Stock market volatility and the forecasting accuracy of implied volatility indices (original) (raw)
Related papers
2012
This paper examines the behavior of several implied volatility indexes in order to compare them with the volatility forecasts obtained from estimating a GARCH model. Though volatility has always been a prevailing subject of research it has become particularly relevant given the increasingly complexity and uncertainty of stock markets in these days. An important measure to assess the market expectations of the future volatility of the underlying asset is the implied volatility (IV) indexes. Generally, these indexes are calculated based on the prices of out-of-the money put and call options on the underlying asset. Sometimes called the "investor fear gauge", the IV indexes are a measure of the implied volatility of the underlying index. This study focuses on the implied and GARCH forecasted volatility of some emerging countries and some developed countries. More specifically, it compares the predictive power of the IV indexes with the ones provided by standard volatility models such as the ARCH/GARCH (Autoregressive Conditional Heteroskedasticity Model/ Generalized Autoregressive Conditional Heteroskedasticity Model) type models. Finally, a debate of the results is also provided.
The quality of market volatility forecasts implied by S&P 100 index option prices
Journal of Empirical Finance, 1998
This study examines the performance of the S & P 100 implied volatility as a forecast of future stock market volatility. The results indicate that the implied volatility is an upward biased forecast, but also that it contains relevant information regarding future volatility. The implied volatility dominates the historical volatility rate in terms of ex ante forecasting power, and its forecast error is orthogonal to parameters frequently linked to conditional volatility, including those employed in various ARCH specifications. These findings suggest that a linear model which corrects for the implied volatility's bias can provide a useful market-based estimator of conditional volatility. q 1998 Elsevier Science B.V. All rights reserved. JEL classification: G12; G13
Forecasting Equity Index Volatility: Empirical Evidence from Japan, UK and USA Data
Financial Risk and Management Reviews
Using non-linear models to forecast volatility for three equity index samples, this study examines weekly returns of three indices; Dow Jones Industrial index, FTSE 100 index, and Nikkei 225 index. The sample covers a twenty year sample period. The study employs an in sample and out of sample volatility forecast using standard symmetric loss functions in order to identify an appropriate model that best forecast volatility. Using the mean error (ME), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), the study finds the EGARCH model to outperform the ARCH, and GARCH model in forecasting volatility. Contribution/Originality: This is among the first studies that found EGARCH model to outperform the ARCH, and GARCH model in forecasting volatility using a combination of Japan, UK and US data.
Forecasting Power of Implied Volatility: Evidence from Individual Equities
SSRN Electronic Journal, 2005
Assuming use of the correct option pricing model and an efficient market, an option's implied volatility is the market's consensus forecast of future realized volatility over the remaining life of that option. We examine 460 of the S&P 500 firms to demonstrate that 1) implied volatility is a better forecaster of realized volatility than historic volatility or GARCH models and 2) the information content of implied volatility significantly decreases with liquidity. Since individual equity options are American style, we obtain implied volatility from calls and puts separately rather than only calls or pooled data.
Global COE Hi-Stat Discussion Paper …, 2009
In this paper, we apply the ARFIMA-GARCH model to the realized volatility and the continuous sample path variations constructed from high-frequency Nikkei 225 data. While the homoskedastic ARFIMA model performs excellently in predicting the Nikkei 225 realized volatility time series and their square-root and log transformations, the residuals of the model suggest presence of strong conditional heteroskedasticity similar to the nding of Corsi et al. (2007) for the realized S&P 500 futures volatility. An ARFIMA model augmented by a GARCH(1,1) specication for the error term largely captures this and substantially improves the t to the data. In a multi-day forecasting setting, we also nd some evidence of predictable time variation in the volatility of the Nikkei 225 volatility captured by the ARFIMA-GARCH model.
GARCH-based Volatility Forecasts for Implied Volatility Indices
2002
Volatility forecasting is one of the main issues in the financial econometrics literature. Besides the many statistical models proposed throughout the years to estimate and forecast conditional variance, professional operators may rely on alternative indices of volatility supplied by banks, consultants or financial analysts. Among those indices, one of the most widely used is the so-called VXN, computed using the implied volatility of the options written on the NASDAQ–100 Index that is supplied by CBOE since 1995. In this paper we show how forecasts obtained with traditional GARCH–type models can be used to forecast the volatility index VXN.
Information content of implied volatility for Asian equity indices
2005
In this paper we look at the ability of implied volatility to predict the volatility realized over the life of the option in Asian equity indices. We find that in Hong Kong and Taiwan the implied volatility is an unbiased predictor of future realized volatility, whereas in South Korea and India the implied volatility is a poor predictor. Regardless of information content of implied volatility for future realized volatility, it is nevertheless true that there is a great deal of predictability of implied volatility across all Asian markets.
Implied Volatility V/s Realized Volatility A Forecasting Dimension for Indian Markets
Delhi Business Review, 2016
THE aim of the present study is to examine the forecasting efficiency of implied volatility index of India in predicting the future stock market volatility. Therefore, the forecasting efficacy of implied volatility index is compared with intra high-low price range volatility in providing volatility forecasts for S&P CNX Nifty 50 index. Design/Methodology/Approach: The generalized autoregressive conditional heteroskedasticity model (GJR-GARCH) is used for the Indian markets as this model captures the asymmetric effect of good news and bad news on conditional volatility. The GJR-GARCH model is augmented with implied volatility and high-low price range volatility.This model is used to compare the forecasting efficiency of implied volatility index with the realised volatility represented by high-low range price volatility, to find out which is a better measure of forecasting the future stock market volatility. For measuring the forecasting performance of IVIX on various forecasting horizons (1-, 5, 10-and 20-days), the test for in-sample and out-of-sample data is done.
GARCH-based Volatility Forecasts for Market Volatility Indices
2002
Volatility forecasting is one of the main issues in the financial econometrics literature. Volatility measures may be derived from statistical models for conditional variance, or from option prices. In recent times, indices have been suggested which summarize the implied volatility of widely traded market index options. One such index is the so-called VXN, an average of 30-day ahead implied volatilities of the options written on the NASDAQ-100 Index. In this paper we show how forecasts obtained with traditional GARCH-type models can be used to forecast the volatility index VXN.
Volatility Forecasts for the RTS Stock Index: Option-Implied Volatility Versus Alternative Methods
2019
This paper compares volatility forecasts for the RTS Index (the main index for the Russian stock market) generated by alternative models, specifically option-implied volatility forecasts based on the Black-Scholes model, ARCH/GARCH-type model forecasts, and forecasts combining those two using a mixing strategy based either on a simple average or a weighted average with the weights being determined according to two different criteria (either minimizing the errors or maximizing the information content). Various forecasting performance tests are carried out which suggest that both implied volatility and combination methods using a simple average outperform ARCH/GARCH-type models in terms of forecasting accuracy.