and expected maturity.

In this paper, we present a flexible Partial Differential Equation (PDE) framework to model autocallable structured products. Our framework allows for products with either discrete or continuous autocall dates. We value the autocallable structured products with discrete autocall dates using the finite difference method, and the products with continuous autocall dates using a closed-form solution. In addition, we estimate the probabilities of an autocallable structured-product being called on each call date. We demonstrate our models by valuing a popular autocallable product and quantify the cost to the investor of adding this feature to a structured product.

Keywords: Structured products, autocallable

Suggested Citation

Deng, Geng and McCann, Craig J. and Mallett, Joshua, Modeling Autocallable Structured Products (August 16, 2011). Journal of Derivatives & Hedge Funds, Vol. 17, pp. 326-340, 2011, Available at SSRN: https://ssrn.com/abstract=1981308

Geng Deng (Contact Author)

Wells Fargo ( email )

1753 Pinnacle Dr
7th Floor
Mc Lean, VA Virginia 22102
United States

Craig J. McCann

Securities Litigation and Consulting Group ( email )

3998 Fair Ridge Drive, Suite 250
Fairfax, VA 22033
United States

Joshua Mallett

Securities and Exchange Commission (SEC) ( email )

450 Fifth Street, NW
Washington, DC 20549-1105
United States

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