This paper introduces the News Impact Curve to measure how new information is incorporated into volatility estimates. A variety of new and existing ARCH models are compared and estimated with daily Japanese stock return data to determine the shape of the News Impact Curve. New diagnostic tests are presented which emphasize the asymmetry of the volatility response to news. A partially non-parametric ARCH model is introduced to allow the data to estimate this shape. A comparison of this model with the existing models suggests that the best models are one by Glosten Jaganathan and Runkle (GJR) and Nelson's EGARCE. Similar results hold on a pre--crash sample period but are less strong. It is now well established that volatility is predictable in most financial markets. In a recent survey by Bollerslev et al (1990) over 200 articles were cited which estimated or examined ARCH or alternative models of time varying heteroskedasticity. With this growth in interest and applications, there has also grown a literature on alternative models which are designed to allow different features of the data to be reflected in the model. Some of these are tightly parameteric models while others are non-parametric in spirit.In this paper, we suggest a new metric with which these volatility models can be compared. We discuss some of the alternative models which are being tried and introduce several models of our own which should nest many of the existing models. We will also suggest several new diagnostic tests for volatility models.In the next section, we discuss several models of predictable volatility and introduce the idea of a News Impact Curve which characterizes the impact of innovations on volatility implicit in a volatility model. In section III, we suggest several new diagnostic tests based on the News Impact Curve. In section IV, a partially non-parametric ARCH model is introduced. Section V presents and compares empirical estimates of several volatility models using a Japanese stock returns series. The new diagnostic tests are employed to check the adequacy of the models. In section VI the partially non-parametric model is estimated and compared with the others, and in section VII, the best models are reestimated on a pre-crash sample period. Section VIII concludes the paper. Apparently, the GARCH model is an infinite order ARCH model and often provides a highly parsimonious lag shape. Empirically these models have been very successful with the GARCH(1,1) the general favorite in the vast majority of cases. Furthermore, these applications typically reveal that there is a long term persistence in the effects of shocks in period t onto the conditional volatility in period t+s for large s. That is, there typically appears to be a unit root in the autoregressive polynomial associated with (2) or (3).In spite of the apparent success of these simple parametrizations, there are some features of the data which these models are unable to pick out. The most interesting of these is the "leverage" effect emphasized by Nelson (19...

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This paper introduces the News Impact Curve to measure how new information is incorporated into volatility estimates. A variety of new and existing ARCH models are compared and estimated with daily Japanese stock return data to determine the shape of the News Impact Curve. New diagnostic tests are presented which emphasize the asymmetry of the volatility response to news. A partially non-parametric ARCH model is introduced to allow the data to estimate this shape. A comparison of this model with the existing models suggests that the best models are one by Glosten Jaganathan and Runkle (GJR) and Nelson's EGARCE. Similar results hold on a pre--crash sample period but are less strong. It is now well established that volatility is predictable in most financial markets. In a recent survey by Bollerslev et al (1990) over 200 articles were cited which estimated or examined ARCH or alternative models of time varying heteroskedasticity. With this growth in interest and applications, there has also grown a literature on alternative models which are designed to allow different features of the data to be reflected in the model. Some of these are tightly parameteric models while others are non-parametric in spirit.In this paper, we suggest a new metric with which these volatility models can be compared. We discuss some of the alternative models which are being tried and introduce several models of our own which should nest many of the existing models. We will also suggest several new diagnostic tests for volatility models.In the next section, we discuss several models of predictable volatility and introduce the idea of a News Impact Curve which characterizes the impact of innovations on volatility implicit in a volatility model. In section III, we suggest several new diagnostic tests based on the News Impact Curve. In section IV, a partially non-parametric ARCH model is introduced. Section V presents and compares empirical estimates of several volatility models using a Japanese stock returns series. The new diagnostic tests are employed to check the adequacy of the models. In section VI the partially non-parametric model is estimated and compared with the others, and in section VII, the best models are reestimated on a pre-crash sample period. Section VIII concludes the paper. Apparently, the GARCH model is an infinite order ARCH model and often provides a highly parsimonious lag shape. Empirically these models have been very successful with the GARCH(1,1) the general favorite in the vast majority of cases. Furthermore, these applications typically reveal that there is a long term persistence in the effects of shocks in period t onto the conditional volatility in period t+s for large s. That is, there typically appears to be a unit root in the autoregressive polynomial associated with (2) or (3).In spite of the apparent success of these simple parametrizations, there are some features of the data which these models are unable to pick out. The most interesting of these is the "leverage" effect emphasized by Nelson (19...