			Home About us Contact

Volatility Estimators (volatility + estimator)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	100%

Selected Abstracts

The quality of volatility traded on the over-the-counter currency market: A multiple horizons study

THE JOURNAL OF FUTURES MARKETS, Issue 3 2003
Vicentiu Covrig
Previous studies of the quality of market-forecasted volatility have used the volatility that is implied by exchange-traded option prices. The use of implied volatility in estimating the market view of future volatility has suffered from variable measurement errors, such as the non-synchronization of option and underlying asset prices, the expiration-day effect, and the volatility smile effect. This study circumvents these problems by using the quoted implied volatility from the over-the-counter (OTC) currency option market, in which traders quote prices in terms of volatility. Furthermore, the OTC currency options have daily quotes for standard maturities, which allows the study to look at the market's ability to forecast future volatility for different horizons. The study finds that quoted implied volatility subsumes the information content of historically based forecasts at shorter horizons, and the former is as good as the latter at longer horizons. These results are consistent with the argument that measurement errors have a substantial effect on the implied volatility estimator and the quality of the inferences that are based on it. © 2003 Wiley Periodicals, Inc. Jrl Fut Mark 23:261,285, 2003 [source]

Consistent High-precision Volatility from High-frequency Data

ECONOMIC NOTES, Issue 2 2001
Fulvio Corsi
Estimates of daily volatility are investigated. Realized volatility can be computed from returns observed over time intervals of different sizes. For simple statistical reasons, volatility estimators based on high-frequency returns have been proposed, but such estimators are found to be strongly biased as compared to volatilities of daily returns. This bias originates from microstructure effects in the price formation. For foreign exchange, the relevant microstructure effect is the incoherent price formation, which leads to a strong negative first-order autocorrelation ,(1),40 per cent for tick-by-tick returns and to the volatility bias. On the basis of a simple theoretical model for foreign exchange data, the incoherent term can be filtered away from the tick-by-tick price series. With filtered prices, the daily volatility can be estimated using the information contained in high-frequency data, providing a high-precision measure of volatility at any time interval. (J.E.L.: C13, C22, C81). [source]

Estimation and forecasting of stock volatility with range-based estimators

THE JOURNAL OF FUTURES MARKETS, Issue 6 2008
Joshy Jacob
This paper examines the estimation and forecasting performance of range-based volatility estimators for stocks, with two-scales realized volatility as the benchmark. There is evidence that the daily range-based estimators provide an efficient and low-bias alternative to the return-based estimators. These are not downwardly biased in the presence of negative autocorrelation and low liquidity, as generally suspected. The drift is a major cause of the poor performance of Parkinson's estimator. The forecasts of volatility with these estimators are about as efficient as those with the benchmark itself but are more biased. The forecasts based on realized range are only marginally better on the criterion of bias and are about as efficient. Considering their simplicity and lower data requirement, the daily range-based estimators appear to be more desirable. These results are particularly relevant for the option valuation and the risk management of derivative markets. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:561,581, 2008 [source]

Forecasting performance of extreme-value volatility estimators

THE JOURNAL OF FUTURES MARKETS, Issue 11 2007
Vipul
This study evaluates the forecasting performance of extreme-value volatility estimators for the equity-based Nifty Index using two-scale realized volatility. This benchmark mitigates the effect of microstructure noise in the realized volatility. Extreme-value estimates with relatively simple forecasting methods provide substantially better short-term and long-term forecasts, compared to historical volatility. The higher efficiency of extreme-value estimators is primarily responsible for this improvement. The extent of possible improvement in forecasts is likely to be economically significant for applications like options pricing. By including extremevalue estimators, the forecasting performance of generalized autoregressive conditional heteroscedasticity (GARCH) can also be improved. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27: 1085,1105, 2007 [source]

Testing range estimators of historical volatility

THE JOURNAL OF FUTURES MARKETS, Issue 3 2006
Jinghong Shu
This study investigates the relative performance of various historical volatility estimators that incorporate daily trading range: M. Parkinson (1980), M. Garman and M. Klass (1980), L. C. G. Rogers and S. E. Satchell (1991), and D. Yang and Q. Zhang (2000). It is found that the range estimators all perform very well when an asset price follows a continuous geometric Brownian motion. However, significant differences among various range estimators are detected if the asset return distribution involves an opening jump or a large drift. By adding microstructure noise to the Monte Carlo simulation, the finding of S. Alizadeh, M. W. Brandt, and F. X. Diebold (2002),that range estimators are fairly robust toward microstructure effects,is confirmed. An empirical test with S&P 500 index return data shows that the variances estimated with range estimators are quite close to the daily integrated variance. The empirical results support the use of range estimators for actual market data. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:297,313, 2006 [source]

A comparative study of alternative extreme-value volatility estimators

THE JOURNAL OF FUTURES MARKETS, Issue 9 2005
Turan G. Bali
Recent advances in econometric methodology and newly available sources of data are used to examine empirically the performance of the various extreme-value volatility estimators that have been proposed over the past two decades. Overwhelming support is found for the use of extreme-value estimators when computing daily volatility measures across all assets: Daily extreme-value volatility estimators are both less biased and substantially more efficient than the traditional close-to-close estimator. In the case of weekly and monthly measures, the results still suggest that extreme-value estimators are appropriate, but the evidence is more mixed. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:873,892, 2005 [source]