Dual long memory property in returns and volatility: Evidence from the CEE countries' stock markets

Predicting BRICS stock returns using ARFIMA models

“…Furthermore, all the markets investigated provide evidence of long memory in volatility with the exception of Botswana. Kasman et al (2009) investigate the presence of long memory in eight Central and Eastern European (CEE) countries' stock market, using the ARFIMA, GPH, FIGARCH and HYGARCH models. The results of these models indicate strong evidence of long memory both in conditional mean and conditional variance.…”

“…However, there are challenges with regard to predicting stock returns of emerging stock market returns. Emerging markets are generally characterized by lower levels of liquidity and at the same time by a higher volatility than developed financial markets (Barkoulas et al, 2000;Kasman et al, 2009). High volatility in these markets is often marked by frequent and erratic changes, which are usually driven by various local events (such as political developments) rather than by the events of global importance (Bekaer and Aggarwal et al, 1999).…”

“…The only related studies for emerging markets are Kasman, et al (2009) returns by presenting ex ante forecasting evidence based on data sets from BRICS.…”

The Long Memory of Equity Volatility: International Evidence

“…Kasman et al (2009) show evidence of long memory dynamics in both the conditional mean and variance for eight Central and Eastern European countries' stock markets and also rely on the both semiparametric (GPH) and parametric (ARFIMA, FIGARCH and HYGARCH) estimation procedures.…”

Study on the development of nonferrous metal futures market

“…Non ferrous metals futures price change law related literature Clinton Watkins, Michael Mc Aleerto Aluminum Alloy, aluminum, copper, lead, nickel, tin, zinc 3 month futures contract data as the research object, using the risk premium hypothesis and cost model, the empirical research shows that if the spot price, futures price, interest rate and stock price variable contains a stochastic trend, you can estimate the long-term version of the general model in association within the framework of the whole [1]. Kasman A, et al on the London metal exchange fluid and the five most important nonferrous metals (copper, aluminum, contract lead, nickel and zinc) as the research object, using multivariate heterogeneous autoregressive (HAR) model of empirical research indicates that other industrial metals volatility series seems to contain useful information for future incremental price fluctuations in the long term, the most obvious the volatility spillover [2]. Yue Y, et alIn order to Chinese nonferrous metal futures market of copper and aluminum futures as the research object, the relationship between the research, return volatility and trading activity results show that when the frequent transactions, revenue volatility increases, and when the public interest is increased, the decline in the volatility of the return [3].…”