Abstract: 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. PubDate: 2020-10-30

Abstract: 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. PubDate: 2020-10-20

Abstract: 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. PubDate: 2020-08-18

Abstract: 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. PubDate: 2020-07-20

Abstract: 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. PubDate: 2020-07-13

Abstract: 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. PubDate: 2020-07-03

Abstract: 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. PubDate: 2020-04-21

Abstract: 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. PubDate: 2020-02-29

Abstract: Abstract Early studies on financial distress prediction (FDP) seldom consider the problem of industry’s relative financial distress concept drift and neglects how to dynamically predict industry’s relative financial distress. This paper proposes a novel model for dynamic prediction of relative financial distress based on imbalanced data stream of certain industry, and the whole model is divided into the three submodules: the financial feature selection module based on plus-L-minus-R approach, the financial condition evaluation module based on principal component analysis, and the FDP modeling module based on SMOTEBoost-SVM/DT/KNN/Logistic. After feature selection, the results of industry financial condition evaluation are used as class labels for industry’s relative FDP modeling, and the model keeps updating with time window sliding on. The empirical experiment is carried out based on the financial ratio data of Chinese iron and steel companies listed in Shanghai and Shenzhen Stock Exchange, and the results indicate the effectiveness of the dynamic model for industry’s relative FDP. PubDate: 2019-12-01

Abstract: 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. PubDate: 2019-11-16

Abstract: 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. PubDate: 2019-09-24

Abstract: 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. PubDate: 2019-08-31

Abstract: 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. PubDate: 2019-08-29

Abstract: Unfortunately, an incorrect grant number has been published. Please find the correct grant number here: [University of Economics, Prague] under Grant [IGA F4/66/2019] PubDate: 2019-04-16

Abstract: Abstract This article proposes a pricing model for space weather derivatives with payout depending on solar activity. By measuring the disturbance of the Earth’s magnetosphere, it is possible to price space weather derivatives which trigger a payoff if a certain level of energization is reached. Since energetic particles emitted by the Sun are a non-tradeable quantity, unique prices of contracts in an incomplete market are obtained using inverse transformation sampling as well as the market price of risk. We find a step-wise decline of option prices with increasing barriers of Kp-index values, a dependence of the option prices on the sunspot cycle, as well as reduced sensitivity of longer-dated maturities for higher Kp-index values. PubDate: 2019-04-09

Abstract: Abstract The paper discusses the uncertainty resulting from vagueness. Within this topic we present an original version of the fuzzy approach to a foreign investment risk estimation based on values of rating indices. The transition from the basic point values of rating indices into the linguistic values within intervals of linguistic variables of fuzzy logic enables us to take into account the diverse kinds of uncertainty. The theoretical and methodological part submits fundamentals of the general fuzzy model of the vaguely defined problem, which is applied to the problem of the foreign investment “risk” estimation of selected countries of Europe and Asia. The inclusion of a country into one of the categories (“high risk countries”, “conditional risk countries” and “non-risk countries”) is based on a vector of value indexes of the sub-components of business environment quality (corrupt environment, economic stability and political stability). PubDate: 2019-03-19 DOI: 10.1057/s41283-019-00051-1

Authors:Rubén Chavarín Abstract: 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. PubDate: 2019-02-25 DOI: 10.1057/s41283-019-00049-9

Authors:Xu Guo; Cuizhen Niu; Wing-Keung Wong Abstract: Abstract Farinelli and Tibiletti (F–T) ratio, a general risk-reward performance measurement ratio, is popular due to its simplicity and yet generality that both Omega ratio and upside potential ratio are its special cases. The F–T ratios are ratios of average gains to average losses with respect to a target, each raised by a power index, p and q. In this paper, we establish the consistency of F–T ratios with any nonnegative values p and q with respect to first-order stochastic dominance. Second-order stochastic dominance does not lead to F–T ratios with any nonnegative values p and q, but can lead to F–T dominance with any \(p<1\) and \(q\ge 1\) . Furthermore, higher-order stochastic dominance ( \(n\ge 3\) ) leads to F–T dominance with any \(p<1\) and \(q\ge n-1\) . We also find that when the variables being compared belong to the same location-scale family or the same linear combination of location-scale families, we can get the necessary relationship between the stochastic dominance with the F–T ratio after imposing some conditions on the means. There are many advantages of using the F–T ratio over other measures, and academics and practitioners can benefit by using the theory we developed in this paper. For example, the F–T ratio can be used to detect whether there is any arbitrage opportunity in the market, whether there is any anomaly in the market, whether the market is efficient, whether there is any preference of any higher-order moment in the market, and whether there is any higher-order stochastic dominance in the market. Thus, our findings enable academics and practitioners to draw better decision in their analysis. PubDate: 2019-02-20 DOI: 10.1057/s41283-019-00050-2

Authors:Shumaila Zeb; Abdul Rashid Abstract: Abstract The aim of this paper is twofold. First, it measures the systemic risk contribution of banks, financial services, and insurance firms of each of BRICS member country for the period 2000–2015. Second, it empirically examines how firm-specific factors determine systemic risk in financial institutions of BRICS countries. To carry out the empirical analysis, the unbalanced firm-level data are used. To gauge the systemic risk of banks, financial services, and insurance firms, the Delta Conditional Value-at-Risk (∆CoVaR) methodology is applied. The panel regression approach is used to examine how firm-specific variables determine the level of systemic risk in different financial institutions of BRICS countries. The empirical findings suggest that the size of institution, the tier 1 ratio, the liquidity ratio, the operating profit margin ratio, and the market-to-book value ratio statistically significantly determine systemic risk in BRICS countries. The results are significant in devising financial regulations to decrease the influence of systemic risk factors in the respective economies. PubDate: 2019-01-21 DOI: 10.1057/s41283-018-00048-2

Authors:Emilio Cardona; Andrés Mora-Valencia; Daniel Velásquez-Gaviria Abstract: Abstract In a recent paper, Acerbi and Székely (Risk Magazine, 76–81, 2014) presented three methods to test expected shortfall, and this is the first empirical application of that paper on emerging markets. We employ daily stock index returns from the Morgan Stanley Capital International Inc. Emerging Markets Index covering the 2000–2015 period, extending Acerbi and Székely (Risk Magazine, 76–81, 2014) results to derive the significance thresholds for the Student’s skewed-t distribution using two testing methods. We find that the thresholds for the Z1 Test and Z2 Test for skewed-t distribution are similar to the values obtained by Acerbi and Székely for Student’s t distribution. Therefore, the Z1 and Z2 thresholds are invariant to the skewed-t-shaped parameter values found in the emerging market stock indices. Empirical results show outperformance of Student’s skewed-t and Student’s t distributions over Gaussian distribution. PubDate: 2018-09-10 DOI: 10.1057/s41283-018-0046-z