PurposeThis study investigates the impact of the Russia–Ukraine war (2022) on the volatility connectedness between Egyptian stock market sectors.Design/methodology/approachThis study employs the newest dynamic conditional correlation (DCC)-generalized autoregressive conditional heteroskedasticity (GARCH)-CONNECTEDNESS approach to examine volatility connectedness in a sample of ten sectors in the Egyptian stock market, namely banks, education, food, healthcare, industry, information technology, real estate, resources, transportation and travel, ranging from February 1, 2019 to May 31, 2022.FindingsThe findings show that connectedness among the Egyptian stock market sectors varies depending on the time. The average dynamic connectedness measure among sectors in Egypt is 73.24%. This average was 85.63% during the Russia–Ukraine War (2022). The author also shows that the transportation sector is the most significant net transmitter of volatility in the remaining sectors during the Russia–Ukraine War (2022).Practical implicationsThis study intends for policymakers to examine the co-movements, market variations and volatility spillover of stock markets, particularly during crises. Furthermore, the results help investors gain insight into diversifying the investors' portfolio assets to optimize profits.Originality/valueTo the best of the authors' knowledge, no study has investigated the implications of the war between Russia and Ukraine (2022) on sectoral interconnectedness within the stock markets in any country and discussion and empirical evidence from African countries are lacking. This study fills this gap in the literature. Additionally, the author uses the newest approach, the DCC-GARCH-CONNECTEDNESS approach, to describe the time-varying volatility spillover between economic sectors in Egypt.

PurposeThis paper investigates the impact of governance on economic growth, considering the spatial dependence between countries.Design/methodology/approachThe study employs spatial regression models to estimate the impact of governance on economic growth in a sample of 116 countries worldwide in 2017.FindingsThe findings imply that the influence of governance on economic growth is statistically significant. Moreover, if all other economic control variables are constant, 1% increase in governance raises the economic growth on average by 1% at 10%, 5% and 1% significance levels, respectively. Furthermore, each country's rise in economic growth favorably and substantially influences the economic growth of its bordering nations. The unobserved characteristics or similar unobserved environments in adjacent countries also affect its economic growth.Originality/valueThis study adds to the discussion and investigation of the influence of governance on economic growth by considering the spatial dependence between countries, which is lacking in the literature.

A new three-parameter cubic transmuted power distribution is proposed using the cubic rank transformation. The density and hazard functions of the new distribution provide great flexibility. Some mathematical properties of the new model such as quantile function, moments, dispersion index, mean residual life, and order statistics are derived. The model parameters are estimated using five different estimation methods. A comprehensive simulation study is carried out to understand the behavior of derived estimators and choose the best estimation method. The usefulness of the proposed distribution is illustrated using a real dataset. It is concluded that the proposed distribution is better than some well-known existing distributions.

In this work, we introduce a new G family with two-parameter called the compound reversed Rayleigh-G family. Several relevant mathematical and statistical properties are derived and analyzed. The new density can be heavy tail and right skewed with one peak, symmetric density, simple right skewed density with one peak, asymmetric right skewed with one peak and a heavy tail and right skewed with no peak. The new hazard function can be "upsidedown-constant", "constant", "increasing-constant", "revised J shape", "upside-down", "J shape" and "increasing". Many bivariate types have been also derived via di¤erent common copulas. The estimation of the model parameters is performed by maximum likelihood method. The usefulness and ‡exibility of the new family is illustrated by means of two real data sets.