The Asymmetric Impact of Funding Liquidity Risk on the Volatility of Stock Portfolios during the COVID-19 Crisis

In this study, we identify economic transmission channels through which changes in funding liquidity conditions in interbank markets asymmetrically affect volatilities of stock portfolios during the COVID-19 crisis. For the purpose of this study, the quantile regression approach is utilized. Controlling for macroeconomic factors, we document that volatilities of high-risk portfolios increase more in response to a deterioration in funding liquidity conditions compared to less risky portfolios. More importantly, this increase intensifies in high-volatility periods of high-risk portfolios, which implies the impact is stronger during uncertain economic environments, such as the one caused by the COVID-19 outbreak.

Computing a Complex Network Hierarchical Structure for Financial Market Networks on the Basis of the Hybrid Heuristic Algorithm

The recent empirical studies showed that money center networks in interbank markets are more robust and stable. Therefore, the research on layered financial networks is a key part of the systemic risk management. Various methods have been proposed in prior studies to find optimal partitioning of interbank networks into core and periphery subsets. However, these methods that have been adopted with approximation methods, in general, do not guarantee optimal bipartition. In this paper, a genetic simulated annealing algorithm is presented to detect a hierarchical structure in interbank networks as a hybrid heuristic algorithm, while its effects are also analyzed. The optimization of the error score for the core-periphery model is mathematically developed firstly as an improved expression of the optimization function, which incorporates the genetic algorithm into a simulated annealing algorithm to guarantee the optimal bipartition and to jump from a local optimization. The results of this algorithm are finally verified by empirical analysis of interbank networks; and, through the immunity strategy under the risk diffusion model, the significance of core-periphery structure to risk management is verified.

On the systemic risk and optimality in non-homogenous innovation clusters

Purpose – to analyse the concept of systemic risk of innovation cluster and show its impact on the optimality of cluster performance as well as to cluster structure. Research methodology – general overview of research papers and documents presenting concepts and methodologies of evaluation of systemic risk and performance of networked structures as interbank markets and business clusters with regard to asymmetric information, applied research. Findings – determination of systemic risk in a networked structure that appears together with synergistic effect as a result of collaboration in a networked structure. The clique-based structure appears to be more favourable for innovation cluster performance due to optimal sharing of information and systemic risk. The interpretation of the model of evaluation of systemic risk can be at least twofold: core-periphery, business entities-R&D institutions, etc. Research limitations – lack of empirical data that cannot be used to implement empirical research on a problem of systemic risk and its modelling. Practical implications – the conceptual model of evaluation of systemic risk should be useful for understanding and further treatment of measuring risk in a case of innovation management. Originality/Value – in this paper, the model of evaluation of systemic risk in innovation cluster and its interpretations are provided. The systemic risk is treated as a generalized risk impacting directly or non-directly the performance of an innovation cluster.