Consider a first-order autoregressive processes , where the innovations are nonnegative random variables with regular variation at both the right endpoint infinity and the unknown left endpoint θ. We propose estimate...Consider a first-order autoregressive processes , where the innovations are nonnegative random variables with regular variation at both the right endpoint infinity and the unknown left endpoint θ. We propose estimates for the autocorrelation parameter f and the unknown location parameter θ by taking the ratio of two sample values chosen with respect to an extreme value criteria for f and by taking the minimum of over the observed series, where represents our estimate for f. The joint limit distribution of the proposed estimators is derived using point process techniques. A simulation study is provided to examine the small sample size behavior of these estimates.展开更多
Geostatistics of extreme values makes it possible to model the asymptotic behavior of random phenomena that depend on time or space. In this paper, we propose new models of the extremal coefficient of a stationary ran...Geostatistics of extreme values makes it possible to model the asymptotic behavior of random phenomena that depend on time or space. In this paper, we propose new models of the extremal coefficient of a stationary random field where the cumulative distribution is associated with a multivariate copula. More precisely, some models of extensions of the extremogram and these derivatives are built in a spatial framework. Moreover, both these two geostatistical tools are modeled using the extremal variogram which characterizes the asymptotic stochastic behavior of the phenomena.展开更多
This paper expounds the nitty-gritty of stock returns transitory, periodical behavior </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><...This paper expounds the nitty-gritty of stock returns transitory, periodical behavior </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">its markets’ demands and cyclical-like tenure-changing of number of the stocks sold. Mingling of autoregressive random processes via Poisson and Extreme-Value-Distributions (Fréchet, Gumbel, and Weibull) error terms were designed, generalized and imitated to capture stylized traits of </span><span style="font-family:Verdana;">k-serial tenures (ability to handle cycles), Markov transitional mixing weights</span><span style="font-family:Verdana;">, switching of mingling autoregressive processes and full range shape changing </span><span style="font-family:Verdana;">predictive distributions (multimodalities) that are usually caused by large fluctuation</span><span style="font-family:Verdana;">s (outliers) and long-memory in stock returns. The Poisson and Extreme-Value-Distributions Mingled Autoregressive (PMA and EVDs) models were applied to a monthly number of stocks sold in Nigeria from 1960 to 2020. It was deduced that fitted Gumbel-MAR (2:1, 1) outstripped other linear models as well as best</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">fitted among the Poisson and Extreme-Value-</span><span style="font-family:Verdana;">Distributions Mingled autoregressive models subjected to the discrete monthly</span><span style="font-family:Verdana;"> stocks sold series.展开更多
文摘Consider a first-order autoregressive processes , where the innovations are nonnegative random variables with regular variation at both the right endpoint infinity and the unknown left endpoint θ. We propose estimates for the autocorrelation parameter f and the unknown location parameter θ by taking the ratio of two sample values chosen with respect to an extreme value criteria for f and by taking the minimum of over the observed series, where represents our estimate for f. The joint limit distribution of the proposed estimators is derived using point process techniques. A simulation study is provided to examine the small sample size behavior of these estimates.
文摘Geostatistics of extreme values makes it possible to model the asymptotic behavior of random phenomena that depend on time or space. In this paper, we propose new models of the extremal coefficient of a stationary random field where the cumulative distribution is associated with a multivariate copula. More precisely, some models of extensions of the extremogram and these derivatives are built in a spatial framework. Moreover, both these two geostatistical tools are modeled using the extremal variogram which characterizes the asymptotic stochastic behavior of the phenomena.
文摘This paper expounds the nitty-gritty of stock returns transitory, periodical behavior </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">its markets’ demands and cyclical-like tenure-changing of number of the stocks sold. Mingling of autoregressive random processes via Poisson and Extreme-Value-Distributions (Fréchet, Gumbel, and Weibull) error terms were designed, generalized and imitated to capture stylized traits of </span><span style="font-family:Verdana;">k-serial tenures (ability to handle cycles), Markov transitional mixing weights</span><span style="font-family:Verdana;">, switching of mingling autoregressive processes and full range shape changing </span><span style="font-family:Verdana;">predictive distributions (multimodalities) that are usually caused by large fluctuation</span><span style="font-family:Verdana;">s (outliers) and long-memory in stock returns. The Poisson and Extreme-Value-Distributions Mingled Autoregressive (PMA and EVDs) models were applied to a monthly number of stocks sold in Nigeria from 1960 to 2020. It was deduced that fitted Gumbel-MAR (2:1, 1) outstripped other linear models as well as best</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">fitted among the Poisson and Extreme-Value-</span><span style="font-family:Verdana;">Distributions Mingled autoregressive models subjected to the discrete monthly</span><span style="font-family:Verdana;"> stocks sold series.
文摘This discussion reviews the paper by Zhengjun Zhang in the context of broader research on multivariate extreme value theory and max-stable processes.
