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This paper investigates the information content of implied volatilities inferred from individual stock options quoted over-the-counter (OTC). First, we examine whether the implied volatility has better explanatory power than historical volatility for forecasting future realized volatility of the underlying stock return. Next, we analyze the properties of volatility spreads, the difference between implied volatilities and realized volatilities. Using near-the-money options for 10 firms over the sample period from April 2005 to April 2010, we first demonstrate that the implied volatilities for most firms don’t have additional information beyond what are already contained in historical volatilities. However, the implied volatilities with some specific remaining maturities for two firms dominate historical volatilities in explaining the future realized volatilities. Second, we find that during the period before global financial crisis, the implied volatilities are systematically lower than the future realized volatilities whereas this reversal disappears after the year 2008. This finding suggests that there’s a possibility of the risk loving behavior of the investors in OTC individual stock options market during the pre-global crisis period. Finally, through the comparative analysis of the KOSPI200 index options quoted OTC over the same sample period, we conclude that the OTC individual stock options market has distinctive characteristics like the KRW/USD OTC currency options market.

The purpose of this paper is to examine the short‐term and long‐term stock price volatility changes around bonus and rights issue announcements, using historical volatility estimation and time varying volatility approach.

Changes in volatility around bonus and rights issues have been examined using the following methodologies. First, to capture historical volatility, change in standard deviation for 20 days and 100 days before and after announcement have been examined. Second, change in time varying volatility and unconditional volatility is examined using GARCH (1, 1) model.

The results indicate that the historical volatility has increased after bonus and rights issue announcement. The volatility persistence and unconditional variance have increased after the bonus and rights issue announcements. This evidence, extendable to any other type of issue announcement, is consistent with theories stating that volatility increases after the seasoned capital issue announcements.

This study analyses historical volatility, volatility persistence and unconditional volatility around bonus and rights issue announcements, which has not been observed in the previous literature. This study fills the gap in literature by empirically examining the change in short‐ and long‐term volatility before and after bonus and rights issue announcements. Moreover, measuring volatility using GARCH model overcomes the potential problem of heteroscedasticity associated with cross‐sectional data. The change in volatility persistence and unconditional volatility before and after the announcement are also examined. This study is useful for researchers and practitioners specialized in finance, international business and management, and professionals in the area of commercial policy development in emerging markets.

The purpose of this paper is to examine whether volatility implied from dollar-rupee options is an unbiased and efficient predictor of ex post volatility, and to determine which options market is a better predictor of future realized volatility and to ascertain whether the model-free measure of implied volatility outperforms the traditional measure derived from the Black–Scholes–Merton model.

The information content of exchange-traded implied volatility and that of quoted implied volatility for OTC options is compared with that of historical volatility and a GARCH(1, 1)-based volatility. Ordinary least squares regression is used to examine the unbiasedness and informational efficiency of implied volatility. Robustness of the results is tested by using two specifications of implied volatility and realized volatility and comparison across two markets.

Implied volatility from both OTC and exchange-traded options is found to contain significant information for predicting ex post volatility, but is neither unbiased nor informationally efficient. The implied volatility of at-the-money options derived using the Black–Scholes–Merton model is found to outperform the model-free implied volatility (MFIV) across both markets. MFIV from OTC options is found to be a better predictor of realized volatility than MFIV from exchange-traded options.

This study throws light on the predictive power of currency options in India and has strong practical implications for market practitioners. Efficient currency option markets can serve as effective vehicles both for hedging and speculation and can convey useful information to the regulators regarding the market participants’ expectations of future volatility.

This study is a comprehensive study of the informational efficiency of options on an emerging currency such as the Indian rupee. To the author’s knowledge, this is one of the first studies to compare the predictive ability of the exchange-traded and OTC markets and also to compare traditional model-dependent volatility with MFIV.

This study aims to investigate the variation in overvaluation proxies and volatility across industry sectors and time.

Using industry sector data from the S&P Capital IQ database, this study applies traditional cross-sectional regressions to investigate the relationship between overvaluation and volatility over the 2001–2020 time period.

This study finds that the most volatile industry sectors generally do not coincide with overvalued industry sectors in the cross-section, implying that there are limitations to price-multiple methods for forecasting future volatility. Rather, this study finds that historical volatility significantly increases the goodness-of-fit when modeling volatility in the cross section of industry sectors. The findings of this study imply that firms should increase disclosures and transparency about corporate practices to decrease downside risk that stems from bad news. In addition, the findings underline the consistency between market efficiency and high levels of volatility in periods of significant uncertainty.

This study proposes a novel approach to examining the cross section of volatility across time for industry sectors.

In this paper we compare three types of forecasts of the volatility of equity returns series. The first is an historical estimate based on a simple sample standard deviation. A second is an estimate found by implying the volatility using the Black‐ Scholes formula. Finally, the third is an estimate obtained by forecasting with an estimated generalized autoregressive conditional heteroscedasticity (GARCH) model.

The purpose of this paper is to present the Greek value at risk (VaR) legislation framework and to highlight some of its major deficiencies, using not only theoretical scenarios but also empirical evidence. Moreover, this paper does not only highlight the VaR legislation’s framework deficiencies but also suggests legal interventions for its revision and a new-alternative, flexible and simple-to-be-applied filtered estimation method which improves the VaR evaluations.

The Greek legislation framework suggests that for the daily VaR to be estimated, a minimum data set of the previous year (250 observations) at the 99 per cent confidence level should be considered. This approach may lead to inaccurate VaR estimations, for example, when after a long-term growth period, there is a sudden recession period, because the data input is not representative to the current financial environment. Taking into serious consideration that high volatility periods are linked to a financial crisis, it is assumed that volatility could be an indicator for the financial environment representation. The conventional historical VaR back-tested results suggest that the specific methodology should be revised, especially during the high volatility period. For the newly suggested filtered VaR, the data sample is divided into several regimes depending on the volatility range. The filtered VaR estimation process applies the conventional historical methodology but uses different historical data input depending on the current volatility. This new approach improves the VaR estimation by reducing the VaR daily violations.

The findings regarding the current legislation framework suggest that the financial analysts in Greece have a motivation to adopt a relative VaR approach for risk asset class portfolios (e.g. Greek domestic equity mutual funds), which enables them to bear increased risk without presenting it to the investors. For lower risk portfolios, the absolute VaR may be useful for increased risk bearing strategies. The stricter VaR approaches are preferred to be adopted because stricter VaR estimations are linked to a reduced number of violations. The filtered volatility approach improves the VaR estimations (fewer violations are relative to the conventional approach).

This methodology is designed to be applied for the VaR estimation, but it could be partly applied in other fields of the financial analysis study.

The suggested methodology could present efficient VaR estimation without using sophisticated procedures or expensive VaR systems. Therefore, it could be easily applied by the risk analysts. Moreover, the overview of the Greek legislation’s framework could be useful not only for the Greek regulators but also for the authorities in countries with a similar regulation.

The newly proposed methodology is so accurate and simple to apply that it could have far-reaching impact on practitioners. Finally, this is the first paper that examines the Greek VaR legislation framework in detail.

Restates the importance of asset volatility forecasts for option pricing and portfolio management and outlines previous research on forecasting models. Discusses the relative information content and predictive power of implied and historical volatility and the existence of overreaction in option markets. Analyses 1989‐1997 daily exchange rate data for six currencies to examine this. Presents the results, which suggest that implied volatility has more information than volatility based on past prices; and is better than GARCH‐based or historic volatility forecasts for horizons up to three months; but can be a biased predictor of future realized volatility. Finds limited evidence that long term volatilities in option prices overreact to short term volatilities.

Ex ante tests of the efficiency of the London options market explain alternative hedging strategies to fund managers who seek to comprehend the opportunities in the options markets and profit by potential market inefficiencies. Over and under valued options were used to form hedge portfolios, which were mostly positive indicating potential inefficiencies in LIFFE. Therefore options appear to incorporate the role of an investment strategy on their own and not only as a hedge against positions in the underlying stocks while the Black‐Scholes formula proved to be an easily computed and implemented way to make above normal, zero risk profits. This paper also confirms the ability of a weighted implied standard deviation to explain future volatility more accurately than historical volatility by use of regression analysis.

This chapter derives sentiment indicators (implied volatility and implied skewness) from the option pricing models of Corrado and Su (1996), Bakshi, Kapadia, and Madan (2003), and Zhang, Zhen, Sun, and Zhao (2017), and then integrates these sentiment indicators with artificial intelligence deep neural network (AIDNN) for developing the behavioral finance AIDNN (BFAIDNN) algorithms. We apply the BFAIDNN algorithms to daily derivatives data of Taiwan Futures and Options markets from 2015 to 2017. Our results demonstrate that the trading strategies established by the BFAIDNN algorithms can generate positive rewards.

The purpose of this paper is to study the variance risk premium in corn and soybean markets, where the variance risk premium is defined as the difference between the historical realized variance and the corresponding risk-neutral expected variance.

The authors compute variance risk premiums using historical derivatives data. The authors use regression analysis and time series econometrics methods, including EGARCH and the Kalman filter, to analyze variance risk premiums.

There are moderate commonalities in variance within the agricultural sector, but fairly weak commonalities between the agricultural and the equity sectors. Corn and soybean variance risk premia in dollar terms are time-varying and correlated with the risk-neutral expected variance. In contrast, agricultural commodity variance risk premia in log return terms are more likely to be constant and less correlated with the log risk-neutral expected variance. Variance and price (return) risk premia in agricultural markets are weakly correlated, and the correlation depends on the sign of the returns in the underlying commodity.

Commodity variance (i.e. volatility) risk cannot be hedged using futures markets. The results have practical implications for US crop insurance programs because the implied volatilities from the relevant options markets are used to estimate the price volatility factors used to generate premia for revenue insurance products such as “Revenue Protection” and “Revenue Protection with Harvest Price Exclusion.” The variance risk premia found implies that revenue insurance premia are overpriced.

The empirical results suggest that the implied volatilities in corn and soybean futures market overestimate true expected volatility by approximately 15 percent. This has implications for derivative products, such as revenue insurance, that use these implied volatilities to calculate fair premia.