Showing papers in "Risk Management in 2020"

An integrated plithogenic MCDM approach for financial performance evaluation of manufacturing industries

Abstract: Financial performance evaluation is very significant for manufacturing industries in a competitive environment to achieve investment goals, especially increasing revenue. Financial performance measures must be identified accurately, because the evaluation process reflects the effectiveness of a company. The purpose of this article is to present a plithogenic multi-criteria decision-making (MCDM) model based on neutrosophic analytic hierarchy process (AHP), Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method, and Technique in Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The financial performance in this study is measured by a set of financial ratios. To examine the proposed model, the top 10 steel companies in Egypt are evaluated based on specified financial ratios. According to steel manufacturing experts, the weight of the criteria is determined using AHP method. The company ranking is determined using VIKOR and TOPSIS comparatively. The results show that the obtained ranks of the companies by these methods are almost the same.

Cybersecurity hazards and financial system vulnerability: a synthesis of literature

Abstract: In this paper, we provide a systematic review of the growing body of literature exploring the issues related to pervasive effects of cybersecurity risk on the financial system. As the cybersecurity risk has appeared as a significant threat to the financial sector, researchers and analysts are trying to understand this problem from different perspectives. There are plenty of documents providing conceptual discussions, technical analysis, and survey results, but empirical studies based on real data are yet limited. Besides, the international and national regulatory bodies suggest guidelines to help banks and financial institutions managing cyber risk exposure. In this paper, we synthesize relevant articles and policy documents on cybersecurity risk, focusing on the dimensions detrimental to the banking system’s vulnerability. Finally, we propose five new research avenues for consideration that may enhance our knowledge of cybersecurity risk and help practitioners develop a better cyber risk management framework.

New development on the third-order stochastic dominance for risk-averse and risk-seeking investors with application in risk management

Abstract: This paper develops new financial theory to link the third-order stochastic dominance (TSD) for risk-averse and risk-seeking investors and provide illustration of application in risk management. We present some interesting new properties of TSD for risk-averse and risk-seeking investors. We show that the means of the assets being compared should be included in the definition of TSD for both investor types. We also derive the conditions on the variance order of two assets with equal means for both investor types and extend the second-order SD reversal result of Levy and Levy (Manag Sci 48(10):1334–1349, 2002) to TSD. We apply our results to analyze the investment behaviors on traditional stocks and internet stocks for both risk averters and risk seekers.

Measuring the contribution of Chinese financial institutions to systemic risk: an extended asymmetric CoVaR approach

Abstract: This study proposes an extension of the Asymmetric CoVaR method in Espinosa et al. (J Bank Finance 58: 471–485, 2015) to capture the time-varying asymmetric responses of the financial system to positive and negative shocks to individual institutions. Building on the extended method and considering a set of Chinese financial institutions, we assess the extent to which distress within different institutions contribute to systemic risk. To provide a formal ranking of risk contributions, we implement the significance and dominance tests with bootstrap Kolmogorov–Smirnov statistics. The estimates of the extended Asymmetric CoVaR method reveal an asymmetric pattern that characterizes the tail interdependence in the Chinese financial system and this pattern changes dynamically over time. Particularly, the impact on the system of a fall in individual market value is only slightly larger than that of an increase during tranquil years. However, the entire system becomes extremely sensitive to downside losses than to upside gains during crises. The result also raises concern about privately owned banks in that they are systemically riskier than state-owned banks and other institutions. Using panel regressions, we also find firm characteristics such as institution size and volatility are important predictors of systemic risk contribution.

China’s growing influence and risk in Asia–Pacific stock markets: evidence from spillover effects and market integration

Abstract: This study examines China’s influence in the Asia–Pacific stock markets by focusing on spillover effects and market integration and employs how the financial crises and financial liberalization affect the relationship among these markets. Based on the series of studies of Diebold and Yilmaz (2009, 2012, 2015), this study employs the generalized vector autoregressive framework to examine the spillover effects among the main Asia–Pacific stock markets. The multifactor R-squared measure proposed by Pukthuanthong and Roll (2009) is employed to examine the market integration of Chinese stock market. The results indicate that spillover effects and market integration tend to increase, indicating that China stock market is playing a more important role in the Asia–Pacific stock markets. This study provides more evidence that financial crises and financial liberalization can strengthen spillover effects and market integration.

Which interbank net is the safest

Abstract: In this paper, we aim at establishing some clear guidelines on which configuration of the interbank net can be most effective in limiting the banks’ default contagion risk. More specifically, based on real banks’ balance sheet data, we analyzed how the exposure concentration on specific counterparts can limit or enhance contagion, and which characteristics (variables) of the counterparts induce these differences. The analysis performed here is based on interbank exposures data, which only represent one of the contagion channels, but the same perspective can be generalized when considering, instead of the direct interbank exposures, the asymmetrical effects of a systemic crisis on the considered bank soundness (similar to what happens for the effect of interbank credit losses on a specific bank), or of the considered bank crisis to the whole system’s soundness (similar to the case of interbank default of the considered bank). Moreover, the simulation model as it is can be applied to both listed and nonlisted banks, since it is based purely on balance sheet data. Results suggest that, if we consider the whole interbank market, a high concentration of exposures can enhance contagion, and that, with reference to specific bank-to-bank exposures, the case in which small banks lend to larger and riskier banks is the most threatening for the system’s stability. These results can help regulators and supervisors keep the banking and financial system safe.

Risk governance, banks affiliated to business groups, and foreign ownership

Abstract: The aim of the present work is to study the effect of risk governance on the profitability of a sample of listed banks in Mexico during the period 2007–2015. The evidence presented here shows that functions of risk governance have an impact of only slight significance on the profitability of banks, which suggests that the dispositions and recommendations for risk governance are only fulfilled in a limited way. One possible explanation for this finding is related to patterns of ownership structure, due to the presence of banks linked to business groups, that give risk management a secondary role while other objectives are given greater emphasis. However, in foreign-owned banks also there were no patterns very different from the previous ones. The results suggest that the effective size of the risk committee and the independence of the chair of this committee are the only relevant risk governance mechanisms in commercial banks established in Mexico.

Another look at the implied and realised volatility relation: a copula-based approach

Abstract: The main aim of this paper is to obtain a direct measure of the relation between the future and implied volatilities, in order to determine the appropriateness of using linear modelling to establish the implied–realised volatility relation. To achieve this aim, the dependence structure for implied and realised volatilities is modelled using bivariate standard copulas. Dependence parameters are estimated using a semiparametric method and by reference to three databases corresponding to different assets and frequencies. Two of these databases have been employed in previous research, and the third was constructed specifically for the present study. The first two databases span periods of major crises during the 1980s and 1990s, while the third contains data corresponding to the 2007 financial and economic crisis. The empirical evidence obtained shows that the dependence coefficient is always positive and constant over time, as expected. However, the influence of extreme-volatility events should be taken into account when the data present significant asymmetric tail dependence; models that impose symmetry underestimate the conditional expectation in extreme tail events. Therefore, it might be preferable to model nonlinear conditional expectations to forecast the realised volatility, using implied volatility as a predictor, as is the case with copula models and neural networks.

Geopolitical Risk Revealed in International Investment and World Trade

Abstract: We construct a metric of geoeconomical distance from international investment and world trade to assess the risk of interstate conflict. Geoeconomical distance measures the mutual excessive acceptance of investments and exports between two countries. We show that a country’s distance to the US and China is a significant predictor of its involvement into interstate conflict(s). Splitting the sample into two periods, we find that countries approaching China at the cost of their proximity to the US are confronted with significantly higher risk of interstate conflict in the early post-Cold War period, as are countries located at the outskirts of the unipolar system as measured by their distance from the US. In the later period, a country’s concurrent motion toward China and away from the US, unlike the earlier period, does not increase its risk to be involved in interstate conflict(s). Furthermore, countries located at the periphery of the nascent Sino-US bipolar system have lower geopolitical risk than those residing close to their respective centers.

Comparison study of two-step LGD estimation model with probability machines

Abstract: Accurate estimation of loss given default is necessary to estimating credit risk. Due to the bi-modal nature of LGD, the two-step LGD estimation model is a promising method for LGD estimation. This study improves the first model in the two-step LGD estimation model using probability machines (random forest, k-nearest neighbors, bagged nearest neighbors, and support vector machines). Furthermore, we compare the predictive performance of each model with traditional logistic regression models. This study confirms that random forest is the best model for developing the first model in the two-step LGD estimation model.

Research on RMB exchange rate forecast based on the neural network model and the Nelson–Siegel model

Abstract: This paper expands the neural network model to predict exchange rate based on the factors extracted from the Nelson–Siegel model. Based on the theory about exchange rate forecasting, interest could be used to predict the movement of exchange rate. Therefore, this paper analyzes the interest rate term structure factors based on the US and China yield curves data, then uses the Nelson–Siegel model to extract the factors of the interest rate term structure. Finally, the factors of yield curves are used as input data to of the neural network model. And the mean forecasting squared errors, mean absolute errors, mean absolute percentage errors of neural network model, Nelson–Siegel regression model, and ARIMA model are compared. The results show that the neural network model has a superior ability to explain the exchange rate fluctuations of the CNY and USD, and the prediction ability is better than the exchange rate prediction ability of the Nelson–Siegel regression model and ARIMA model.

Liability-driven investments of life insurers under investment credit risk

Abstract: In this paper, we present a model of liability-driven investments for life insurers by assuming that equity portfolios can be wiped out by catastrophic default risk of the firms whose stock the life insurer holds. A model of trinomial defaultable asset trees is used and it is calibrated to market data, while a stochastic programming model is set up to solve for the optimal asset allocation strategy of the life insurer to ensure maximization of assets while keeping solvency at a specific confidence level. We find relatively invariant allocations with changes to default correlation, while we find that previous models without taking credit risk explicitly into account require very high volatility parameters to reproduce allocations similar to those of the model with credit risk.

Singular spectrum analysis for modelling the hard-to-model risk factors

Abstract: The modelling of the hard-to-model risks factors is one of the topics of great interest to the financial industry The industry is spending lots of resources on efforts to account for the hard-to-model risks in their risk management frameworks Currently, the concept describing these risks is the Risk Not in VaR In its turn, the newly composed Fundamental Review of the Trading Book text similarly prescribes to classify risk factors that do not have a history of continuously available real prices as non-modellable risk factors Both entities and financial regulatory authorities have shown great concern in the search for efficient techniques and models that allow for a more accurate estimation of the risks factors linked to the derivatives An accurate modelling of these risk factors can lead to considerable optimization in the capital charges, but any model assumption must be duly justified and supported by the entities In this paper, the (Multichannel) Singular Spectrum Analysis for modelling these risk factors is analysed