Pub Date : 2023-04-14DOI: 10.1108/jrf-05-2022-0116
A. Banerjee
PurposeThis paper investigates the influence of the ongoing crisis of Russia's incursion on Ukraine on the risk dynamics of energy futures contracts with high-frequency data on four different futures contracts using risk metrics of value at risk (VaR) and conditional value at risk (CVaR) for the USA market.Design/methodology/approachThe author used different generalised autoregressive conditional heteroscedasticity - Extreme Value Theory (GARCH)-EVT models and compared the performance of each of the competing models. Backtesting evidence shows that VaR and CVaR combined with GARCH-EVT better estimate risk.FindingsThe study results show that combined risk metrics are efficient and adaptive to estimating the risk dynamics and backtesting of the models, revealing that the autoregressive moving average (ARMA) (1,1)-asymmetric power autoregressive conditional heteroscedasticity (APARCH) model performs relatively better than other models.Practical implicationsThe paper has practical implications for different market participants. From the risk manager's and day traders' angles, the market participants can estimate the risk exposure in the energy futures contract and take positions accordingly. The results are important for oil-importing countries due to the developing supply crisis and price escalation, which can brew inflation in the economy.Originality/valueTo the best of the author's knowledge, the paper is the first to throw light on the risk angle of energy futures contracts during the ongoing crisis of the Russia–Ukraine war.
{"title":"Russia–Ukrainian war: measuring the intraday risk dynamics of energy futures contracts using VaR and CVaR","authors":"A. Banerjee","doi":"10.1108/jrf-05-2022-0116","DOIUrl":"https://doi.org/10.1108/jrf-05-2022-0116","url":null,"abstract":"PurposeThis paper investigates the influence of the ongoing crisis of Russia's incursion on Ukraine on the risk dynamics of energy futures contracts with high-frequency data on four different futures contracts using risk metrics of value at risk (VaR) and conditional value at risk (CVaR) for the USA market.Design/methodology/approachThe author used different generalised autoregressive conditional heteroscedasticity - Extreme Value Theory (GARCH)-EVT models and compared the performance of each of the competing models. Backtesting evidence shows that VaR and CVaR combined with GARCH-EVT better estimate risk.FindingsThe study results show that combined risk metrics are efficient and adaptive to estimating the risk dynamics and backtesting of the models, revealing that the autoregressive moving average (ARMA) (1,1)-asymmetric power autoregressive conditional heteroscedasticity (APARCH) model performs relatively better than other models.Practical implicationsThe paper has practical implications for different market participants. From the risk manager's and day traders' angles, the market participants can estimate the risk exposure in the energy futures contract and take positions accordingly. The results are important for oil-importing countries due to the developing supply crisis and price escalation, which can brew inflation in the economy.Originality/valueTo the best of the author's knowledge, the paper is the first to throw light on the risk angle of energy futures contracts during the ongoing crisis of the Russia–Ukraine war.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45786159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-07DOI: 10.1108/jrf-03-2022-0053
Olivier Nataf, Lieven De Moor
PurposeThis paper aims to assess the consequences of credit risk downgrades on credit default swaps (hereafter CDS) written on financial companies from two different perspectives, namely the overall stress level observed on the market and the rating agency performing the downgrade.Design/methodology/approachThe authors' study relies on several wavelet analyses performed on different subsamples of data coming from the iTraxx index, the downgrade dates ranging between October 28, 2005 and February 3, 2015. This study highlights that both the overall stress level and the rating agency taking actions do have an influence on how market players will react.FindingsThe authors' study points out that market players will anticipate and react to downgrades in different ways depending on the level of stress. Feedback effects are observed after the downgrade only during periods of tension. From a rating agency point of view, the authors' study shows that the market share as well as the reputation of each agency have an influence on the aftermaths of a downgrade.Originality/valueTo the authors' knowledge, this paper is the first one relying on wavelet to analyse the consequences of a downgrade on CDS market. The use of this methodology allows to capture the multiple impacts of a downgrade through time and, therefore, to analyse the dynamics triggered on the market by a negative rating event. Moreover, the study of the downgrades' repercussions of each of the main rating agencies underlines a psychological dimension in the way market players react to a downgrade.
{"title":"Credit risk downgrades and the CDS market: a wavelet analysis","authors":"Olivier Nataf, Lieven De Moor","doi":"10.1108/jrf-03-2022-0053","DOIUrl":"https://doi.org/10.1108/jrf-03-2022-0053","url":null,"abstract":"PurposeThis paper aims to assess the consequences of credit risk downgrades on credit default swaps (hereafter CDS) written on financial companies from two different perspectives, namely the overall stress level observed on the market and the rating agency performing the downgrade.Design/methodology/approachThe authors' study relies on several wavelet analyses performed on different subsamples of data coming from the iTraxx index, the downgrade dates ranging between October 28, 2005 and February 3, 2015. This study highlights that both the overall stress level and the rating agency taking actions do have an influence on how market players will react.FindingsThe authors' study points out that market players will anticipate and react to downgrades in different ways depending on the level of stress. Feedback effects are observed after the downgrade only during periods of tension. From a rating agency point of view, the authors' study shows that the market share as well as the reputation of each agency have an influence on the aftermaths of a downgrade.Originality/valueTo the authors' knowledge, this paper is the first one relying on wavelet to analyse the consequences of a downgrade on CDS market. The use of this methodology allows to capture the multiple impacts of a downgrade through time and, therefore, to analyse the dynamics triggered on the market by a negative rating event. Moreover, the study of the downgrades' repercussions of each of the main rating agencies underlines a psychological dimension in the way market players react to a downgrade.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45096189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-07DOI: 10.1108/jrf-05-2022-0107
K. Najaf, M. Joshipura, M. M. Alshater
PurposeThis study examined the impact of war/conflict-related news on the Russian and Ukrainian stock markets in the build-up and beginning of the war that sparked in the year 2022.Design/methodology/approachIn order to examine the impact of war-related news on stock returns, data were gathered from the United States (US) and Russian stock indices, oil price and volatile index (VIX) from Yahoo.finance; Ukrainian stock values from pfts.ua website and daily related news retrieved from nexis.com were analysed. The data were gathered from January 1, 2022 to February 24, 2022. Seeming unrealated regressions (SUR) and exponential generalised autoregressive conditional heteroscedastic (EGARCH) models were carried out to determine the formulated correlations. This study controlled the oil price, US stock returns, Chicago Board Options Exchange (CBOE) VIX and difference in stock returns of Russia and Ukraine.FindingsThe results are presented two-fold: first, war-related news between the two countries enhanced volatility and caused a significant decline in the stock market indices for both countries. Second, the Russian stock market faced a steeper decline in the build-up and the actual beginning of the war than the Ukrainian stock market. Notably, the Russian markets feared the adverse economic consequences that stemmed from the sanctions the US and the Western world imposed.Research limitations/implicationsAs this study was based on early evidence, future studies with a longer window may provide better insights. This present study is restricted to the stock returns of the countries directly involved in the build-up towards war. Studies focusing on the impact of other asset classes, currencies, commodities and global stock markets might offer holistic insights.Practical implicationsThe study outcomes suggest that global portfolio investors should stay away from stock markets of the war-raged countries and equity markets in general, but instead look for safe-haven assets.Originality/valueThe paper evaluates stock markets' performance during the pre-war period, considering the context of this historical war between the neighbours. It is important to understand this issue as this war is subject to sanctions by the US and leads to a global supply chain crisis.
{"title":"War build-up and stock returns: evidence from Russian and Ukrainian stock markets","authors":"K. Najaf, M. Joshipura, M. M. Alshater","doi":"10.1108/jrf-05-2022-0107","DOIUrl":"https://doi.org/10.1108/jrf-05-2022-0107","url":null,"abstract":"PurposeThis study examined the impact of war/conflict-related news on the Russian and Ukrainian stock markets in the build-up and beginning of the war that sparked in the year 2022.Design/methodology/approachIn order to examine the impact of war-related news on stock returns, data were gathered from the United States (US) and Russian stock indices, oil price and volatile index (VIX) from Yahoo.finance; Ukrainian stock values from pfts.ua website and daily related news retrieved from nexis.com were analysed. The data were gathered from January 1, 2022 to February 24, 2022. Seeming unrealated regressions (SUR) and exponential generalised autoregressive conditional heteroscedastic (EGARCH) models were carried out to determine the formulated correlations. This study controlled the oil price, US stock returns, Chicago Board Options Exchange (CBOE) VIX and difference in stock returns of Russia and Ukraine.FindingsThe results are presented two-fold: first, war-related news between the two countries enhanced volatility and caused a significant decline in the stock market indices for both countries. Second, the Russian stock market faced a steeper decline in the build-up and the actual beginning of the war than the Ukrainian stock market. Notably, the Russian markets feared the adverse economic consequences that stemmed from the sanctions the US and the Western world imposed.Research limitations/implicationsAs this study was based on early evidence, future studies with a longer window may provide better insights. This present study is restricted to the stock returns of the countries directly involved in the build-up towards war. Studies focusing on the impact of other asset classes, currencies, commodities and global stock markets might offer holistic insights.Practical implicationsThe study outcomes suggest that global portfolio investors should stay away from stock markets of the war-raged countries and equity markets in general, but instead look for safe-haven assets.Originality/valueThe paper evaluates stock markets' performance during the pre-war period, considering the context of this historical war between the neighbours. It is important to understand this issue as this war is subject to sanctions by the US and leads to a global supply chain crisis.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43479964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-03DOI: 10.1108/jrf-07-2022-0191
V. Milovidov
PurposeThe purpose of the article is to show the changing behavior of investors in the post-pandemic period, the continued development of “emotional communities” in the financial market, as well as the factors contributing to their formation and the role of such communities in the elaboration of investors' decisions.Design/methodology/approachThe research includes an analysis of the popularity of various terms searched in the US segment of Google in the financial category from 2004 to 2022, their correlation with financial market indicators and theoretical observations around these data.FindingsThe results obtained by the author allow him to draw the following conclusions: (1) the change in investors' behavior indicates the formation of the new distributed community-centric model of the financial market; (2) the main distinguishing feature of the behavior of many retail investors is gamification; (3) the networking of investors contributes to a significant change in their priorities in the elaboration of investment decisions; (4) the fundamental indicators of the financial market play an ever decreasing role in the decision-making of individual investors.Originality/valueTo the best of the author's knowledge, the formation of emotional communities of investors and their role in the elaboration of mass investor decisions is not widely covered in the literature. The paper develops a framework for further studies on the role of emotional communities in the financial market and in changing behavior of retail investors.
{"title":"Redefining investors' goals in the post–normal world","authors":"V. Milovidov","doi":"10.1108/jrf-07-2022-0191","DOIUrl":"https://doi.org/10.1108/jrf-07-2022-0191","url":null,"abstract":"PurposeThe purpose of the article is to show the changing behavior of investors in the post-pandemic period, the continued development of “emotional communities” in the financial market, as well as the factors contributing to their formation and the role of such communities in the elaboration of investors' decisions.Design/methodology/approachThe research includes an analysis of the popularity of various terms searched in the US segment of Google in the financial category from 2004 to 2022, their correlation with financial market indicators and theoretical observations around these data.FindingsThe results obtained by the author allow him to draw the following conclusions: (1) the change in investors' behavior indicates the formation of the new distributed community-centric model of the financial market; (2) the main distinguishing feature of the behavior of many retail investors is gamification; (3) the networking of investors contributes to a significant change in their priorities in the elaboration of investment decisions; (4) the fundamental indicators of the financial market play an ever decreasing role in the decision-making of individual investors.Originality/valueTo the best of the author's knowledge, the formation of emotional communities of investors and their role in the elaboration of mass investor decisions is not widely covered in the literature. The paper develops a framework for further studies on the role of emotional communities in the financial market and in changing behavior of retail investors.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41719097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-28DOI: 10.1108/jrf-06-2022-0166
Nadia Basty, Ines Ghazouani
PurposeThis study investigates how bank competition affects financial stability and whether government intervention contributes to shaping this relationship in North African countries.Design/methodology/approachA review of the literature on the subject was conducted, combined with an empirical analysis that used a two-step system generalized method of moments (GMM) and a sample of 45 banks operating in North African countries over the period 2005–2019.FindingsThe findings reveal a quadratic relationship between competition and banking stability in North African countries. Competition–stability view and competition–fragility view could be applied at the same time for North African banks. Additionally, in this context, results highlight a negative impact of government intervention on financial stability in a competitive financial sector. North African banks operating in a high government intervention quality environment tend to engage in high-risk investments. Robustness checks with alternative measures of competition and banking stability also show consistent results.Originality/valueTo the authors’ knowledge, this is the first time that the North African context has been explored to determine the role of the quality of government intervention in the relationship between competition and banking system fragility. This paper seeks to cover the shadow field in existing literature through further new information. Thus, it contributes to the emerging market banking literature by showing that both high and low levels of competition can improve financial stability in North African countries. Moreover, it expands its contribution by displaying the moderator effect of intervention quality on the bank competition–stability relationship.
{"title":"Competition–banking stability: the moderating role of government intervention quality in North African countries","authors":"Nadia Basty, Ines Ghazouani","doi":"10.1108/jrf-06-2022-0166","DOIUrl":"https://doi.org/10.1108/jrf-06-2022-0166","url":null,"abstract":"PurposeThis study investigates how bank competition affects financial stability and whether government intervention contributes to shaping this relationship in North African countries.Design/methodology/approachA review of the literature on the subject was conducted, combined with an empirical analysis that used a two-step system generalized method of moments (GMM) and a sample of 45 banks operating in North African countries over the period 2005–2019.FindingsThe findings reveal a quadratic relationship between competition and banking stability in North African countries. Competition–stability view and competition–fragility view could be applied at the same time for North African banks. Additionally, in this context, results highlight a negative impact of government intervention on financial stability in a competitive financial sector. North African banks operating in a high government intervention quality environment tend to engage in high-risk investments. Robustness checks with alternative measures of competition and banking stability also show consistent results.Originality/valueTo the authors’ knowledge, this is the first time that the North African context has been explored to determine the role of the quality of government intervention in the relationship between competition and banking system fragility. This paper seeks to cover the shadow field in existing literature through further new information. Thus, it contributes to the emerging market banking literature by showing that both high and low levels of competition can improve financial stability in North African countries. Moreover, it expands its contribution by displaying the moderator effect of intervention quality on the bank competition–stability relationship.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41767346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-28DOI: 10.1108/jrf-07-2022-0184
Safaa. K. Kadhem, Haider Thajel
PurposeOne of the most important sources of energy in the world, due to its great impact on the global economy, is the crude oil. Due to the instability of oil prices which exhibit extreme fluctuations during periods of different times of market uncertainty, it became hard to the governments to predict accurately the prices of crude oil in order to build their financial budgets. Therefore, this study aims to analyse and model crude oil price using the hidden Markov process (HMM).Design/methodology/approachTraditional mathematical approaches of time series may be not give accurate results to measure and analyse the crude oil price, since the latter has an unstable and fluctuating nature, hence, its prediction forms a challenge task. A novel methodology that is so-called the HMM is proposed that takes into account the heterogeneity in prices as well as their hidden state-based behaviour.FindingsUsing the Bayesian approach, several estimated models with different ranks are fitted to a non-homogeneous data of Iraqi crude oil prices from January 2010 into December 2021. The model selection criteria and measures of the prediction performance of each model are applied to choose the best model. Movements of crude oil prices exhibit extreme fluctuations during periods of different times of market uncertainty. The processes of model estimation and the model selection were conducted in Python V.3.10, and it is available from the first author on request.Originality/valueUsing the Bayesian approach, several estimated models with different ranks are fitted to a non-homogeneous data of Iraqi crude oil prices from January 2010 to December 2021.
{"title":"Modelling of crude oil price data using hidden Markov model","authors":"Safaa. K. Kadhem, Haider Thajel","doi":"10.1108/jrf-07-2022-0184","DOIUrl":"https://doi.org/10.1108/jrf-07-2022-0184","url":null,"abstract":"PurposeOne of the most important sources of energy in the world, due to its great impact on the global economy, is the crude oil. Due to the instability of oil prices which exhibit extreme fluctuations during periods of different times of market uncertainty, it became hard to the governments to predict accurately the prices of crude oil in order to build their financial budgets. Therefore, this study aims to analyse and model crude oil price using the hidden Markov process (HMM).Design/methodology/approachTraditional mathematical approaches of time series may be not give accurate results to measure and analyse the crude oil price, since the latter has an unstable and fluctuating nature, hence, its prediction forms a challenge task. A novel methodology that is so-called the HMM is proposed that takes into account the heterogeneity in prices as well as their hidden state-based behaviour.FindingsUsing the Bayesian approach, several estimated models with different ranks are fitted to a non-homogeneous data of Iraqi crude oil prices from January 2010 into December 2021. The model selection criteria and measures of the prediction performance of each model are applied to choose the best model. Movements of crude oil prices exhibit extreme fluctuations during periods of different times of market uncertainty. The processes of model estimation and the model selection were conducted in Python V.3.10, and it is available from the first author on request.Originality/valueUsing the Bayesian approach, several estimated models with different ranks are fitted to a non-homogeneous data of Iraqi crude oil prices from January 2010 to December 2021.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45714301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-28DOI: 10.1108/jrf-08-2022-0227
I. Abínzano, H. Bonilla, L. Muga
PurposeUsing data from business reorganization processes under Act 1116 of 2006 in Colombia during the period 2008 to 2018, a model for predicting the success of these processes is proposed. The paper aims to validate the model in two different periods. The first one, in 2019, characterized by stability, and the second one, in 2020, characterized by the uncertainty generated by the COVID-19 pandemic.Design/methodology/approachA set of five financial variables comprising indebtedness, profitability and solvency proxies, firm age, macroeconomic conditions, and industry and regional dummies are used as independent variables in a logit model to predict the failure of reorganization processes. In addition, an out-of-sample analysis is carried out for the 2019 and 2020 periods.FindingsThe results show a high predictive power of the estimated model. Even the results of the out-of-sample analysis are satisfactory during the unstable pandemic period. However, industry and regional effects add no predictive power for 2020, probably due to subsidies for economic activity and the relaxation of insolvency legislation in Colombia during that year.Originality/valueIn a context of global reform in insolvency laws, the consistent predictive ability shown by the model, even during periods of uncertainty, can guide regulatory changes to ensure the survival of companies entering into reorganization processes, and reduce the observed high failure rate.
{"title":"Duty calls: prediction of failure in reorganization processes","authors":"I. Abínzano, H. Bonilla, L. Muga","doi":"10.1108/jrf-08-2022-0227","DOIUrl":"https://doi.org/10.1108/jrf-08-2022-0227","url":null,"abstract":"PurposeUsing data from business reorganization processes under Act 1116 of 2006 in Colombia during the period 2008 to 2018, a model for predicting the success of these processes is proposed. The paper aims to validate the model in two different periods. The first one, in 2019, characterized by stability, and the second one, in 2020, characterized by the uncertainty generated by the COVID-19 pandemic.Design/methodology/approachA set of five financial variables comprising indebtedness, profitability and solvency proxies, firm age, macroeconomic conditions, and industry and regional dummies are used as independent variables in a logit model to predict the failure of reorganization processes. In addition, an out-of-sample analysis is carried out for the 2019 and 2020 periods.FindingsThe results show a high predictive power of the estimated model. Even the results of the out-of-sample analysis are satisfactory during the unstable pandemic period. However, industry and regional effects add no predictive power for 2020, probably due to subsidies for economic activity and the relaxation of insolvency legislation in Colombia during that year.Originality/valueIn a context of global reform in insolvency laws, the consistent predictive ability shown by the model, even during periods of uncertainty, can guide regulatory changes to ensure the survival of companies entering into reorganization processes, and reduce the observed high failure rate.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48961864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since the World Health Organization (WHO) declared the COVID-19 outbreak as a public health emergency of international concern, the global economy has been straining under a range of burdens: surging inflation and unemployment rates, tangled global supply chains and tumbling financial markets (Batten et al., 2022;Boubaker et al., 2022;Choudhury et al., 2022;Liu et al., 2022). [...]using a difference-in-differences (DID) analysis, they investigate the epidemic's impact on the market quality of overseas companies and compare it to that of local firms with the same name. [...]the authors compare international companies based on firm-specific features and those of their home countries. According to the authors' findings, the conflict has significantly negatively influenced airlines, although it has benefited the market for military goods. According to the results, investors in these energy markets exhibit a herding behavior.
{"title":"Guest editorial: Implications of the Russia–Ukraine conflict on the global financial markets","authors":"Sabri Boubaker, A. Sarea, T. Choudhury","doi":"10.1108/jrf-01-2023-244","DOIUrl":"https://doi.org/10.1108/jrf-01-2023-244","url":null,"abstract":"Since the World Health Organization (WHO) declared the COVID-19 outbreak as a public health emergency of international concern, the global economy has been straining under a range of burdens: surging inflation and unemployment rates, tangled global supply chains and tumbling financial markets (Batten et al., 2022;Boubaker et al., 2022;Choudhury et al., 2022;Liu et al., 2022). [...]using a difference-in-differences (DID) analysis, they investigate the epidemic's impact on the market quality of overseas companies and compare it to that of local firms with the same name. [...]the authors compare international companies based on firm-specific features and those of their home countries. According to the authors' findings, the conflict has significantly negatively influenced airlines, although it has benefited the market for military goods. According to the results, investors in these energy markets exhibit a herding behavior.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41744458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-24DOI: 10.1108/jrf-09-2022-0240
A. Misra, Molla Ramizur Rahman, A. Tiwari
PurposeThis paper has used account-level data of corporate and retail borrowers, assessed their credit risk through the risk-neutral principle and examined its implication on loan pricing.Design/methodology/approachIt derives the capital charge and credit risk-premium for expected and unexpected losses through a risk-neutral approach. It estimates the risk-adjusted return on capital as the pricing principle for loans. Using GMM regression, the article has assessed the determinants of risk-based pricing.FindingsIt has been found that risk-premium is not reflected in the current loan pricing policy as per Basel II norms. However, the GMM estimation on RAROC can price risk premium and probability of default, LGD, risk weight, bank beta and capital adequacy, which are the prime determinants of loan pricing. The average RAROC for retail loans is more than that of corporate loans despite the same level of risk capital requirement for both categories of loans. The robustness tests indicate that the RAROC method of loan pricing and its determinants are consistent against the time and type of borrowers.Research limitations/implicationsThe RAROC method of pricing effectively assesses the inherent risk associated with loans. Though the empirical findings are confined to the sample bank, the model can be used for any bank implementing the Basel principle of risk and capital assessments.Practical implicationsThe article has developed and validated the model for estimating RAROC, as per Basel II guidelines, for loan pricing that any bank can use.Social implicationsIt has developed the risk-based loan pricing model for retail and corporate borrowers. It has significant practical utility for banks to manage their risk, reduce their losses and productively utilise the public deposits for societal developments.Originality/valueThe article empirically validated the risk-neutral pricing principle using a unique 1,520 retail and corporate borrowers dataset.
{"title":"A risk-neutral approach to the RAROC method of loan pricing using account-level data","authors":"A. Misra, Molla Ramizur Rahman, A. Tiwari","doi":"10.1108/jrf-09-2022-0240","DOIUrl":"https://doi.org/10.1108/jrf-09-2022-0240","url":null,"abstract":"PurposeThis paper has used account-level data of corporate and retail borrowers, assessed their credit risk through the risk-neutral principle and examined its implication on loan pricing.Design/methodology/approachIt derives the capital charge and credit risk-premium for expected and unexpected losses through a risk-neutral approach. It estimates the risk-adjusted return on capital as the pricing principle for loans. Using GMM regression, the article has assessed the determinants of risk-based pricing.FindingsIt has been found that risk-premium is not reflected in the current loan pricing policy as per Basel II norms. However, the GMM estimation on RAROC can price risk premium and probability of default, LGD, risk weight, bank beta and capital adequacy, which are the prime determinants of loan pricing. The average RAROC for retail loans is more than that of corporate loans despite the same level of risk capital requirement for both categories of loans. The robustness tests indicate that the RAROC method of loan pricing and its determinants are consistent against the time and type of borrowers.Research limitations/implicationsThe RAROC method of pricing effectively assesses the inherent risk associated with loans. Though the empirical findings are confined to the sample bank, the model can be used for any bank implementing the Basel principle of risk and capital assessments.Practical implicationsThe article has developed and validated the model for estimating RAROC, as per Basel II guidelines, for loan pricing that any bank can use.Social implicationsIt has developed the risk-based loan pricing model for retail and corporate borrowers. It has significant practical utility for banks to manage their risk, reduce their losses and productively utilise the public deposits for societal developments.Originality/valueThe article empirically validated the risk-neutral pricing principle using a unique 1,520 retail and corporate borrowers dataset.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48997936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-17DOI: 10.1108/jrf-06-2022-0130
Imen Omri
PurposeThis paper aims to quantify the volatility spillover impact and the directional predictability from stock market indexes to Bitcoin.Design/methodology/approachDaily data of 15 developed and 15 emerging stock markets are used for the period March 2017–December 2021.; The author uses vector autoregressive (VAR) model, Granger causality test and impulse response function (IRF) to estimate the results of the study.FindingsEmpirical results show a significant unidirectional volatility spillover impact from emerging markets to Bitcoin and only six stock markets are powerful predictors of Bitcoin return in the short term. Additionally, there is no a difference between developed and developing markets regarding the directional predictability however there is difference in the reaction of Bitcoin return to shocks in the emerging markets compared to developed ones.Originality/valueThe paper proposes different econometric techniques from prior research and presents a comparative analysis between developed and emerging markets.
{"title":"Directional predictability and volatility spillover effect from stock market indexes to Bitcoin: evidence from developed and emerging markets","authors":"Imen Omri","doi":"10.1108/jrf-06-2022-0130","DOIUrl":"https://doi.org/10.1108/jrf-06-2022-0130","url":null,"abstract":"PurposeThis paper aims to quantify the volatility spillover impact and the directional predictability from stock market indexes to Bitcoin.Design/methodology/approachDaily data of 15 developed and 15 emerging stock markets are used for the period March 2017–December 2021.; The author uses vector autoregressive (VAR) model, Granger causality test and impulse response function (IRF) to estimate the results of the study.FindingsEmpirical results show a significant unidirectional volatility spillover impact from emerging markets to Bitcoin and only six stock markets are powerful predictors of Bitcoin return in the short term. Additionally, there is no a difference between developed and developing markets regarding the directional predictability however there is difference in the reaction of Bitcoin return to shocks in the emerging markets compared to developed ones.Originality/valueThe paper proposes different econometric techniques from prior research and presents a comparative analysis between developed and emerging markets.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41844159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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