A differential evolution copula-based approach for a multi-period cryptocurrency portfolio optimization

Recent years have seen a growing interest among investors in the new technology of blockchain and cryptocurrencies and some early investors in this new type of digital assets have made significant gains. The heuristic algorithm, differential evolution, has been advocated as a powerful tool in portfolio optimization. We propose in this study two new approaches derived from the traditional differential evolution (DE) method: the GARCH-differential evolution (GARCH-DE) and the GARCH-differential evolution t-copula (GARCH-DE-t-copula). We then contrast these two models with DE (benchmark) in single and multi-period optimizations on a portfolio consisting of five cryptoassets under the coherent risk measure CVaR constraint. Our analysis shows that the GARCH-DE-t-copula outperforms the DE and GARCH-DE approaches in both single- and multi-period frameworks. For these notoriously volatile assets, the GARCH-DE-t-copula has shown risk-control ability, hereby confirming the ability of t-copula to capture the dependence structure in the fat tail.     

A Spatial Analysis of Property Crime Rates in South Africa

The main objective of this paper is to examine the relationship between two types of property-related crime and some socio-economic, demographic and deterrence factors. It employs the conditional autoregressive specification to account for spatial autocorrelation that characterises property-related crime data, which are aggregated up to the level of municipality. First, the analysis confirms the presence of spatial autocorrelation in the data, which means that neighbouring municipalities exhibit similar levels of property crime rates. In addition, and most importantly, empirical findings show that internal migration, youth and education are important predictors of property-related crime across municipalities in South Africa for the period under consideration.     

Common risk factors in returns in Asian emerging stock markets

This paper examines the application of the Fama and French's (1993) three-factor model in three Asian emerging markets (Hong Kong, Singapore and Taiwan). The empirical evidence is consistent with the US findings that the model can explain most of the variations in average returns. However, we find that the main contributing factor is the contemporaneous market excess returns. The impact of the size effect and book-to-market (BE/ME) factor is limited and in some cases insignificant. When the three-factor model is modified by using lagged market excess returns instead in order to check for the predictability of the market factor, the explanatory power of the model drops substantially but both the risk factors for size and BE/ME are now able to contribute significantly in explaining the cross-sectional variations of stock returns. Their explanatory powers are strongest for small-size with high BE/ME portfolios. The robustness of our results is also checked for the separation of up and down markets periods and January effect.     

Do business strategies vary across firms in the banking industry? New perspectives from the bank size–profitability nexus

This study examines the roles of interdependence and policy variations across firms in the causality between bank size and profitability in Nigeria, using second-generation estimators and the Dumitrescu–Hurlin panel Granger non-causality test. The findings support different business strategies and policy variances across banks. Causality is found non-existent in the cases of 11 banks. A unidirectional causality from size to profitability is established in two banks while evidence of a unidirectional causality is established from profitability to bank size in the other two banks. This study concludes that cross-sectional dependence and policy variations across firms matter in the bank size–profitability nexus.