The Information Content of Implied Volatilities and Model-Free Volatility Expectations


Book Description

The volatility information content of stock options for individual firms is measured using option prices for 149 U.S. firms during the period from January 1996 to December 1999. Volatility forecasts defined by historical stock returns, at-the-money (ATM) implied volatilities and model-free (MF) volatility expectations are compared for each firm. The recently developed model-free volatility expectation incorporates information across all strike prices, and it does not require the specification of an option pricing model.Our analysis of ARCH models shows that, for one-day-ahead estimation, historical estimates of conditional variances outperform both the ATM and the MF volatility estimates extracted from option prices for more than one-third of the firms. This result contrasts with the consensus about the informational efficiency of options written on stock indices; several recent studies find that option prices are more informative than daily stock returns when estimating and predicting index volatility. However, for the firms with the most actively traded options, we do find that the option forecasts are nearly always more informative than historical stock returns. When the prediction horizon extends until the expiry date of the options, our regression results show that the option forecasts are more informative than forecasts defined by historical returns for a substantial majority (86%) of the firms. Although the model-free (MF) volatility expectation is theoretically more appealing than alternative volatility estimates and has been demonstrated to be the most accurate predictor of realized volatility by Jiang and Tian (2005) for the Samp;P 500 index, the results for our firms show that the MF expectation only outperforms both the ATM implied volatility and the historical volatility for about one-third of the firms. The firms for which the MF expectation is best are not associated with a relatively high level of trading in away-from-the-money options.




Forecasting SMI Volatility


Book Description

Measures of volatility implied in option prices are widely believed to be the best available volatility forecasts. In this paper, we examine the information content and predictive power of implied standard deviations derived from EUREX options on the Swiss market index (SMI). Implied volatilities are computed from the Black and Scholes (1973) model as well as the Duan (1995) GARCH option pricing model, a more flexible method to price options. The statistical analysis shows that a combination of implied volatilities from the GARCH option pricing model and daily returns delivers the best results. We find no incremental information in using the model of Black and Scholes or intraday returns. In the medium term, two to three weeks, the implied volatility according to Duan is the single most informative source.




The Information Content of Canadian Implied Volatility Indexes


Book Description

This book compares the efficacy of Black-Scholes implied volatility with model-free implied volatility in providing volatility forecasts in the framework of Canadian S&P/TSX 60 stock index option. In-sample volatility forecasts show that both MVX and VIXC significantly improve the fit of a GJR-GARCH(1,1) model. However, VIXC dominates MVX for predicting future volatility. Out-of-sample volatility forecasts also indicate that VIXC outperforms MVX for the 1-, 5-, 10-, and 22-day forecasting horizons. we also investigate the predictive power between VIXC and alternative volatility forecasts derived from historical index prices.We find that for time horizons lesser than 10-trading days, VIXC provides more accurate forecasts. However, for longer time horizons, the historical volatilities, particularly the random walk, provide better forecasts.




The Model-Free Implied Volatility and Its Information Content


Book Description

Britten-Jones and Neuberger (2000) derived a model-free implied volatility under the diffusion assumption. In this article, we extend their model-free implied volatility to asset price processes with jumps and develop a simple method for implementing it using observed option prices. In addition, we perform a direct test of the informational efficiency of the option market using the model-free implied volatility. Our results from the Standard & Poor's 500 index (SPX) options suggest that the model-free implied volatility subsumes all information contained in the Black-Scholes (B-S) implied volatility and past realized volatility and is a more efficient forecast for future realized volatility.




Asset Price Dynamics, Volatility, and Prediction


Book Description

This book shows how current and recent market prices convey information about the probability distributions that govern future prices. Moving beyond purely theoretical models, Stephen Taylor applies methods supported by empirical research of equity and foreign exchange markets to show how daily and more frequent asset prices, and the prices of option contracts, can be used to construct and assess predictions about future prices, their volatility, and their probability distributions. Stephen Taylor provides a comprehensive introduction to the dynamic behavior of asset prices, relying on finance theory and statistical evidence. He uses stochastic processes to define mathematical models for price dynamics, but with less mathematics than in alternative texts. The key topics covered include random walk tests, trading rules, ARCH models, stochastic volatility models, high-frequency datasets, and the information that option prices imply about volatility and distributions. Asset Price Dynamics, Volatility, and Prediction is ideal for students of economics, finance, and mathematics who are studying financial econometrics, and will enable researchers to identify and apply appropriate models and methods. It will likewise be a valuable resource for quantitative analysts, fund managers, risk managers, and investors who seek realistic expectations about future asset prices and the risks to which they are exposed.




The Model-Free Implied Volatility and its Information Content


Book Description

Britten-Jones and Neuberger (2000) derived a model-free implied volatility under the diffusion assumption. In this article, we extend their model-free implied volatility to asset price processes with jumps and develop a simple method for implementing it using observed option prices. In addition, we perform a direct test of the informational efficiency of the option market using the model-free implied volatility. Our results from the Standard amp; Poor`s 500 index (SPX) options suggest that the model-free implied volatility subsumes all information contained in the Black-Scholes (B-S) implied volatility and past realized volatility and is a more efficient forecast for future realized volatility.




Predicting Volatility and the Information Content of Informed Traders in an Option Market


Book Description

We investigate the impact of information trading on predicting variation of implied volatility. First, we find that informed traders do trade in the index options market. The predicting biases of implied volatilities on the realized volatility are correlated with the information trading. Second, we find that delta market depth and bid-ask spread are correlated with the predicting variations in implied volatilities. Moreover, the difference between realized and implied volatility, bid-ask spread, and delta market depth are the determinants of price discovery in the option market. Third, the intraday patterns in realized volatility exhibit an inverse J-shape, which induces forecasting biases in implied volatilities. Finally, based on the performance of the volatility trading strategy, the result does not support efficient market hypothesis.




Analysing Intraday Implied Volatility for Pricing Currency Options


Book Description

This book focuses on the impact of high-frequency data in forecasting market volatility and options price. New technologies have created opportunities to obtain better, faster, and more efficient datasets to explore financial market phenomena at the most acceptable data levels. It provides reliable intraday data supporting financial investment decisions across different assets classes and instruments consisting of commodities, derivatives, equities, fixed income and foreign exchange. This book emphasises four key areas, (1) estimating intraday implied volatility using ultra-high frequency (5-minutes frequency) currency options to capture traders' trading behaviour, (2) computing realised volatility based on 5-minute frequency currency price to obtain speculators' speculation attitude, (3) examining the ability of implied volatility to subsume market information through forecasting realised volatility and (4) evaluating the predictive power of implied volatility for pricing currency options. This is a must-read for academics and professionals who want to improve their skills and outcomes in trading options.




Volatility Surface and Term Structure


Book Description

This book provides different financial models based on options to predict underlying asset price and design the risk hedging strategies. Authors of the book have made theoretical innovation to these models to enable the models to be applicable to real market. The book also introduces risk management and hedging strategies based on different criterions. These strategies provide practical guide for real option trading. This book studies the classical stochastic volatility and deterministic volatility models. For the former, the classical Heston model is integrated with volatility term structure. The correlation of Heston model is considered to be variable. For the latter, the local volatility model is improved from experience of financial practice. The improved local volatility surface is then used for price forecasting. VaR and CVaR are employed as standard criterions for risk management. The options trading strategies are also designed combining different types of options and they have been proven to be profitable in real market. This book is a combination of theory and practice. Users will find the applications of these financial models in real market to be effective and efficient.




Implied Volatilities as Forecasts of Future Volatility, Time-Varying Risk Premia, and Returns Variability


Book Description

The unbiasedness tests of implied volatility as a forecast of future realized volatility have found implied volatility to be a biased predictor. We explain this puzzle by recognizing that option prices contain a market risk premium not only on the asset itself, but also on its volatility. Hull and White (1987) show using a stochastic volatility model that a call option price can be represented as an expected value of the Black-Scholes formula evaluated at the average integrated volatility. If we allow volatility risk to be priced, this expectation should be taken under the risk-neutral probability measure, and can be decomposed into the expectation with respect to the physical measure and the risk-premium term. This term is just a linear function of the unobservable spot volatility. The decomposition explains the bias documented in the empirical literature and shows that the realized and historical volatility, which are used in the tests, are in fact the estimates of the unobserved quadratic variation and spot volatility of the stock-return generating process. Therefore, the use of these estimates generates the error-in-the-variables problem. We generalize the above results from a stochastic volatility model to a model with multiple volatility and jump factors. We provide an empirical illustration based on two US equity indices and three foreign currency rates. We find, that when we take into an account the risk-premium and use efficient methods to estimate volatility, the unbiasedness hypothesis can not be rejected, and the point estimate of the loading on the implied volatility in the traditional regression is equal to 1.