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Stock Return Volatility (stock + return_volatility)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	85%

Selected Abstracts

How Persistent is Stock Return Volatility?

JOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 5-6 2007
An Answer with Markov Regime Switching Stochastic Volatility Models
Abstract:, We propose generalised stochastic volatility models with Markov regime changing state equations (SVMRS) to investigate the important properties of volatility in stock returns, specifically high persistence and smoothness. The model suggests that volatility is far less persistent and smooth than the conventional GARCH or stochastic volatility. Persistent short regimes are more likely to occur when volatility is low, while far less persistence is likely to be observed in high volatility regimes. Comparison with different classes of volatility supports the SVMRS as an appropriate proxy volatility measure. Our results indicate that volatility could be far more difficult to estimate and forecast than is generally believed. [source]

The Impact of Trade Characteristics on Stock Return Volatility: Evidence from the Australian Stock Exchange,

ASIA-PACIFIC JOURNAL OF FINANCIAL STUDIES, Issue 2 2009
Alex Frino
Abstract This study examines the impact of trade characteristics on stock return volatility. Using a sample of transaction data from the Australian Stock Exchange, the trading frequency of medium sized trades is found to have the greatest impact on stock return volatility. The result lends support to the stealth trading hypothesis (Barclay and Warner, 1993). After controlling for trading frequency, the average trade size is found to have little explanatory power on price volatility. Stock return volatility is more sensitive to buyer-initiated trades than seller-initiated trades, especially so for buyer-initiated medium sized trades. This finding is consistent with the assertion that information effects are stronger for buys than for sells (Chan and Lakonishok, 1993). [source]

FINANCIAL CRISES AND INTERNATIONAL STOCK MARKET VOLATILITY TRANSMISSION

AUSTRALIAN ECONOMIC PAPERS, Issue 3 2010
INDIKA KARUNANAYAKE
This paper examines the interplay between stock market returns and their volatility, focusing on the Asian and global financial crises of 1997,98 and 2008,09 for Australia, Singapore, the UK, and the US. We use a multivariate generalised autoregressive conditional heteroskedasticity (MGARCH) model and weekly data (January 1992,June 2009). Based on the results obtained from the mean return equations, we could not find any significant impact on returns arising from the Asian crisis and more recent global financial crises across these four markets. However, both crises significantly increased the stock return volatilities across all of the four markets. Not surprisingly, it is also found that the US stock market is the most crucial market impacting on the volatilities of smaller economies such as Australia. Our results provide evidence of own and cross ARCH and GARCH effects among all four markets, suggesting the existence of significant volatility and cross volatility spillovers across all four markets. A high degree of time-varying co-volatility among these markets indicates that investors will be highly unlikely to benefit from diversifying their financial portfolio by acquiring stocks within these four countries only. [source]

Bayesian modeling of financial returns: A relationship between volatility and trading volume

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 2 2010
Carlos A. Abanto-Valle
Abstract The modified mixture model with Markov switching volatility specification is introduced to analyze the relationship between stock return volatility and trading volume. We propose to construct an algorithm based on Markov chain Monte Carlo simulation methods to estimate all the parameters in the model using a Bayesian approach. The series of returns and trading volume of the British Petroleum stock will be analyzed. Copyright © 2009 John Wiley & Sons, Ltd. [source]

On the Quantile Regression Based Tests for Asymmetry in Stock Return Volatility

ASIAN ECONOMIC JOURNAL, Issue 2 2002
Beum-Jo Park
This paper attempts to examine whether the asymmetry of stock return volatility varies with the level of volatility. Thus, quantile regression based tests (,-tests) are presupposed. These tests differ from the diagnostic tests introduced by Engle and Ng (1993) insofar as they can provide a complete picture of asymmetries in volatility across quantiles of variance distribution and, in case of non-normal errors, they have improved power due to their robustness against non-normality. A small Monte Carlo evidence suggests that the Wald and likelihood ratio (LR) tests out of ,-tests are reasonable, showing that they outperform the Lagrange multiplier (LM) test based on least squares residuals when the innovations exhibit heavy tail. Using the normalized residuals obtained from AR(1)-GARCH(1, 1) estimation, the test results demonstrated that only the TOPIX out of six stock-return series had asymmetry in volatility at moderate level, while all stock return series except the FAZ and FA100 had more significant asymmetry in volatility at higher levels. Interestingly, it is clear from the empirical findings that, like hypothesis of leverage effects, volatility of the TOPIX, CAC40, and, MIB tends to respond significantly to extremely negative shock at high level, but is not correlated with any positive shock. These might be valuable findings that have not been seriously considered in past research, which has focussed only on mean level of volatility. [source]