Suchergebnisse

Purpose – This article examines volatility spillovers, cross-market correlation, and comovements between selected developed and former communist emerging stock markets in the European Union. Modelling the behavioural dynamics of European stock markets represents a vital topic in a fascinating context, but also a current challenge of great interest. Research Methodology – We propose to estimate and model volatility using GARCH family models for selected European markets. We aim to explore volatility movement, presence of leverage effect/ asymmetry in selected financial markets. Findings – The econometric approach includes GARCH (1, 1) models for the sample period from 1, January 2000 to 12, July 2018. The empirical results revealed that exists a significant presence of volatility clustering in all selected financial markets except Poland and Croatia. The empirical analysis also indicates that both recent and past news generate a considerable impact on present volatility. Research limitations – Our empirical study has certain limitations regarding the relatively small number of only eight stock markets. Practical implications – It can provide a useful perspective for researchers, academics, investors, investment managers, decision-makers, and scientists. Originality/Value – The empirical analysis is focused on 8 European stock markets, which are classified as developed (Spain, UK, Germany, and France) and emerging (Poland, Hungary, Croatia, and Romania).

This paper estimates NIFTY index from Indian stock market by considering a cluster of MSCI European, Middle East and Asian stock market indices. In the forecasting process, we obtain group of independent variables to test its relative impact over dependent variable (NIFTY) considering a sample size of daily observations from January 2000 to December 2021 abstracted from Bloomberg. We run OLS regression, Quantile estimations with additional parameter of VIF and BKW. We found significant impact association with China (Asian index) and Saudi Arabia (Middle East index) during the forecasting process compared to rest of sample indices that exceed unexpectedly out of VIF limits. Further, we recorded strong association of independent variables despite of statistical significance (<1%) in OLS regression estimation.

Technology, along with political and economic factors, is one of the main drivers of the future of banking. Banking managers urgently need to know technological trends to make strategic decisions, know the future accurately, and make the most of existing opportunities. Industry 5.0 is the dream of modern banking, based on strategies for successful entry into the field in a completely different way. Using a complex literature survey, 49 indicators were identified to enter Industry 5.0 and were classified into three categories of insignificant indicators, essential indicators, and very necessary indicators. Then, based on the opinions of 10 experts from ten countries with modern banking in the world, the researchers focused on 14 essential indicators. To analyze the drawn space, structural-interpretive modeling and MICMAC analysis were used and the model was classified into nine levels. The results showed that low-level indices are the most influential (TMBE and HEMS) and higher-level indices are the most influenced (PZM and RNC). Finally, researchers analyzed how to use new technologies in the banking industry with the entry of the Industry 5.0 and revealed what the characteristics of the impact of these indicators on entering Industry 5.0 are.