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Absolute Returns (absolute + return)
Selected AbstractsMarket interdependence and financial volatility transmission in East AsiaINTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, Issue 1 2009Giampiero M. Gallo Abstract In this paper, we adapt the Multiplicative Error Model (MEM) to analyze the interdependence of volatility across markets. The MEM specifies the dynamics of a volatility proxy (absolute returns) for one market including terms accounting for an asymmetric impact of good or bad news on the market, and possible volatility spillover terms from other markets. The specific empirical focus of the paper is on the interdependence structure of seven East Asian markets between 1990 and 2005. We pay specific attention to the stability of the significance of the links across markets on subperiods that consider or exclude the 1997 crisis and contrast results between earlier samples and more recent ones. Copyright © 2008 John Wiley & Sons, Ltd. [source] Persistence in some energy futures marketsTHE JOURNAL OF FUTURES MARKETS, Issue 5 2010Juncal Cunado In this study, we examine the possibility of long-range dependence in some energy futures markets for different maturities. In order to test for persistence, we use a variety of techniques based on non-parametric, semi-parametric and parametric methods. The results indicate that there is little or no evidence of long memory in gasoline, propane, oil and heating oil at different maturities. However, when we focus on the volatility process, proxied by the absolute returns, we find strong evidence of long memory in all the variables at different contracts. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:490,507, 2010 [source] Minimum capital requirement calculations for UK futuresTHE JOURNAL OF FUTURES MARKETS, Issue 2 2004John Cotter Key to the imposition of appropriate minimum capital requirements on a daily basis is accurate volatility estimation. Here, measures are presented based on discrete estimation of aggregated high-frequency UK futures realizations underpinned by a continuous time framework. Squared and absolute returns are incorporated into the measurement process so as to rely on the quadratic variation of a diffusion process and be robust in the presence of fat tails. The realized volatility estimates incorporate the long memory property. The dynamics of the volatility variable are adequately captured. Resulting rescaled returns are applied to minimum capital requirement calculations. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:193,220, 2004 [source] APPROXIMATING VOLATILITIES BY ASYMMETRIC POWER GARCH FUNCTIONSAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 2 2009Jeremy Penzer Summary ARCH/GARCH representations of financial series usually attempt to model the serial correlation structure of squared returns. Although it is undoubtedly true that squared returns are correlated, there is increasing empirical evidence of stronger correlation in the absolute returns than in squared returns. Rather than assuming an explicit form for volatility, we adopt an approximation approach; we approximate the ,th power of volatility by an asymmetric GARCH function with the power index , chosen so that the approximation is optimum. Asymptotic normality is established for both the quasi-maximum likelihood estimator (qMLE) and the least absolute deviations estimator (LADE) in our approximation setting. A consequence of our approach is a relaxation of the usual stationarity condition for GARCH models. In an application to real financial datasets, the estimated values for , are found to be close to one, consistent with the stylized fact that the strongest autocorrelation is found in the absolute returns. A simulation study illustrates that the qMLE is inefficient for models with heavy-tailed errors, whereas the LADE is more robust. [source] | ||||||||||