The modeling of volatility and correlation is important in order to calculate hedge ratios, value at risk estimates, CAPM （Capital Asset Pricing Model betas）, derivate pricing and risk management in general. Recent ...The modeling of volatility and correlation is important in order to calculate hedge ratios, value at risk estimates, CAPM （Capital Asset Pricing Model betas）, derivate pricing and risk management in general. Recent access to intra-daily high-frequency data for two of the most liquid contracts at the Nord Pool exchange has made it possible to apply new and promising methods for analyzing volatility and correlation. The concepts of realized volatility and realized correlation are applied, and this study statistically describes the distribution （both distributional properties and temporal dependencies） of electricity forward data from 2005 to 2009. The main findings show that the logarithmic realized volatility is approximately normally distributed, while realized correlation seems not to be. Further, realized volatility and realized correlation have a long-memory feature. There also seems to be a high correlation between realized correlation and volatilities and positive relations between trading volume and realized volatility and between trading volume and realized correlation. These results are to a large extent consistent with earlier studies of stylized facts of other financial and commodity markets.展开更多
Often in longitudinal studies, some subjects complete their follow-up visits, but others miss their visits due to various reasons. For those who miss follow-up visits, some of them might learn that the event of intere...Often in longitudinal studies, some subjects complete their follow-up visits, but others miss their visits due to various reasons. For those who miss follow-up visits, some of them might learn that the event of interest has already happened when they come back. In this case, not only are their event times interval-censored, but also their time-dependent measurements are incomplete. This problem was motivated by a national longitudinal survey of youth data. Maximum likelihood estimation (MLE) method based on expectation-maximization (EM) algorithm is used for parameter estimation. Then missing information principle is applied to estimate the variance-covariance matrix of the MLEs. Simulation studies demonstrate that the proposed method works well in terms of bias, standard error, and power for samples of moderate size. The national longitudinal survey of youth 1997 (NLSY97) data is analyzed for illustration.展开更多
On the basis of Argo profile data of the temperature and salinity from January 2001 to July 2014, the spatial distributions of an upper ocean heat content(OHC) and ocean salt content(OSC) of the western Pacific warm p...On the basis of Argo profile data of the temperature and salinity from January 2001 to July 2014, the spatial distributions of an upper ocean heat content(OHC) and ocean salt content(OSC) of the western Pacific warm pool(WPWP) region and their seasonal and interannual variations are studied by a cyclostationary empirical orthogonal function(CSEOF) decomposition, a maximum entropy spectral analysis, and a correlation analysis.Probable reasons for variations are discussed. The results show the following.(1) The OHC variations in the subsurface layer of the WPWP are much greater than those in the surface layer. On the contrary, the OSC variations are mainly in the surface layer, while the subsurface layer varies little.(2) Compared with the OSC, the OHC of the WPWP region is more affected by El Ni?o-Southern Oscillation(ENSO) events. The CSEOF analysis shows that the OHC pattern in mode 1 has strong interannual oscillation, with eastern and western parts opposite in phase. The distribution of the OSC has a positive-negative-positive tripole pattern. Time series analysis shows that the OHC has three phase adjustments with the occurrence of ENSO events after 2007, while the OSC only had one such adjustment during the same period. Further analysis indicates that the OHC variations are mainly caused by ENSO events, local winds, and zonal currents, whereas the OSC variations are caused by much more complex reasons. Two of these, the zonal current and a freshwater flux, have a positive feedback on the OSC change in the WPWP region.展开更多
文摘The modeling of volatility and correlation is important in order to calculate hedge ratios, value at risk estimates, CAPM （Capital Asset Pricing Model betas）, derivate pricing and risk management in general. Recent access to intra-daily high-frequency data for two of the most liquid contracts at the Nord Pool exchange has made it possible to apply new and promising methods for analyzing volatility and correlation. The concepts of realized volatility and realized correlation are applied, and this study statistically describes the distribution （both distributional properties and temporal dependencies） of electricity forward data from 2005 to 2009. The main findings show that the logarithmic realized volatility is approximately normally distributed, while realized correlation seems not to be. Further, realized volatility and realized correlation have a long-memory feature. There also seems to be a high correlation between realized correlation and volatilities and positive relations between trading volume and realized volatility and between trading volume and realized correlation. These results are to a large extent consistent with earlier studies of stylized facts of other financial and commodity markets.
文摘Often in longitudinal studies, some subjects complete their follow-up visits, but others miss their visits due to various reasons. For those who miss follow-up visits, some of them might learn that the event of interest has already happened when they come back. In this case, not only are their event times interval-censored, but also their time-dependent measurements are incomplete. This problem was motivated by a national longitudinal survey of youth data. Maximum likelihood estimation (MLE) method based on expectation-maximization (EM) algorithm is used for parameter estimation. Then missing information principle is applied to estimate the variance-covariance matrix of the MLEs. Simulation studies demonstrate that the proposed method works well in terms of bias, standard error, and power for samples of moderate size. The national longitudinal survey of youth 1997 (NLSY97) data is analyzed for illustration.
基金The National Natural Science Foundation of China under contract Nos 41406022 and 41606003the Scientific Research Fund of the Second Institute of Oceanography,State Oceanic Administration of China under contract Nos JG1812 and JG1709the Special Program for the National Basic Research of China under contract No.2012FY112300
文摘On the basis of Argo profile data of the temperature and salinity from January 2001 to July 2014, the spatial distributions of an upper ocean heat content(OHC) and ocean salt content(OSC) of the western Pacific warm pool(WPWP) region and their seasonal and interannual variations are studied by a cyclostationary empirical orthogonal function(CSEOF) decomposition, a maximum entropy spectral analysis, and a correlation analysis.Probable reasons for variations are discussed. The results show the following.(1) The OHC variations in the subsurface layer of the WPWP are much greater than those in the surface layer. On the contrary, the OSC variations are mainly in the surface layer, while the subsurface layer varies little.(2) Compared with the OSC, the OHC of the WPWP region is more affected by El Ni?o-Southern Oscillation(ENSO) events. The CSEOF analysis shows that the OHC pattern in mode 1 has strong interannual oscillation, with eastern and western parts opposite in phase. The distribution of the OSC has a positive-negative-positive tripole pattern. Time series analysis shows that the OHC has three phase adjustments with the occurrence of ENSO events after 2007, while the OSC only had one such adjustment during the same period. Further analysis indicates that the OHC variations are mainly caused by ENSO events, local winds, and zonal currents, whereas the OSC variations are caused by much more complex reasons. Two of these, the zonal current and a freshwater flux, have a positive feedback on the OSC change in the WPWP region.
