This study attempts to examine the existence of interdependencies between specific stock market indices, exchange rates and crude oil for the period January 2021 to July 2022 with daily data. In the period we have chosen, the post-vaccination phase against COVID-19, as well as the war in Ukraine, is covered. The variables selected for this study are RTSI, Eurostoxx, S&P 500, EUR/USD and RUB/USD exchange rates and crude oil prices. The selection of the specific variables was made because they are directly related to the pre-war period that coincides with the post-vaccine period of the pandemic, which allowed us to characterize it as the normal period and to characterize the period of the war in Ukraine that coincides with the energy crisis as the unstable period. In this way, the present study covers the markets of Russia and other developed economies. For empirical purposes, we applied a wavelet coherence approach in order to investigate the possible existence of simultaneous coherence between the variables at different times and scales for all the considered times. The findings of the study reveal the existence of strong correlations between all variables, during different time periods and for different frequencies during the period under review. Of particular interest is the finding that shows that during the crisis period, the RTSI significantly affects both the European and American stock markets, while also determining the evolution of the Russian currency. In addition, it appears that capital constraints in the Russian stock market, combined with increased demand for crude oil, determine the interdependence between RTSI and crude oil. Finally, an interesting finding of the study is the existence of a negative correlation between the US stock index and crude oil in low-frequency bands and the RTSI and Eurostoxx with crude oil for the post-vaccination and pre-war periods in the medium term. These findings can be used by both investors and portfolio managers to hedge risks and make more confident investment decisions. In addition, these findings can be used by policy makers in the planning of regulatory policies regarding the limitations of the systemic risks in capital markets.

This paper investigates the interrelations between stock returns and crude oil prices for European oil-importing/exporting countries. A vector autoregression (VAR) model is applied to estimate the significance of stock market responses to changes in oil prices during the pandemic period 2019–2021. A Granger causality test is applied to find the direction and the intensity of the relation between crude oil and the indices of the European stock markets. The findings of this paper hold with or without the COVID-19 pandemic episode and reveal the interaction between the European stock markets and the crude oil prices. The results indicate that in steady periods, before the COVID-19 outbreak and after the announcement of vaccinations, there is no interdependence between crude oil and stock prices, whereas in high volatility periods, the causality from stock markets to oil prices increases and both oil-exporting and -importing countries are equally influenced. These findings have implications both for investors and fund managers.

  Although in literature numerous multivariate models have been applied for optimal portfolio selection based on either market or accounting stock characteristics, whereby plenty of technical and/or fundamental criteria have been proposed, the problem is yet to be solved. This paper enhances a two-phased analysis that combines both fundamental and technical criteria, to overcome the aforementioned shortcomings. Initially, the fundamental characteristics of 25 stocks from 11 industries/sectors with the largest market capitalization, are compared to the performance of the Athens Stock Exchange FTSE/XA Large CAP Index, and hence two scoring tables are formed using different benchmarks, where the best performing stocks are selected. Subsequently, for these stocks, based on weekly data covering a 3-year period, the Sharpe Index Model is applied, and the best performing portfolio is selected. The estimated Sharpe Index Model (SIM) reveals that there are several opportunities to optimize return and diversify risk in an efficient manner, outperforming the FTSE Large Cap Index. JEL Classifications: D53, G11, G12, G17, G23 Key words: Risk & Return, Technical Analysis, Fundamental Analysis, Sharpe Index Model