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Stochastic Proportional Dividends


Hans Buehler


JP Morgan Chase, London; Technical University of Berlin - Institut fur Mathematik

Anissa Stephanie Dhouibi


JP Morgan Chase

Dimitri Sluys


JPMorgan Chase

December 1, 2010


Abstract:     
Motivated by recently increased interest in trading derivatives on dividends, we present a simple, yet efficient equity stock price model with discrete stochastic proportional dividends.

The model has a closed form for European option pricing and can therefore be calibrated efficiently to vanilla options on the equity. It can also be simulated efficiently with Monte-Carlo and has fast analytics to aid the pricing of derivatives on dividends.

While its efficiency makes the model very appealing, it has the twin drawbacks that dividends in this model can become negative, and that it does not price in any skew on either dividends or the stock price.

We present the model and also discuss various extensions to stochastic interest rates, local volatility and jumps.

(The 2012 revision corrects a minor mistake in the original paper)

Number of Pages in PDF File: 21

Keywords: Options on Dividends, Stochastic Dividends, Dividend Yield, Dividend-Linked Derivatives

JEL Classification: G12

working papers series


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Date posted: November 12, 2010 ; Last revised: May 6, 2012

Suggested Citation

Buehler, Hans, Dhouibi, Anissa Stephanie and Sluys, Dimitri, Stochastic Proportional Dividends (December 1, 2010). Available at SSRN: http://ssrn.com/abstract=1706758 or http://dx.doi.org/10.2139/ssrn.1706758

Contact Information

Hans Buehler (Contact Author)
JP Morgan Chase, London ( email )
EDG QR 2/F
2 Aldermanbury
London, EC2V 7RF
United Kingdom
HOME PAGE: http://www.jpmorgan.com
Technical University of Berlin - Institut fur Mathematik ( email )
Institut fur Mathematik, Sekr. MA 6-1
Strasse des 17. Juni 136
Berlin, 10623
Germany
HOME PAGE: http://www.math.tu-berlin.de/~buehler
Anissa Stephanie Dhouibi
JP Morgan Chase ( email )
London
United Kingdom
Dimitri Sluys
JPMorgan Chase ( email )
EDG QR 2/F
2 Aldermanbury
London, EC2V 7RF
United Kingdom
Feedback to SSRN (Beta)


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