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Showing 1 - 27 of 27 Journals sorted alphabetically
Annals of Actuarial Science     Full-text available via subscription   (Followers: 2)
Asia-Pacific Journal of Risk and Insurance     Hybrid Journal   (Followers: 7)
Assurances et gestion des risques     Full-text available via subscription  
Astin Bulletin     Full-text available via subscription   (Followers: 1)
Banks in Insurance Report     Hybrid Journal   (Followers: 1)
Blätter der DGVFM     Hybrid Journal   (Followers: 2)
British Actuarial Journal     Full-text available via subscription   (Followers: 1)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Geneva Risk and Insurance Review     Hybrid Journal   (Followers: 7)
Health Affairs     Full-text available via subscription   (Followers: 80)
Insurance Markets and Companies     Open Access  
Insurance: Mathematics and Economics     Hybrid Journal   (Followers: 10)
International Journal of Business Continuity and Risk Management     Hybrid Journal   (Followers: 17)
International Journal of Forensic Engineering     Hybrid Journal   (Followers: 3)
International Journal of Forensic Engineering and Management     Hybrid Journal   (Followers: 3)
International Journal of Health Economics and Management     Hybrid Journal   (Followers: 13)
International Social Security Review     Hybrid Journal   (Followers: 8)
Journal for Labour Market Research     Open Access   (Followers: 10)
Journal of Derivatives & Hedge Funds     Hybrid Journal   (Followers: 9)
Journal of Risk and Insurance     Hybrid Journal   (Followers: 17)
Journal of Risk Finance     Hybrid Journal   (Followers: 6)
Risk Management     Hybrid Journal   (Followers: 15)
Risk Management & Insurance Review     Hybrid Journal   (Followers: 10)
Scandinavian Actuarial Journal     Hybrid Journal   (Followers: 2)
SourceOECD Finance & Investment/Insurance & Pensions     Full-text available via subscription   (Followers: 3)
The Geneva Reports     Free   (Followers: 2)
Zeitschrift für die gesamte Versicherungswissenschaft     Hybrid Journal   (Followers: 1)
Similar Journals
Journal Cover
Risk Management
Journal Prestige (SJR): 0.189
Citation Impact (citeScore): 1
Number of Followers: 15  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1460-3799 - ISSN (Online) 1743-4637
Published by Springer-Verlag Homepage  [2656 journals]
  • Covid-19 and high-yield emerging market bonds: insights for liquidity risk
    • Abstract: Around the apogee of the pandemic crisis in late March 2020, trading liquidity has evaporated out of high-yield (HY) bond markets across developing states. Concerned about this phenomenon, we assess emerging market (EM) debt liquidity as a combination of three metrics: (i) bid–ask spreads; (ii) relative liquidity score incorporating market depth, trading volumes, and time needed to liquidate an asset; and (iii) round-trip transaction costs—evidencing that all have worsened by the end of the first quarter of 2020. We complement our analysis by tracking the dynamics of the option-adjusted spreads of the EM HY bonds and document that the recovery trends of the credit and liquidity components in bonds spreads have decoupled in the aftermath of the Covid-triggered global meltdown. We evidence relevant differences in bond liquidity between chosen countries, representative of geopolitical regions. All the considered liquidity measures provide a coherent picture of the pandemic impact and allow for insights regarding the recovery from the crisis turmoil and the risk management of the EM HY bond portfolios throughout a systemic crisis.
      PubDate: 2021-04-30
  • Are stock prices driven by expected growth rather than discount rates'
           Evidence based on the COVID-19 crisis
    • Abstract: We use the Gordon (Rev Econ Stat 41(2):99-105, 1959) constant growth model to gauge the effects from innovations in implied growth versus discount rates. During the COVID-19 downturn and the Global Financial Crisis (GFC), stock returns were largely affected by a change in the long-run implied growth rate and only to a lesser extent by a change in discount rate, the latter typically used to explain stock returns in the classical asset pricing literature. We reach this conclusion by using ordinary least-squares (OLS) regressions of stock returns on the unobservable Gordon factors, which we estimate from firm-level valuation ratios D/P, P/E, and P/B. The effects from a decrease in implied growth outweigh those from an increase in discount rate by a factor of approximately 1.6 to 1.7. Also, firms with a decrease in implied growth show a stock return that is approximately 6.6% more negative than that of firms with no decrease in implied growth. Investors can infer valuable information from the joint interpretation of underlying market fundamentals as derived from the Gordon model.
      PubDate: 2021-04-14
  • Do risk management committee characteristics influence the market value of
    • Abstract: This study aims to examine the effects of the risk management committee’s characteristics on the market performance of non-financial listed firms in Malaysia between 2015 and 2017. The regression result shows that risk management committee (RMC) size, independence, expertise and female RMC members have a substantial negative influence on firm performance. By employing a different measurement of expertise, further analysis shows that RMC members with risk management expertise have a significantly positive relationship with firm performance. The results suggest that RMC members with specific risk management expertise can promote efficient risk monitoring, thus, enhancing the value of firms compared to RMC members with only general financial and accounting backgrounds. Nevertheless, the results are the same for the female members of the RMC even though different measurements are used. The robust negative result is supported by the tokenism interpretation for female members in the risk management committee.
      PubDate: 2021-04-11
  • Determinants of corporate exposure at default under distressed economic
           and financial conditions in a developing economy: the case of Zimbabwe
    • Abstract: We design ordinary least squares (OLS) regression models to estimate the credit conversion factor (CCF) in order to precisely predict the EAD at the account level for defaulted private nonfinancial corporations having credit lines under distressed economic and financial conditions in a developing economy. Our primary focus is on identifying and interpreting the CCF determinants for the defaulted privately owned corporates with credit lines. We apply the models to a unique real-life cross-sectional dataset of defaulted Zimbabwean private corporations. Our empirical results show that the committed amount, the credit usage, the drawn amount, the time to default, the total assets, the ratio of bank debt to total assets, the current ratio, the earnings before interest and tax to total assets ratio, the real gross domestic product growth rate, and the inflation rate are all substantial drivers of the CCF for Zimbabwean private corporates with credit lines. We observe that accounting information is essential in analysing the CCF for private corporations with credit lines under downturn conditions in a developing country. Furthermore, we reveal that the CCF models' forecasting results and the corresponding EAD estimates are augmented by including macroeconomic variables.
      PubDate: 2021-03-31
  • On management risk and price in the mutual fund industry: style and
           performance distribution analysis
    • Abstract: This study shows how investing in mutual funds involves an additional risk, which we call management risk as a consequence of the uncertainty in the results of active management. To address this issue, we analyze a sample of 2539 US equity mutual funds. For comparative purposes, we differentiate among index funds and actively managed mutual funds with different investment styles. We observe that performance distribution shows negative mean, negative skewness, and excess kurtosis. Results also show that management risk is not rewarded with higher abnormal performance. Moreover, higher active management prices are linked to funds with higher management risk and negative asymmetry. Therefore, investors seem to be risk-seeking since they are paying more to participate in high asymmetric bets. Finally, we attempt to solve this puzzle from the behavioral finance perspective.
      PubDate: 2021-03-29
  • The maximum-return-and-minimum-volatility effect: evidence from choosing
           risky and riskless assets to form a portfolio
    • Abstract: The healthcare sector has the highest mean and a low correlation with the business cycle, while Treasury Bills (T-Bills) have the lowest variance in our study. In this paper, we examine the conjecture of whether investors should choose an asset with the highest expected return and an asset with the smallest variance even when the mean–variance rule says “NO”. We examine the conjecture by comparing the performance of portfolios with and without healthcare and 6-M T-bills in the US market. Our findings support the conjecture that investors prefer to invest in portfolios with both healthcare and 6-M T-bills. In addition, we find an arbitrage opportunity in the markets and our findings reject market efficiency. Based on our findings, academics could incorporate both maximum-return and minimum-volatility assets to construct a maximum-return-and-minimum-volatility aggressive-and-yet-defensive trading approach that stochastically dominates most of other assets/portfolios. Thus, our findings can be called the maximum-return-and-minimum-volatility anomaly or the maximum-return-and-minimum-volatility puzzle, or the maximum-return-and-minimum-volatility paradox.
      PubDate: 2021-03-29
  • Risk assessment of VAT invoice crime levels of companies based on DFPSVM:
           a case study in China
    • Abstract: In recent years, with the implementation of the policy of “Replacing Business Tax with Value-Added Tax” and “Streamlining Administration, Delegating Powers and Improving Regulation and Services” in China, criminals have been issuing false invoices, and such cases have shown a trend of high frequency in the category of economic crimes. Tax departments and public security departments are facing increasingly a serious crime situation that has created a new challenge. By studying the current trend of false invoice crime, the difficulties of investigation in such cases are analyzed. Using the tax information of enterprises that have conducted false invoice as the breakthrough point, the machine learning method is introduced to build a risk pre-warning assessment model based on the Support Vector Machine (SVM) method to detect enterprises issuing false invoices. Three steps were designed in this paper. First, a risk pre-warning assessment model was established to detect enterprises issuing false invoices. Second, enterprises were classified into three groups according to the risk levels: A, B, and C. Third, collected data were used to make an empirical analysis, and the results show that the accuracy rate of the model is 97%. In China, due to the high crime rate of tax fraud cases, it is important to obtain data from tax and public security departments to establish a model that can detect such crimes as early as possible. The police and tax authorities can use this model to jointly combat such crimes.
      PubDate: 2021-03-13
  • Achieving financial stability during a liquidity crisis: a multi-objective
    • Abstract: Following the financial crisis of 2007, regulators undertook an ample process of redefinition of the necessary objectives to achieve financial stability. Nevertheless, it is highly likely that the achievement of a specific stability goal precludes the possibility of pursuing other stability goals during financial turmoil. Once considered in this way, financial stability can be immediately translated into a multi-objective decision-making problem. In this paper, we analyze some possible trade-offs faced by a regulator in order to preserve financial stability during a liquidity crisis. To this end, we employ a model of liquidity cascades applied to credit networks and we determine the best among different policy options relying on the MOORA (multi-objective optimization on the basis of ratio analysis) method.
      PubDate: 2021-02-12
      DOI: 10.1057/s41283-021-00067-6
  • CEO overconfidence, firm-specific factors, and systemic risk: evidence
           from China
    • Abstract: This study aims to measure the contribution of banks, financial services institutions, and insurance companies to China’s systemic risk during the 2004–2018 period. This study also evaluates the effect of CEO (chief executive officer) overconfidence and firm-level factors on systemic risk. We employ ΔCoVaR (delta conditional value-at-risk) as a measure of systemic risk and earnings forecast bias to measure CEO overconfidence. We use a fixed effects panel regression approach to evaluate the effect of CEO overconfidence, firm-level factors, and systemic risk. Our findings show that banks that are managed by overconfident CEOs enhance the firm’s contributions to systemic risk. Empirical results also show that the firm’s size, leverage ratio, and loan ratio increase the firm’s contributions to systemic risk. Furthermore, return on assets is found to have an inverse relation with systemic risk. The results of this study are important for constructing financial regulations and policies to mitigate the impact of these factors on systemic risk in China.
      PubDate: 2021-02-08
      DOI: 10.1057/s41283-021-00066-7
  • 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.
      PubDate: 2020-10-30
      DOI: 10.1057/s41283-020-00065-0
  • 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.
      PubDate: 2020-10-20
      DOI: 10.1057/s41283-020-00064-1
  • Cybersecurity hazards and financial system vulnerability: a synthesis of
    • 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
      DOI: 10.1057/s41283-020-00063-2
  • 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.
      PubDate: 2020-07-20
      DOI: 10.1057/s41283-020-00061-4
  • 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.
      PubDate: 2020-07-13
      DOI: 10.1057/s41283-020-00062-3
  • 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.
      PubDate: 2020-07-03
      DOI: 10.1057/s41283-020-00060-5
  • Comparison study of two-step LGD estimation model with probability
    • 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
      DOI: 10.1057/s41283-020-00059-y
  • 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.
      PubDate: 2020-02-29
      DOI: 10.1057/s41283-020-00058-z
  • 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.
      PubDate: 2019-11-16
      DOI: 10.1057/s41283-019-00057-9
  • 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.
      PubDate: 2019-08-31
      DOI: 10.1057/s41283-019-00055-x
  • 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.
      PubDate: 2019-08-29
      DOI: 10.1057/s41283-019-00054-y
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Heriot-Watt University
Edinburgh, EH14 4AS, UK
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