By analyzing the variability of global SST （sea surface temperature） anomalies, we propose a unified Nifio index using the surface thermal centroid anomaly of the region along the Pacific equator embraced by the 0.7...By analyzing the variability of global SST （sea surface temperature） anomalies, we propose a unified Nifio index using the surface thermal centroid anomaly of the region along the Pacific equator embraced by the 0.7~C contour line of the standard deviation of the SST anomalies and try to unify the traditional Nifio regions into a single entity. The unified Nifio region covers almost all of the traditional Nifio regions. The anomaly time series of the averaged SST over this region are closely correlated to historical Nifio indices. The anomaly time series of the zonal and meridional thermal centroid have close correlation with historical TNI （Trans-Nifio index） indices, showing differences among E1 Nifio （La Nifia） events. The meridional centroid anomaly suggests that areas of maximum temperature anomaly are moving meridionally （although slightly） with synchronous zonal movement. The zonal centroid anomalies of the unified Nifio region are found helpful in the classification of the Eastern Pacific （EP）/Central Pacific （CP） types of E1 Nifio events. More importantly, the zonal centroid anomaly shows that warm areas might move during a single warming/cooling phase. All the current Nifio indices can be well represented by a simple linear combination of unified Nifio indices, which suggests that the thermal anomaly （SSTA） and thermal centroid location anomaly of the unified Nifio region would yield a more complete image of each E1 Nifio/ La Nina event.展开更多
The threshold GARCH(TGARCH)models have been very useful for analyzing asymmetric volatilities arising from financial time series.Most research on TGARCH has been directed to the stationary case.This paper studies the ...The threshold GARCH(TGARCH)models have been very useful for analyzing asymmetric volatilities arising from financial time series.Most research on TGARCH has been directed to the stationary case.This paper studies the estimation of non-stationary first order TGARCH models.Restricted normal mixture quasi-maximum likelihood estimation(NM-QMLE)for non-stationary TGARCH models is proposed in the sense that we estimate the other parameters with any fixed location parameter.We show that the proposed estimators(except location parameter)are consistent and asymptotically normal under mild regular conditions.The impact of relative leptokursis and skewness of the innovations’distribution and quasi-likelihood distributions on the asymptotic efficiency has been discussed.Numerical results lend further support to our theoretical results.Finally,an illustrated real example is presented.展开更多
基金Supported by the National Basic Research Program of China(973 Program)(Nos.2012CB957704,2009CB723903)the National Natural Science Foundation of China(Nos.40506035,40876005)
文摘By analyzing the variability of global SST （sea surface temperature） anomalies, we propose a unified Nifio index using the surface thermal centroid anomaly of the region along the Pacific equator embraced by the 0.7~C contour line of the standard deviation of the SST anomalies and try to unify the traditional Nifio regions into a single entity. The unified Nifio region covers almost all of the traditional Nifio regions. The anomaly time series of the averaged SST over this region are closely correlated to historical Nifio indices. The anomaly time series of the zonal and meridional thermal centroid have close correlation with historical TNI （Trans-Nifio index） indices, showing differences among E1 Nifio （La Nifia） events. The meridional centroid anomaly suggests that areas of maximum temperature anomaly are moving meridionally （although slightly） with synchronous zonal movement. The zonal centroid anomalies of the unified Nifio region are found helpful in the classification of the Eastern Pacific （EP）/Central Pacific （CP） types of E1 Nifio events. More importantly, the zonal centroid anomaly shows that warm areas might move during a single warming/cooling phase. All the current Nifio indices can be well represented by a simple linear combination of unified Nifio indices, which suggests that the thermal anomaly （SSTA） and thermal centroid location anomaly of the unified Nifio region would yield a more complete image of each E1 Nifio/ La Nina event.
基金supported by National Natural Science Foundation of China (Grant No.11101448)the Program for New Century Excellent Talents in University+3 种基金the Program for Young Talents of Beijing (Grant No.YETP0955)the Program for National Statistics Science Research Plan (Grant No.2013LY015)the "Project 211" of the Central University of Finance and Economicsthe Central University of Finance Young Scholar Innovation Fund
文摘The threshold GARCH(TGARCH)models have been very useful for analyzing asymmetric volatilities arising from financial time series.Most research on TGARCH has been directed to the stationary case.This paper studies the estimation of non-stationary first order TGARCH models.Restricted normal mixture quasi-maximum likelihood estimation(NM-QMLE)for non-stationary TGARCH models is proposed in the sense that we estimate the other parameters with any fixed location parameter.We show that the proposed estimators(except location parameter)are consistent and asymptotically normal under mild regular conditions.The impact of relative leptokursis and skewness of the innovations’distribution and quasi-likelihood distributions on the asymptotic efficiency has been discussed.Numerical results lend further support to our theoretical results.Finally,an illustrated real example is presented.
