 Subjects -> STATISTICS (Total: 130 journals)
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- Digitalization and stability in banking sector: a systemic risk
perspective-
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Abstract: Abstract This paper analyzes the relationship between digitalization and systemic risk of commercial banks using data from listed commercial banks from 2010 to 2019. The findings show that the higher the level of digital awareness and the richer the digital products, the lower the systemic risk of banks; the higher the complexity of banks’ digital organization, the higher the systemic risk of banks. The digital development of banks reduces systemic risk overall by optimizing the structure of banks’ deposits and loans, and optimizing the structure of banks' deposits and loans at other levels. The degree of impact of digitalization on the systemic risk of banks differs by their attributes, and regional commercial banks are more affected than large state-owned banks and joint-stock banks. This paper provides useful references for improving banks' refined management, establishing corresponding risk warning mechanisms, and avoiding the risks that information technology may bring.
PubDate: 2023-03-14
- Impact of corporate hedging practices on firm's value: An empirical
evidence from Indian MNCs-
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Abstract: Abstract This paper investigates the effect of financial and non-financial hedging on firms' value while mitigating the exchange rate exposure for 97 Indian multinational corporations from 2009 to 2020. Despite mixed evidence of the value increment effect of corporate hedging, the reduction effect is sporadic. With a dynamic panel set-up and applying the two-step GMM model, the result shows no impact from operation hedging on firms' value. In contrast, financial hedging is significant and impactful. Firms' value enhanced on average by 16.64–19.65% and 10.33–16.15% through derivative and foreign debt, respectively, after controlling non-operating profit (loss) from foreign exchange accounting and translation profit (loss) simultaneously and separately. The result of the robustness test is also consistent with the findings. The positive valuation effect motivates the decision maker of Indian MNCs to hedge the risk of foreign exchange exposure through derivatives and debts.
PubDate: 2023-02-24
- Mean-variance investing with factor tilting
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Abstract: Abstract Factor analysis proposes an alternative approach to standard portfolio theory: the latter is optimisation based, while the former is estimation based. Also, in standard portfolio theory, returns are only explained by the portfolio volatility factor, while factor analysis proposes a multiplicity of factors, which the managers can choose from to tilt their portfolios. In attempting to reconcile these alternative worlds, we propose a penalised utility function, incorporating both the Markowitzian risk-return trade-off and the manager’s preferences towards factors, and discriminating among losses and gains relative to a reference asset. The penalisation affects the optimisation process, favouring the selection of portfolios with less variance and more tilted towards the chosen risk factors. Penalty levels set by the manager generalise the traditional notion of risk aversion. We test our model by building an investment portfolio based on a combination of asset classes and selected investing factors, focussed on the eurozone. To identify the optimal portfolio, we adopt a set of three metaheuristic optimisation algorithms: the fitness function stochastic maximization using genetic algorithms, differential evolution algorithm for global optimisation, and the particle swarm optimisation, and dynamically choose the best solution. In this way, we can improve the Markowitzian optimisation by tilting the asset allocation with managers’ expectations and desired exposures towards designated factors.
PubDate: 2023-02-14
- The role of interactive style of use in improving risk management
effectiveness-
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Abstract: Abstract The purpose of this paper is to investigate how to make risk management systems (RMS) more effective. We identify a critical element for effective RMS, which is neglected in the literature, and argue that the potential of advanced RMS designs (e.g., enterprise risk management—ERM) depends on the interactive style of use. However, interactive use is costly in terms of managers’ time and attention, so many organizations do not use RMS in this way. Using survey data and PLS-SEM, we show for our sample companies that the positive effects of advanced RMS design on effectiveness are fully mediated through interactive use of RMS. This brings on an important implication that a RMS leads to better results when it is used interactively. For example, more interactive use of less developed RMS design (such as the traditional silo-based design) would improve its effectiveness by compensating for the lack of an organization-wide RMS.
PubDate: 2023-02-14
- Non-performing loans and bank lending behaviour
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Abstract: Abstract This article empirically investigates whether the level of non-performing loans (NPLs) affects the bank lending behaviour using the bank-level data across 42 countries, spanning over the period from 2000 to 2017. We find a negative and statistically significant relationship between NPL and bank loan growth. This impact is not geographically restricted and is confirmed for the EU, non-EU, advanced, and emerging countries subsamples. We also examine the channels through which NPLs affect loan growth. Our results show that the association between NPL and loan growth is more pronounced for well-capitalized banks. We find no evidence in support of an effect of asset management companies on the negative association between NPLs and loan growth. In addition, our results are robust with respect to alternative measure of credit risk and different specifications.
PubDate: 2023-01-20
- Assessing the importance of the choice threshold in quantifying market
risk under the POT approach (EVT)-
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Abstract: Abstract From a theoretical point of view, the selection of thresholds is a critical issue in the framework of the Peaks Over Threshold (POT) approach, which is why in the last decade numerous methodologies have been proposed for its selection. In this paper, we address this subject from an empirical point of view by assessing to what extent the selection of the threshold is decisive in quantifying the market risk. For measuring market risk, we use the Value at Risk (VaR) and the Expected Shortfall (ES) measures. The results obtained indicate that there is a large set of thresholds that provide similar Generalized Pareto Distribution (GPD) quantiles estimators and as a consequence similar market risk measures. Just only, for large thresholds, those corresponding to the 98th and 99th percentile of the GPD some differences are found. It means that the choice of threshold in the framework of the POT method may not be relevant in quantifying market risk when we use the VaR and ES measures for this task.
PubDate: 2023-01-06
DOI: 10.1057/s41283-022-00106-w
- Not all bull and bear markets are alike: insights from a five-state hidden
semi-Markov model-
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Abstract: Abstract This paper employs the hidden semi-Markov model and a novel model selection procedure to determine different regimes in the US stock market. The empirical results suggest that the US stock market is switching between five states that can be classified into three bull states and two bear states. The three bull states are categorized as a low-volatility bull market, a high-volatility bull market, and a stock market bubble. One of the bear states represents a regular bear market, while the other corresponds to either a stock market crash or a market correction. The paper demonstrates that the five-state model is consistent with a number of stylized facts and provides many valuable insights into the regime-switching dynamics of the US stock market and the risk-reward pattern of each regime. Besides, the paper demonstrates that the five-state model enables investors to make better asset allocation decisions. Specifically, in out-of-sample tests, the asset allocation strategy based on the five-state model achieves higher performance with lower risk than the strategy based on the two-state model and the buy-and-hold benchmark.
PubDate: 2022-12-23
DOI: 10.1057/s41283-022-00112-y
- Risk measures-based cluster methods for finance
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Abstract: Abstract This paper performs an extensive comparison of cluster techniques for financial applications based on risk measures and returns as classification variables. We consider the cluster techniques and risk measures largely used in the literature. For the analysis, we use a database composed of daily returns of the U.S. equity market. As for financial applications, we consider capital determination, portfolio optimization, and asset pricing. We found that the number of clusters varies over the years. The years with the fewest clusters coincide with periods of instability, such as 2008 (Subprime Crisis) and 2015 (slowdown in United States domestic product). Overall, we observe that our data support the superiority of the Fanny and MC approaches. By construction, both techniques are more robust to the distinct probabilistic distribution of data, which is typically the case for financial data. Furthermore, our results highlight the practical utility of considering risk measures and returns as classification variables in financial applications.
PubDate: 2022-12-22
DOI: 10.1057/s41283-022-00110-0
- Information security risk management terminology and key concepts
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Abstract: Abstract Language is the foundation for any communication and the vocabulary used has a decisive influence on the ability of the communication partners to clearly understand each other. In Information Security Risk Management (ISRM), the terminology used is often dictated by industry standards and frameworks. However, there is no universally accepted terminology, which makes collaboration difficult for professionals and researchers alike. This publication compares the terminology defined by frequently used frameworks, such as ISO and NIST, in the field of ISRM. It examines the terms and inherent concepts of each terminology, compares the notion of risk and derives a concept diagram based on the most important key concepts. The result facilitates a common understanding of ISRM across frameworks and organisational boundaries, thus enables further research, discussion, intra- and inter-firm communication.
PubDate: 2022-12-16
DOI: 10.1057/s41283-022-00108-8
- IRB Asset and Default Correlation: Rationale for the Macroprudential
Mark-Ups to the IRB Risk-Weights-
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Abstract: Abstract There is a vast amount of literature criticizing the Basel Committee approach to the credit risk regulation, more specifically, the Internal Ratings-Based (IRB), as an excessively conservative one. However, the novelty of the current paper is that we identify when the IRB approach is too lax, i.e., we are able to present cases with the material credit risk underestimation. We show that the portfolio default rate (DR) depends on two parameters: probability of default (PD) and default correlation. Inversely, we offer a reproducible approach on how to derive the default correlation from historical data. Then it also depends on two parameters: PD and the historical DR variance. However, the IRB approach previewed only PD (and asset class) as the correlation determinants, neglecting the second contributor (DR variance). Hence, we demonstrate that when the actual DR variance exceeds the mean DR value, IRB may result in the credit risk underestimation. The almost two-fold underestimation is found for the credit cards (qualified revolving retail loans) asset class. The paper offers a practical solution how to adjust the revealed credit risk underestimation. The macroprudential add-ons to IRB risk-weights might be a workable solution format. Opinions expressed in the paper are solely those of the author and may not necessarily reflect the official position of the affiliated institution. Bank of Russia neither assumes any responsibility for the publication.
PubDate: 2022-12-16
DOI: 10.1057/s41283-022-00109-7
- Exploring the indirect links between enterprise risk management and the
financial performance of SMEs-
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Abstract: Abstract This paper responds to the lack of empirical evidence on how enterprise risk management (ERM) and the financial performance of small and medium-sized enterprises (SMEs) are related. Structural equation modeling is used to explore new mediators in the relationship between ERM and SME financial performance. The results show that organizational culture (mission dimension) and strategic risk management performance are full and positive mediators between ERM and financial performance. These research results highlight the fact that the implementation of ERM in an enterprise does not by itself generate the expected effects without the existence of a mature organizational culture and the monitoring of strategic risk management performance. These findings are particularly relevant for SMEs with “pretend ERM” that lacks the strategic and operational components. ERM also helps to transform the negative effect of foreign capital in SME equity on financial performance into a positive effect.
PubDate: 2022-12-12
DOI: 10.1057/s41283-022-00107-9
- Do risk governance and effective board affect bank performance'
Evidence from large banks worldwide-
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Abstract: Abstract Worldwide, recent corporate financial scandals have raised many questions in terms of risk management and governance of banks. This study examines the impact of risk management-related corporate governance mechanisms on bank performance. Focusing on a sample of large banks, we want to assess the effect of the board of directors and risk management committee features as well as the presence of a Chief Risk Officer (CRO) in the executive board on performance over the period 2006–2017. To examine this relation properly, we employ the system-GMM (Generalized Method of Moments). Results show the importance of the risk management committee in enhancing bank performance. We also find that the presence of a CRO in the bank’s executive board decreases performance and that the lower the number of meetings, women, and independent members, the better the banks’ performance. Most importantly, results show that establishing risk governance will make banks more profitable and sustainable for the future.
PubDate: 2022-09-01
DOI: 10.1057/s41283-022-00101-1
- Oil tail-risk forecasts: from financial crisis to COVID-19
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Abstract: Abstract The coronavirus outbreak has caused unprecedented volatility in oil prices. This paper extends previous studies on oil Value-at-Risk (VaR) by providing extra insights into Expected Shortfall (ES) forecasting over the last decade, including several oil crises. We introduce a conditional volatility model combined with the Cornish–Fisher expansion for ES forecasting. In comparison to the widely used volatility models and innovation distributions, this approach is superior for predicting the ES of long positions but overestimates VaR for short positions. Overall, the volatility model addressing leverage effects with skewed t innovation produces the most accurate joint VaR and ES forecasting. Moreover, the magnitude of ES relative to VaR varies across models and time, implying that ES should be used in conjunction with VaR to inform timely risk management decisions. The results would be of interest to the regulatory authorities, energy companies, and financial institutions for oil tail-risk forecasting.
PubDate: 2022-08-27
DOI: 10.1057/s41283-022-00100-2
- Automated text mining process for corporate risk analysis and management
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Abstract: Abstract The aim of this research is to introduce innovative automated text mining process to extract operation risks from accounting narratives and to further examine the association between these risk types and operating performance. Specifically, we perform topic modeling to decompose a large amount of unstructured textual disclosures into some topics and preserve these topics, which are relevant to business operation risk. Sequentially, we propose a measure for the degree of financial default, referred to as the “intensity of risk-word list,” by joint utilization of text mining and a statistical approach. The analyzed results are then fed into a support vector machine-based model to construct the forecasting model. The results show that the textual-based risk indicators are significantly and positively related to a corporate’s operation efficiency. This study also echoes the recent trend of financial reporting regulations to add a new section on risk factors in annual reports.
PubDate: 2022-08-08
DOI: 10.1057/s41283-022-00099-6
- Changes in risk and entrepreneurship
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Abstract: Abstract In this paper, we extend the existing literature on entrepreneurship by analyzing the effects of changes in risk on two decisions made by the entrepreneur: first, the decision to transit from paid and risk-free employment to risky entrepreneurship, and second, the decision regarding the size or scale of the venture for transitioned entrepreneurs. We provide the conditions that guarantee expected comparative static results under first- and second-order stochastic dominance shifts. We then apply our results to the case of hyperbolic absolute risk aversion preferences, which is a specific functional form commonly used in the economics of risk literature. Interesting results arise from the analysis, where relative risk aversion, risk tolerance, and the inverse of prudence play key roles in our results.
PubDate: 2022-07-13
DOI: 10.1057/s41283-022-00098-7
- Systematic extreme potential gain and loss spillover across countries
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Abstract: Abstract This paper investigates the existence of systematic extreme risks at a multi-country level that leads to gains and losses spillover. A measure of systematic risk that quantifies both the downside risk and the upside potential in the extreme is introduced. This measure is based on the Conditional-Value-at-Risk (CoVaR) measure and copulas to capture dependencies. Using our approach, we study the contagion effect between different financial markets in the extreme. We show that there is an asymmetric contagion effect from the US stock market to other international markets. The impact is higher when the US market is extremely bear than when it is extremely bull. This paper adds novel findings on the asymmetry between extreme losses and extreme gains and the differences among different countries’ reactions to shocks.
PubDate: 2022-07-11
DOI: 10.1057/s41283-022-00097-8
- Default risk as a factor preventing companies from entering the sukuk
market-
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Abstract: Abstract The international sukuk market is represented by a limited number of issuers. One of the factors preventing companies from entering the market is, apparently, elucidating the true default risk of the potential sukuk issuance and related risk-minimization tools, such as guarantees and ratings. The article focuses on the default risk the potential sukuk issuer should consider. The research methodology includes a comparison between the theoretical maxims of sukuk, described by scholars and standard setters, and the existing market practice. To evaluate the potential impact of defaults and near defaults on the issuer’s reputation, a poll was conducted among the market practitioners. The results show that sukuk largely continue to imitate the bond market as per the default risk, and the path dependence of the industry on the ill-formed sukuk dominating the market impedes the revert to the initial concept of sukuk as an investment instrument. Certain steps are suggested for a potential issuer to minimize the default risk.
PubDate: 2022-07-07
DOI: 10.1057/s41283-022-00096-9
- Heterogeneity in cyber loss severity and its impact on cyber risk
measurement-
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Abstract: Abstract We use the world’s largest publicly available dataset of operational risk to model cyber losses and show that the Tweedie model best fits the cyber loss severity in the financial industry. Three key determinants of loss severity are firm size, contagion risk and legal liability. We also measure the size of risk based on the estimation results and show a large degree of heterogeneity across financial firms. The results are particularly relevant with respect to the recent discussion on simplifying operational risk capital requirements and reiterate the importance of considering individual firm characteristics when modelling operational losses.
PubDate: 2022-06-06
DOI: 10.1057/s41283-022-00095-w
- Political, economic, and financial country risks and the volatility of the
South African Exchange Traded Fund market: A GARCH-MIDAS approach-
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Abstract: Abstract Despite the soaring popularity of Exchange Traded Funds (ETFs) in South Africa, country risk may have a minimal or no effect on ETFs because ETF investors can use a wide variety of market timing activities to minimize their exposure to country risks. This study investigated the effect of political, economic, and financial components of country risk on the volatility of the South African ETF market. A GARCH-MIDAS approach was employed to analyse a sample of South African ETFs from November 2000 to December 2019. The ETF market was segregated into a market of ETFs with domestic benchmarks and a market of ETFs with international benchmarks. The findings suggest that country risk components are significant sources of volatility in ETF markets except for financial risk which does not significantly impact ETFs with international benchmarks suggesting that these ETFs can be used to minimize an investor’s exposure to financial risk. Overall, this study provides new insight into the use of ETFs to diversify an investor’s exposure to different country risk components.
PubDate: 2022-04-07
DOI: 10.1057/s41283-022-00093-y
- Sparsity and stability for minimum-variance portfolios
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Abstract: Abstract The popularity of modern portfolio theory has decreased among practitioners because of its unfavorable out-of-sample performance. Estimation risk tends to affect the optimal weight calculation noticeably, especially when a large number of assets are considered. To overcome these issues, many methods have been proposed in recent years, but only a few address practically relevant questions related to portfolio allocation. This study therefore uses different covariance estimation techniques, combines them with sparse model approaches, and includes a turnover constraint that induces stability. We use two datasets of the S&P 500 to create a realistic data foundation for our empirical study. We discover that it is possible to maintain the low-risk profile of efficient estimation methods while automatically selecting only a subset of assets and further inducing low portfolio turnover. Moreover, we find that simply using LASSO is insufficient to lower turnover when the model’s tuning parameter can change over time.
PubDate: 2022-03-11
DOI: 10.1057/s41283-022-00091-0
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