This paper investigates the short‐term response of the Saudi stock market (Tadawul) to the COVID‐19 outbreak. Event study methodology applied to data derived from the 21 industry groups that constitute the Saudi stock market to calculate abnormal returns for the trading days after the announcement of the COVID‐19 in both China and Saudi Arabia. The results indicate that the estimated CARs for the industry groups and their sum on the event day were not statistically significant. Furthermore, the formal announcement of the first case of the COVID‐19 in China had a negative but not significant impact on the Saudi stock market. In contrast, in the first 9‐days event window, the announcement of the first confirmed case in Saudi Arabia had a negative and significant effect. Moreover, the most negatively affected industry groups were banks, consumer services, capital goods, transportation and commercial services, whereas telecommunication services and food and beverage were positively affected at the event window (+1, +9). In general, the Saudi stock market's response had become weaker in the event windows come after (+1, +9), and different industry groups were found to have different responses to the COVID‐19 outbreak.
A mixture distribution is a combination of two or more probability distributions; it can be obtained from different distribution families or the same distribution families with different parameters. The underlying distributions may be discrete or continuous, so the resulting mixture probability distribution function should be a mass or density function. In the last few years, there has been great interest in the problem of developing a mixture distribution based on the binomial distribution. This paper uses the probability generating function method to develop a new two-parameter discrete distribution called a binomial-geometric (BG) distribution, a mixture of binomial distribution with the number of trials (parameter n) taken after a geometric distribution. The quantile function, moments, moment generating function, Shannon entropy, order statistics, stress-strength reliability and simulating the random sample are some of the statistical highlights of the BG distribution that are explored. The model's parameters are estimated using the maximum likelihood method. To examine the performance of the accuracy of point estimates for BG distribution parameters, the Monte Carlo simulation is performed with different scenarios. Finally, the BG distribution is fitted to two real lifetime count data sets from the medical field. As a result, the proposed BG distribution is an overdispersed right-skewed and can accommodate a constant hazard rate function. The proposed BG distribution is appropriate for modelling the overdispersed right-skewed real-life count data sets and it can be an alternative to the negative binomial and geometric distributions.
This paper introduces a new Estimator for multicollinearly matrix data and autocorrelated errors. We purpose two Stages Ridge Estimator(TR) for the multiple linear regression model, which suffers from both problems autocorrelation (AR(1)) and multicollinearity. After adjusting this with the ordinary ridge regression estimator (ORR), we use a mixed method to apply the two stages least squares procedure (TS). We also derive some statistical properties of this biased estimator and the paper is achieved by an application example.
In the process of building a linear regression model, the essential part is to identify influential observations. Various influence measures involving Cook's distance and DFFITS are designed to detect the linear regression's influential observations using the Least Squares (LS). The existence of influential observations in the data is complicated by the presence of severe collinearity and affects the efficiency of the detection measures. This paper proposes new diagnostic methods based on the Liu type estimator (LTE) defined by Liu [1] . The Cook's distance and DFFITS for the LTE are introduced. Moreover, approximate formulas for Cook's distance and DFFITS are also proposed for LTE. Two real data sets with a high level of multicollinearity among the explanatory variables as well as the simulation study are used to illustrate and evaluate performance of the methodologies presented in this paper.
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