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Stock Volatility (stock + volatility)
Selected AbstractsOne-way analysis of variance with long memory errors and its application to stock return dataAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 6 2007Jaechoul Lee Abstract Recent empirical results indicate that many financial time series, including stock volatilities, often have long-range dependencies. Comparing volatilities in stock returns is a crucial part of the risk management of stock investing. This paper proposes two test statistics for testing the equality of mean volatilities of stock returns using the analysis of variance (ANOVA) model with long memory errors. They are modified versions of the ordinary F statistic used in the ANOVA models with independently and identically distributed errors. One has a form of the ordinary F statistic multiplied by a correction factor, which reflects slowly decaying autocorrelations, that is, long-range dependence. The other is a test statistic such that the degrees of freedom of the denominator in the ordinary F test statistic is calibrated by the so-called effective sample size. Empirical sizes and powers of the proposed test statistics are examined via Monte Carlo simulation. An application to German stock returns is presented. Copyright © 2007 John Wiley & Sons, Ltd. [source] Volatility Information Trading in the Option MarketTHE JOURNAL OF FINANCE, Issue 3 2008SOPHIE X. NI ABSTRACT This paper investigates informed trading on stock volatility in the option market. We construct non-market maker net demand for volatility from the trading volume of individual equity options and find that this demand is informative about the future realized volatility of underlying stocks. We also find that the impact of volatility demand on option prices is positive. More importantly, the price impact increases by 40% as informational asymmetry about stock volatility intensifies in the days leading up to earnings announcements and diminishes to its normal level soon after the volatility uncertainty is resolved. [source] Does Idiosyncratic Risk Really Matter?THE JOURNAL OF FINANCE, Issue 2 2005TURAN G. BALI ABSTRACT Goyal and Santa-Clara (2003) find a significantly positive relation between the equal-weighted average stock volatility and the value-weighted portfolio returns on the NYSE/AMEX/Nasdaq stocks for the period of 1963:08 to 1999:12. We show that this result is driven by small stocks traded on the Nasdaq, and is in part due to a liquidity premium. In addition, their result does not hold for the extended sample of 1963:08 to 2001:12 and for the NYSE/AMEX and NYSE stocks. More importantly, we find no evidence of a significant link between the value-weighted portfolio returns and the median and value-weighted average stock volatility. [source] Estimation and forecasting of stock volatility with range-based estimatorsTHE JOURNAL OF FUTURES MARKETS, Issue 6 2008Joshy 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] | ||||||||||