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                                                         Seminar
             Department of Systems Engineering and Engineering Management
The Chinese University of Hong Kong

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Title	:	Volatility Smile and the Informational Content of Implied Volatility
Speaker	:	Dr. GuanJun Wang
		Whitman School of Management
		Syracuse University
Date	:	May 9th, 2007 (Wednesday)
Time	:	10:00 a.m. - 11:00 a.m.
Venue	:	Room 513
		William M.W. Mong Engineering Building
		(Engineering Building Complex Phase 2)
		CUHK

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Abstract:
　

Numerous explanations for the volatility smile/skew phenomenon and extensions of, or alternatives to, the Black-Scholes model, have been offered in the literature. The inconsistence between the alternative models and data makes it worthwhile to re-think the well-known Black-Scholes model. Though it has been conjectured that the presence of measurement error can be of substantial impact on volatility estimation, I first show rigorously the level of sensitivity of price error to the volatility estimation for the options at different strikes and maturities. My results show that the degree of the bias of the implied volatility obtained from the option market price is directly related to the option’s moneyness and maturity and such bias can be minimized using option with appropriate strike and maturity from a mathematical point of view. I conclude that observed volatility smile/skew or non-flat term structure phenomenon does not necessarily violate the log-normal return and constant volatility assumption required by the Black-Scholes model in the presence of price error: price error alone can produce volatility smile/skew phenomenon even if the Black-Scholes model is correct. The smile phenomenon makes it unclear which implied volatility provides the best measure of the market volatility expectation over the remaining life of the options. Due to its liquidity and less sensitivity to price error, at-the-money implied volatility is often considered as a good measure of future volatility. In the second part of this paper, I empirically test the predictive power of the implied volatility from options with highest vega in comparison to that of at-the-money implied volatility. My empirical results are consistent with my conjecture that implied volatility from option with highest vega is more likely an efficient and less biased forecast of future volatility and has more predictive power than at-the-money implied volatility; the advantage of using highest vega implied volatility as future volatility estimate increases as forecasting horizon increases.

Key Words: options, Black-Scholes Model, implied volatility, volatility smile/skew, term structure, inversion, predictive power

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Biography:
　

GuanJun Wang received her B.S. in mathematics from Ningbo University in 1989, M.S. in probability theory and statistics from Zhejiang University and Ph.D. in stochastic control from the Chinese Academy of Sciences in 1996. She was a postdoctoral fellow at University of Missouri in Kansas City and Washington University in St.Louis from late 1998 to early 2001. Currently she is a Ph.D candidate in Finance at Syracuse University where she will receive her degree in May 2007. Her current research interests focus on Mathematical Finance. In particular, valuation of derivatives, risk management and computational finance.

************************* ALL ARE WELCOME ************************

Host	:	Prof. Duan Li
Tel	:	(852) 2609-8316, 2609-8323
Email	:	dli@se.cuhk.edu.hk
Enquiries	:	Bolin Ding or Jeffrey Xu Yu
	:	Department of Systems Engineering and Engineering Management
		CUHK
Website	:	http://www.se.cuhk.edu.hk/~seg5810
Email	:	seg5810@se.cuhk.edu.hk

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