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This paper analyzes the volatility dynamics in the financial markets of the (three) most powerful countries from a military perspective, namely, the U.S., Russia, and China, during the period 2015&ndash, 2018 that corresponds to their intervention in the Syrian war. As far as we know, there is no literature studying this topic during such an important distress period, which has had very serious economic, social, and humanitarian consequences. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH (1, 1)) model yielded the best volatility results for the in-sample period. The weighted historical simulation produced an accurate value at risk (VaR) for a period of one month at the three considered confidence levels. For the out-of-sample period, the Monte Carlo simulation method, based on student t-copula and peaks-over-threshold (POT) extreme value theory (EVT) under the Gaussian kernel and the generalized Pareto (GP) distribution, overstated the risk for the three countries. The comparison of the POT-EVT VaR of the three countries to a portfolio of stock indices pertaining to non-military countries, namely Finland, Sweden, and Ecuador, for the same out-of-sample period, revealed that the intervention in the Syrian war may be one of the pertinent reasons that significantly affected the volatility of the stock markets of the three most powerful military countries. This paper is of great interest for policy makers, central bank leaders, participants involved in these markets, and all practitioners given the economic and financial consequences derived from such dynamics.

This paper examines the behavior of Bitcoin and Ripple compared to the three fiat currencies, EURUSD, GBPUSD and CNYUSD, by comparing their volatility and VaR during the period extending from March 01, 2016 to February 28, 2019. EWMA, GARCH (1, 1), GARCH (p, q) and EGARCH (1, 1) were used to forecast volatilities. EWMA model outperformed the rest of the models for all of the selected fiat and cryptocurrencies during the in-sample period and for EURUSD, GBPUSD and Bitcoin during the out-of-sample period. GARCH (p, q) was the optimal model for the CNYUSD and Ripple in the out-of-sample period. Bitcoin and Ripple exhibit an asymmetry in their volatility which is significantly higher than all the volatilities of the studied currencies. When estimated volatilities were compared to the implied volatility, the GARCH (1, 1), GARCH (6, 6) and EWMA were the optimal models for the EURUSD, CNYUSD and GBPUSD, respectively for the in-sample period. VaR results were accepted for the EURUSD, GBPUSD and Bitcoin at all confidence levels. For the CNYUSD, VaR measures underestimated the risk at the 99% confidence level unlike Ripple’s VaR that was accepted at the 90% and 99% confidence levels. Our results suggest that Bitcoin and generally the cryptocurrencies market cannot act as alternatives to fiat currencies at the moment.

This research aims at investigating the factors determining the dividend payout policy in the Lebanese banks listed on the Beirut Stock Exchange. This study considers the impact of seven variables, namely, profitability, liquidity, leverage, firm size, growth, firm risk and previous year’s dividend payout on the dividend payout ratios by using an unbalanced panel dataset of listed banks between the years of 2005 and 2011. Two models were tested using the OLS and the dynamic panel regressions. Empirical results show that the dividend payout policies are positively affected by the firm size, risk and previous year’s dividends, but are negatively affected by the opportunity growth and profitability. The results obtained might indicate that firms pay dividends with the intention of reducing the agency conflicts. Furthermore, managers take into consideration the stability of dividends while determining the dividend policy. Moreover, the results suggest that the Lebanese listed firms prefer to invest their earnings to grow rather than to pay more dividends.

Given the fact that the government debt is financed by commercial banks in Lebanon, there is a need to uncover and study the determinants of commercial bank deposits. This paper investigates empirically the main determinants of non-residents deposits in Lebanese commercial banks using monthly time series data covering January 2002 to January 2013 (131 observations). The necessary tests were performed so that the ordinary least square regression can be safely applied. The estimated model had non-resident deposits as the dependent variables, and the explanatory variables as internal factors, external variables, and bank specific variables. The dependent variable was measured in three ways: in the local currency, in foreign currency, and total non-resident deposits. The results show that non-residents’ deposits are shaped differently between domestic and foreign currency. For instance, bank assets, interest rates, and some adverse political situations affect non-resident deposits in all its measures. However, while total non-resident deposits and foreign non-residents deposits are roughly affected by the same factors, local resident deposits seem to be affected by other factors; this fact is attributed to the fact that local currency deposits account for a small percentage of total non-resident deposits. Based on the results, several policy implications were drawn that aim at increasing non-resident deposits. First, the stability of macroeconomic system should be maintained. Second, the government should maintain healthy rate differentials to support the deposit growth. Lastly, Lebanese banks should search for internalization to diversify their losses.

This paper applies the multi-index model to explore the sensitivity of the stock return of European automotive companies to some macroeconomic variables. The analysis involves the monthly returns of nine European companies operating in the automotive industry from January 2003 to April 2012 using 15 macroeconomic variables and the market index represented by S&P 350. The study finds that the S&P 350 positively affects stock returns, supporting the single index model. Furthermore, exchange rate, exports and platinum positively affect stock return, while aluminium and unemployment rate negatively affect stock return. The results suggest that a multi-index model using selected macroeconomics variables provides additional power in explaining the variability of the European stock returns in the automobile industry over a single index model using the market index alone.