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This paper analyzes the relationship between change in capital and change in risk for a sample of 23 Lebanese banks between 2009 and 2014. Using the simultaneous equations model, our evidence reveals that banks determine their capital and risk levels simultaneously, with a negative relationship. While banks adjust their capital rapidly, they adjust their risk slowly. Furthermore, undercapitalized banks adjust their capital levels more rapidly than well-capitalized banks, and they increase their capital when they decrease their risk. However, there is no conclusive evidence regarding the role of regulatory pressure in driving risk-taking behavior. Finally, there are no major differences in the relationships between capital and risk regardless of whether banks are listed or not.

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.