The Informational Content of Implied Volatility

Canina, Linda; Figlewski, Stephen

doi:10.1093/rfs/5.3.659

Cited by 460 publications

(286 citation statements)

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“…For example, Rubinstein (1985) has shown that the implied volatilities derived via BS as a function of the moneyness ratio (S/ X ) and time to expiration (T ) often exhibit a U shape, known as the volatility smile. This is why BS is usually referred to as being a misspecified model with an inherent source of bias (see also Latane and Jr., 1976;Bates, 1991;Canica and Figlewski, 1993;Bakshi et al, 2000;Andersen et al, 2002). Under the existence of this anomaly, any historical volatility measure is doomed to fail, while measures (like the implied ones) that mitigate this bias could perform better.…”

Section: The Black and Scholes Option Pricing Modelmentioning

confidence: 99%

Robust Artificial Neural Networks for Pricing of European Options

2006

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Section: The Black and Scholes Option Pricing Modelmentioning

confidence: 99%

Robust Artificial Neural Networks for Pricing of European Options

2006

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“…Beckers (1981) studies the predictive accuracy of implied standard deviation (ISD) for future price variability and finds that option implicit standard deviation is an ef ficient measure of future price variability. However, Canina and Figlewski (1993) study the S&P 100 Index options for the period March 15, 1983to March 28, 1987 and document that implied volatility (IV) computed using BlackScholes options pricing formula is inefficient, biased and inferior estimate of market's future volatility forecast, when com pared to historical volatility. Chen, Cuny and Haugen (1995) study the relationship between stock volatility, basis 2 and open interests in futures market using S&P 500 Index.…”

Section: Introductionmentioning

confidence: 99%

Volatility Informed Trading in the Options Market: Evidence from India

Pathak

2015

Business: Theory and Practice

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The purpose of this paper is to investigate the trading activity in options market based on information about expected future volatility in spot market. We employ Common Implied Volatility as a measure of expected volatility and options volume and changes in Open Interests as measures of options trading activity. We first test for simultaneous information flow in the two markets using multiple regression technique. Next, we test for information based or hedge based use of options using Trivariate Vector-auto Regression framework. We further consider the classes of options moneyness and the market trends in our analysis to examine if the trader’s preference of options changes with change in description of options intrinsic value and market environment. We use daily closing data of S&P CNX Nifty Index options traded on National Stock Exchange, India. We, for the most part, find negative and significant relationship in contemporaneous regression suggesting active trading by arbitrageurs. A feedback relationship is observed in vector auto regression analysis suggesting that options are traded in India for both information based trading and hedging purposes. We also observe the relationship to be varying when market trends and classes of options moneyness are considered. This indicates that traders are not indifferent in their choice of trading venue when market conditions and factors change. The results of this study are helpful for traders in managing the risk and return of their portfolio based on volatility forecast. This study is distinctive as it examines the scarcely researched area of volatility informed trading in an emerging market set up.

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“…The work of Christensen and Prabhala (1998) suggests that the amount of noise in option prices is large enough to introduce a significant attenuation bias to regression-based implied volatility forecast tests, such as those conducted by Canina and Figlewski (1993). Research by Hentschel (2003) demonstrates that noise in option prices can cause large biases in estimates of the implied volatility smile.…”

Section: Introductionmentioning

confidence: 99%