Jiang and and Tian (2010) have estimated an ARFIMA model for stock return volatility. We argue that this result does not imply actual 'long memory' in such time series -as any kind of instability in the population mean yields apparent fractional integration as a statistical artifact. Alternative high-pass filters for studying stock market volatility data are suggested.

This paper focuses on the performance of various GARCH models in terms of their ability of delivering volatility forecasts for stock return data. Volatility forecasts obtained from a variety of mean and variance specifications in GARCH models are compared to a proxy of actual volatility calculated using daily data. In-sample tests suggest that a regression of volatility estimates on actual vola...

This paper investigates empirically the effect of volatility of the exchange rate of the U.S. dollar vis-à-vis the euro on U.S. stock market volatility while controlling for a number of drivers of stock return volatility. Using a GARCH(1, 1) model and using weekly data covering the period from the week of January 1, 1999 through the week of January 25, 2010, it is found that the 9/11 terrorist ...

oil-exporting economies largely dependent on oil revenues and oil income fluctuation are one of the most important factors that influence sectors of the economy specially the stock market. this paper investigate the relationship between oil markets and stock return volatility and transmission in a selection of opec countries, using a multivariate garch models (full-vech) over the period may 201...

This study examines the relationships between stock fundamental ratios and idiosyncratic volatility from 1993 to 2010 for Australian Securities Exchange listed companies. The portfolio analysis results show that high idiosyncratic volatility companies tend to be small (measured by size), highly leveraged (measured by interest cover ratio), low profitability (measured by return on equity and ear...

Implied option volatility averages about 19% per year, while the unconditional return volatility is only about 16%. The difference, coined the volatility premium, is substantial and translates into large returns for sellers of index options. This paper studies a general equilibrium model based on long-run risk which in an effort to explain the premium. In estimating the model on past data of st...

I study how growth affects liquidity of global stock exchanges and how liquidity determines cross-sectional returns on those stock exchange index portfolios. I measure portfolio liquidity by turnover ratio computed as value of shares traded over the market capitalization. I obtain data from FIBV, an association of global stock exchanges. In a multiple regression model for turnover ratio, I find...

Financial markets are one of the most fundamental markets in any country. In the financial markets, the securities market and the foreign exchange market are sensitive sectors. These two markets are affected by fluctuations and economic cycles so reflect economic changes rapidly. Changes in the returns of one market due to arbitrage conditions during time lead to changes in the returns of other...

We examine how the evidence of the time-varying volatility in stock returns affects optimal dynamic portfolio choice of investors with long horizons. As return volatility shows a relatively small correlation with realized return, its time-variation is expected to cause little, if any, hedging demand (in the sense of Merton (1973)). However, we find that, once transaction costs are taken into ac...