nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒06‒09
two papers chosen by
Yong Yin
SUNY at Buffalo

  1. Asymptotic Theory for Beta-t-GARCH By Ryoko Ito
  2. Spatial Autoregressive Conditional Heteroscedasticity Model and Its Application By Takaki Sato; Yasumasa Matsuda

  1. By: Ryoko Ito
    Abstract: The dynamic conditional score (DCS) models with variants of Student's t innovation are gaining popularity in volatility modeling, and studies have found that they outperform GARCH-type models of comparable specifications. DCS is typically estimated by the method of maximum likelihood, but there is so far limited asymptotic theories for justifying the use of this estimator for non-Gaussian distributions. This paper develops asymptotic theory for Beta-t-GARCH, which is DCS with Student's t innovation and the benchmark volatility model of this class. We establish the necessary and sufficient condition for strict stationarity of the first-order Beta-t-GARCH using one simple moment equation, and show that its MLE is consistent and asymptotically normal under this condition. The results of this paper theoretically justify applying DCS with Student's t innovation to heavy-tailed data with a high degree of kurtosis, and performing standard statistical inference for model selection using the estimator. Since GARCH is Beta-t-GARCH with infinite degrees of freedom, our results imply that Beta-t-GARCH can capture the size of the tail or the degree of kurtosis that is too large for GARCH.
    Keywords: robustness, score, consistency, asymptotic normality.
    JEL: C22 C58
    Date: 2016–01–24
  2. By: Takaki Sato; Yasumasa Matsuda
    Abstract: This paper proposes spatial autoregressive conditional heteroscedasticity (S- ARCH) models to estimate spatial volatility in spatial data. S-ARCH model is a spatial extension of time series ARCH model. S-ARCH models specify conditional variances as the variances given the values of surrounding observations in spatial data, which is regarded as a spatial extension of time series ARCH models that spec- ify conditional variances as the variances given the values of past observations. We consider parameter estimation for S-ARCH models by maximum likelihood method and propose test statistics for ARCH effects in spatial data. We demonstrate the empirical properties by simulation studies and real data analysis of land price data in Tokyo.
    Date: 2016–04–26

This nep-ets issue is ©2016 by Yong Yin. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.