This paper employs the Panel Vector Autoregression (PVAR) method to examine the dynamic interrelationship between Economic Policy Uncertainty (EPU) and stock market returns. The existing literature has not reached a consensus on the relationship between EPU and stock market returns, and there is a lack of comparative analysis of domestic and foreign EPU. Therefore, this paper is the first to incorporate domestic and foreign EPU, stock market returns, and output into a unified framework, considering the dual impact of domestic and foreign EPU shocks. Additionally, the generalizability of the results is ensured by including a large sample of nine emerging and eleven advanced economies. The main findings are as follows: First, a positive shock to foreign EPU leads to a decline in stock market returns and is stronger than the impact of domestic EPU. Second, a positive shock to stock market returns reduces both domestic and foreign EPU. Third, a rise in stock market returns promotes domestic output growth, while increases in domestic and foreign EPU suppress domestic output growth. Finally, the United States is a net exporter of EPU rather than a net importer.
{"title":"Research on the Dynamic Interrelationship between Economic Policy Uncertainty and Stock Market Returns","authors":"Mingguo Zhao, Hail Park","doi":"10.3390/jrfm17080347","DOIUrl":"https://doi.org/10.3390/jrfm17080347","url":null,"abstract":"This paper employs the Panel Vector Autoregression (PVAR) method to examine the dynamic interrelationship between Economic Policy Uncertainty (EPU) and stock market returns. The existing literature has not reached a consensus on the relationship between EPU and stock market returns, and there is a lack of comparative analysis of domestic and foreign EPU. Therefore, this paper is the first to incorporate domestic and foreign EPU, stock market returns, and output into a unified framework, considering the dual impact of domestic and foreign EPU shocks. Additionally, the generalizability of the results is ensured by including a large sample of nine emerging and eleven advanced economies. The main findings are as follows: First, a positive shock to foreign EPU leads to a decline in stock market returns and is stronger than the impact of domestic EPU. Second, a positive shock to stock market returns reduces both domestic and foreign EPU. Third, a rise in stock market returns promotes domestic output growth, while increases in domestic and foreign EPU suppress domestic output growth. Finally, the United States is a net exporter of EPU rather than a net importer.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"369 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932061","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}
This paper investigates five different different Auto Regressive Moving Average (ARMA) and Generalized Auto Regressive Condition-al Heteroscedacity (GARCH models (GARCH, exponential GARCH or EGARCH, integrated GARCH or IGARCH, Component GARCH or CGARCH and the Glosten-Jagannathan-Runkle GARCH or GJR-GARCH) along with six distributions (normal, Student’s t, GED and their skewed forms), which are used to estimate the price dynamics of the Bulgarian stock index SOFIX. We use the best model to predict how much time it will take, after the latest crisis, for the SOFIX index to reach its historical peak once again. The empirical data cover the period between the years 2000 and 2024, including the 2008 financial crisis and the COVID-19 pandemic. The purpose is to answer which of the five models is the best at analysing the SOFIX price and which distribution is most appropriate. The results, based on the BIC and AIC, show that the ARMA(1,1)-CGARCH(1,1) specification with the Student’s t-distribution is preferred for modelling. From the results obtained, we can confirm that the CGARCH model specification supports a more appropriate description of SOFIX volatility than a simple GARCH model. We find that long-term shocks have a more persistent impact on volatility than the effect of short-term shocks. Furthermore, for the same magnitude, negative shocks to SOFIX prices have a more significant impact on volatility than positive shocks. According to the results, when predicting future values of SOFIX, it is necessary to include both a first-order autoregressive component and a first-order moving average in the mean equation. With the help of 5000 simulations, it is estimated that the chances of SOFIX reaching its historical peak value of 1976.73 (08.10.2007) are higher than 90% at 13.08.2087.
{"title":"Econometric Analysis of SOFIX Index with GARCH Models","authors":"Plamen Petkov, Margarita Shopova, Tihomir Varbanov, Evgeni Ovchinnikov, Angelin Lalev","doi":"10.3390/jrfm17080346","DOIUrl":"https://doi.org/10.3390/jrfm17080346","url":null,"abstract":"This paper investigates five different different Auto Regressive Moving Average (ARMA) and Generalized Auto Regressive Condition-al Heteroscedacity (GARCH models (GARCH, exponential GARCH or EGARCH, integrated GARCH or IGARCH, Component GARCH or CGARCH and the Glosten-Jagannathan-Runkle GARCH or GJR-GARCH) along with six distributions (normal, Student’s t, GED and their skewed forms), which are used to estimate the price dynamics of the Bulgarian stock index SOFIX. We use the best model to predict how much time it will take, after the latest crisis, for the SOFIX index to reach its historical peak once again. The empirical data cover the period between the years 2000 and 2024, including the 2008 financial crisis and the COVID-19 pandemic. The purpose is to answer which of the five models is the best at analysing the SOFIX price and which distribution is most appropriate. The results, based on the BIC and AIC, show that the ARMA(1,1)-CGARCH(1,1) specification with the Student’s t-distribution is preferred for modelling. From the results obtained, we can confirm that the CGARCH model specification supports a more appropriate description of SOFIX volatility than a simple GARCH model. We find that long-term shocks have a more persistent impact on volatility than the effect of short-term shocks. Furthermore, for the same magnitude, negative shocks to SOFIX prices have a more significant impact on volatility than positive shocks. According to the results, when predicting future values of SOFIX, it is necessary to include both a first-order autoregressive component and a first-order moving average in the mean equation. With the help of 5000 simulations, it is estimated that the chances of SOFIX reaching its historical peak value of 1976.73 (08.10.2007) are higher than 90% at 13.08.2087.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931964","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}
Isabel Oliveira, Jorge Figueiredo, Maria Faria, Francisco V. Martins
This study analyses the variables that influence investment in a sample of small, labour-intensive companies in a sector that is highly dependent on external demand and the world economy. The aim is to test the three traditional theories of investment (neoclassical theory, free cash flow theory and agency theory), as well as consider the existence of other variables endogenous and exogenous to the company, in order to obtain a model that is appropriate to the reality of the companies in the sample, which consists of 3859 companies in the Portuguese textile sector, for the period from 2010 to 2022. Although there are many studies on the subject, the sample of companies used is different from the others, presenting a unique perspective for understanding investment dynamics in this type of company. The methodology used involves estimating panel data models using the GMM method. The results show that there is a statistically significant and negative relationship between liquidity and asset turnover and investment, so the free cash flow and neoclassical theories, respectively, are partially verified. The agency theory is not confirmed. Other variables are significant in explaining investment: the debt structure is statistically negative, while the size of the company, the GDP and the interest rate are statistically positive. Return on assets proved not to be statistically significant in explaining investment. To summarise, the study highlights the need for financial strategies adapted to the unique characteristics of small businesses.
{"title":"Accounting and Macroeconomic Variables Explaining Investment: An Empirical Study with Panel Data in the Portuguese Textile Sector","authors":"Isabel Oliveira, Jorge Figueiredo, Maria Faria, Francisco V. Martins","doi":"10.3390/jrfm17080345","DOIUrl":"https://doi.org/10.3390/jrfm17080345","url":null,"abstract":"This study analyses the variables that influence investment in a sample of small, labour-intensive companies in a sector that is highly dependent on external demand and the world economy. The aim is to test the three traditional theories of investment (neoclassical theory, free cash flow theory and agency theory), as well as consider the existence of other variables endogenous and exogenous to the company, in order to obtain a model that is appropriate to the reality of the companies in the sample, which consists of 3859 companies in the Portuguese textile sector, for the period from 2010 to 2022. Although there are many studies on the subject, the sample of companies used is different from the others, presenting a unique perspective for understanding investment dynamics in this type of company. The methodology used involves estimating panel data models using the GMM method. The results show that there is a statistically significant and negative relationship between liquidity and asset turnover and investment, so the free cash flow and neoclassical theories, respectively, are partially verified. The agency theory is not confirmed. Other variables are significant in explaining investment: the debt structure is statistically negative, while the size of the company, the GDP and the interest rate are statistically positive. Return on assets proved not to be statistically significant in explaining investment. To summarise, the study highlights the need for financial strategies adapted to the unique characteristics of small businesses.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"19 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925819","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}
We study recent monthly data to help long-term investors buy or sell from the 30 Dow Jones Industrial Average (DJIA) Index components. The recommendations are based on six stock-picking algorithms and their average ranks. We explain the reasons for ignoring the claim that the Sharpe ratio algorithm lacks monotonicity. Since the version of “omega” in the literature uses weights that distort the actual gain–pain ratio faced by investors, we propose new weights. We use data from 30 stocks using the past 474 months (39+ years) of monthly closing prices, ending in May 2024. Our buy-sell recommendations also use newer “pandemic-proof” out-of-sample portfolio performance comparisons from the R package ‘generalCorr’. We report twelve sets of ranks for both out-of- and in-sample versions of the six algorithms. Averaging the twelve sets yields the top and bottom k stocks. For example, k=2 suggests buying Visa Inc. and Johnson & Johnson while selling Coca-Cola and Procter & Gamble.
{"title":"May 2024 Buy-Sell Guide for Dow Jones 30 Stocks and Modified Omega Criterion","authors":"H. D. Vinod","doi":"10.3390/jrfm17080343","DOIUrl":"https://doi.org/10.3390/jrfm17080343","url":null,"abstract":"We study recent monthly data to help long-term investors buy or sell from the 30 Dow Jones Industrial Average (DJIA) Index components. The recommendations are based on six stock-picking algorithms and their average ranks. We explain the reasons for ignoring the claim that the Sharpe ratio algorithm lacks monotonicity. Since the version of “omega” in the literature uses weights that distort the actual gain–pain ratio faced by investors, we propose new weights. We use data from 30 stocks using the past 474 months (39+ years) of monthly closing prices, ending in May 2024. Our buy-sell recommendations also use newer “pandemic-proof” out-of-sample portfolio performance comparisons from the R package ‘generalCorr’. We report twelve sets of ranks for both out-of- and in-sample versions of the six algorithms. Averaging the twelve sets yields the top and bottom k stocks. For example, k=2 suggests buying Visa Inc. and Johnson & Johnson while selling Coca-Cola and Procter & Gamble.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"26 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926403","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}
We investigate the impact of diversity and inclusion (D&I) on firm performance for the period 2017–2021. While the existing literature examines the relationship between diversity and firm performance, little is known about the combined effects of D&I on firm performance. This study aims to utilize the most widely used data source, the Global Diversity and Inclusion (D&I) Index, provided by the LSEG workspace. Using 8089 firm-year observations from a sample of globally listed firms and an OLS regression model, we find that firms with a higher D&I score have better firm performance, as measured by Tobin’s Q. Our moderating analysis shows that the impact of D&I on firm performance is more pronounced for firms with higher institutional ownership. We also split institutional ownership into domestic and foreign institutional ownership and show that the influence of D&I on firm performance differs between domestic and foreign institutional ownership. Our result is robust when we use an alternative proxy for firm performance and consider the findings without US firms in the sample. The overall findings indicate that considering a diverse and inclusive workforce is worthwhile for key stakeholders when making policy decisions.
{"title":"Impact of Diversity and Inclusion on Firm Performance: Moderating Role of Institutional Ownership","authors":"Rubel Saha, Md Nurul Kabir, Syed Asif Hossain, Sheikh Mohammad Rabby","doi":"10.3390/jrfm17080344","DOIUrl":"https://doi.org/10.3390/jrfm17080344","url":null,"abstract":"We investigate the impact of diversity and inclusion (D&I) on firm performance for the period 2017–2021. While the existing literature examines the relationship between diversity and firm performance, little is known about the combined effects of D&I on firm performance. This study aims to utilize the most widely used data source, the Global Diversity and Inclusion (D&I) Index, provided by the LSEG workspace. Using 8089 firm-year observations from a sample of globally listed firms and an OLS regression model, we find that firms with a higher D&I score have better firm performance, as measured by Tobin’s Q. Our moderating analysis shows that the impact of D&I on firm performance is more pronounced for firms with higher institutional ownership. We also split institutional ownership into domestic and foreign institutional ownership and show that the influence of D&I on firm performance differs between domestic and foreign institutional ownership. Our result is robust when we use an alternative proxy for firm performance and consider the findings without US firms in the sample. The overall findings indicate that considering a diverse and inclusive workforce is worthwhile for key stakeholders when making policy decisions.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925874","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}
This paper investigates the relationship between a country’s political governance and financial market dynamics, with a specific focus on non-U.S. stocks listed on the NYSE. Utilizing an ordinary least squares (OLS) regression model with heteroscedasticity-robust (Huber–White) estimators, we analyze the impact of political governance on stock liquidity and information asymmetry. Our analysis shows that stocks from democracies demonstrate improved liquidity and decreased information asymmetry, contrasting with stocks from autocracies that exhibit the opposite trend. Furthermore, shifts in political regimes dynamically impact stock liquidity and information transparency. These findings offer essential insights for investors, policymakers, and regulators, contributing to informed decision making and the formulation of policies that promote market health and transparency. Additionally, these findings underscore the importance of promoting political stability and transparent governance to foster healthy and efficient financial markets.
{"title":"Political Regimes, Stock Liquidity, and Information Asymmetry in a Global Context","authors":"Jang-Chul Kim, Qing Su, Teressa Elliott","doi":"10.3390/jrfm17080342","DOIUrl":"https://doi.org/10.3390/jrfm17080342","url":null,"abstract":"This paper investigates the relationship between a country’s political governance and financial market dynamics, with a specific focus on non-U.S. stocks listed on the NYSE. Utilizing an ordinary least squares (OLS) regression model with heteroscedasticity-robust (Huber–White) estimators, we analyze the impact of political governance on stock liquidity and information asymmetry. Our analysis shows that stocks from democracies demonstrate improved liquidity and decreased information asymmetry, contrasting with stocks from autocracies that exhibit the opposite trend. Furthermore, shifts in political regimes dynamically impact stock liquidity and information transparency. These findings offer essential insights for investors, policymakers, and regulators, contributing to informed decision making and the formulation of policies that promote market health and transparency. Additionally, these findings underscore the importance of promoting political stability and transparent governance to foster healthy and efficient financial markets.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"53 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928017","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}
Paolo Maccarrone, Alessandro Illuzzi, Simone Inguanta
In recent years, the field of “ESG finance” has seen rapid growth, resulting in the emergence and expansion of ESG ratings and rating agencies. This study investigates how financial investors react to updates in ESG ratings provided by two prominent ESG rating agencies, namely MSCI and Refinitiv. The main objective is to determine whether any positive or negative changes in a company’s sustainability ratings directly impact its market value. The Event Study methodology was used for this investigation, which analyses the Cumulated Average Abnormal Returns (CAARs) of economic events to assess their influence on corporate valuations. We analysed over 840 rating updates (events) using a sample of 75 companies across various industries, all listed on major stock exchanges. Our findings indicate that shifts in sustainability ratings, as evaluated by the two rating agencies, do not significantly impact companies’ market capitalisation. Furthermore, these outcomes remain consistent over time, suggesting that financial markets are not assigning increasing significance to ESG ratings. We offer potential explanations for these findings, which are discussed in light of the existing literature on the subject.
{"title":"Does a Change in the ESG Ratings Influence Firms’ Market Value? Evidence from an Event Study","authors":"Paolo Maccarrone, Alessandro Illuzzi, Simone Inguanta","doi":"10.3390/jrfm17080340","DOIUrl":"https://doi.org/10.3390/jrfm17080340","url":null,"abstract":"In recent years, the field of “ESG finance” has seen rapid growth, resulting in the emergence and expansion of ESG ratings and rating agencies. This study investigates how financial investors react to updates in ESG ratings provided by two prominent ESG rating agencies, namely MSCI and Refinitiv. The main objective is to determine whether any positive or negative changes in a company’s sustainability ratings directly impact its market value. The Event Study methodology was used for this investigation, which analyses the Cumulated Average Abnormal Returns (CAARs) of economic events to assess their influence on corporate valuations. We analysed over 840 rating updates (events) using a sample of 75 companies across various industries, all listed on major stock exchanges. Our findings indicate that shifts in sustainability ratings, as evaluated by the two rating agencies, do not significantly impact companies’ market capitalisation. Furthermore, these outcomes remain consistent over time, suggesting that financial markets are not assigning increasing significance to ESG ratings. We offer potential explanations for these findings, which are discussed in light of the existing literature on the subject.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931967","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}
Traditional financial performance measures should be extended to provide additional information to stakeholders. One such extension is the economic value added (EVA). It shows residual profit above the cost of financing, both creditors and equity financing. This paper elaborates on the impact of selected financial ratios on EVA to total assets and EVA to capital employed using the 20-year aggregated data of non-financial business entities operating in Croatia. It answers the research question of which of the selected financial ratios impacts the above-mentioned EVA-based ratios. Applying dynamic panel data modeling using the generalized method of moments technique resulted in the derivation of two models. The human capital efficiency ratio was statistically significant in both models, positively affecting EVA/total assets and EVA/capital employed. In contrast, the debt ratio and net profit margin were significant only in the second model, where EVA/capital employed was a dependent variable. The research results indicate that the debt ratio affects EVA/capital employed negatively while the net profit margin has a positive effect, confirming the existing research. Total liabilities/earnings before interest, taxes, depreciation and amortization, and total asset turnover were not found to be significant in either of the two models.
应扩展传统的财务业绩衡量标准，为利益相关者提供更多信息。经济增加值（EVA）就是这样一种扩展。它显示的是高于融资成本（包括债权人和股权融资）的剩余利润。本文利用在克罗地亚运营的非金融企业实体的 20 年汇总数据，详细阐述了选定财务比率对 EVA 与总资产之比以及 EVA 与已动用资本之比的影响。本文回答了所选财务比率中哪些比率会影响上述基于 EVA 的比率这一研究问题。通过使用广义矩法技术建立动态面板数据模型，得出了两个模型。在这两个模型中，人力资本效率比率都具有统计意义，对 EVA/总资产和 EVA/已动用资本产生正向影响。相比之下，负债率和净利润率只在第二个模型中显著，而 EVA/ 已用资本是一个因变量。研究结果表明，负债率对 EVA/资本占用率有负面影响，而净利润率则有正面影响，这证实了现有的研究。总负债/未计利息、税项、折旧和摊销前利润以及总资产周转率在两个模型中均不显著。
{"title":"The Impact of Selected Financial Ratios on Economic Value Added: Evidence from Croatia","authors":"Robert Zenzerović, Manuel Benazić","doi":"10.3390/jrfm17080338","DOIUrl":"https://doi.org/10.3390/jrfm17080338","url":null,"abstract":"Traditional financial performance measures should be extended to provide additional information to stakeholders. One such extension is the economic value added (EVA). It shows residual profit above the cost of financing, both creditors and equity financing. This paper elaborates on the impact of selected financial ratios on EVA to total assets and EVA to capital employed using the 20-year aggregated data of non-financial business entities operating in Croatia. It answers the research question of which of the selected financial ratios impacts the above-mentioned EVA-based ratios. Applying dynamic panel data modeling using the generalized method of moments technique resulted in the derivation of two models. The human capital efficiency ratio was statistically significant in both models, positively affecting EVA/total assets and EVA/capital employed. In contrast, the debt ratio and net profit margin were significant only in the second model, where EVA/capital employed was a dependent variable. The research results indicate that the debt ratio affects EVA/capital employed negatively while the net profit margin has a positive effect, confirming the existing research. Total liabilities/earnings before interest, taxes, depreciation and amortization, and total asset turnover were not found to be significant in either of the two models.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931966","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}
There are certain times in our economic and financial environments when it makes sense to assess carefully and dispassionately where we are in the credit cycle and how this cycle relates to the business cycle. Now, mid-2024, is one of those times, as the economic uncertainties are at substantial levels. This note reflects my long history of studying credit cycles dating back to the early 1970s. My current assessment is that the Benign Credit Cycle we have enjoyed since 2010, with the exception of a few months in 2016 and early 2020, ended in 2023. We recently reached an inflection point for an average credit risk scenario. This assessment is based on an analysis of a number of historical indicators over the last 50 years. This conclusion is tempered by the possibility that the U.S. credit picture will continue its heightened risk trend toward a Stressed Scenario by the end of 2024, and combined with a “hard-landing” economic recession, we could witness another financial-credit crisis.
{"title":"Forecasting Credit Cycles: The Case of the Leveraged Finance Market in 2024 and Outlook","authors":"Edward I. Altman","doi":"10.3390/jrfm17080339","DOIUrl":"https://doi.org/10.3390/jrfm17080339","url":null,"abstract":"There are certain times in our economic and financial environments when it makes sense to assess carefully and dispassionately where we are in the credit cycle and how this cycle relates to the business cycle. Now, mid-2024, is one of those times, as the economic uncertainties are at substantial levels. This note reflects my long history of studying credit cycles dating back to the early 1970s. My current assessment is that the Benign Credit Cycle we have enjoyed since 2010, with the exception of a few months in 2016 and early 2020, ended in 2023. We recently reached an inflection point for an average credit risk scenario. This assessment is based on an analysis of a number of historical indicators over the last 50 years. This conclusion is tempered by the possibility that the U.S. credit picture will continue its heightened risk trend toward a Stressed Scenario by the end of 2024, and combined with a “hard-landing” economic recession, we could witness another financial-credit crisis.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931965","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}
This paper examines the time-varying spillover effects and connectedness between the euro and other EU and non-EU currencies after the end of the sovereign-debt crisis. We employ the Quantile Vector Autoregression connectedness approach using intraday data for seven currencies (the euro, the British pound, the Swiss franc, the Polish zloty, the Hungarian forint, the Czech koruna, and the Norwegian krone) spanning from 1 January 2016 to 30 November 2022. The results indicate that, almost in all quantiles, the currencies of Eastern European Group countries (i.e., Czech Republic, Hungary, and Poland) are net contributors of information spillovers to other currencies, while currencies of non-EU countries (Switzerland, UK, and Norway) are net takers. Further, we find that the euro is the highest transmitter of net information spillovers to all other currencies until 2021. Interestingly, after 2021, the euro changes to net information spillover taker from all other currencies; highlighting that external shocks (e.g., COVID-19, the energy crisis) have significant risk spillover effects on the European currency market. Policymakers and market participants could benefit from knowing which currency drives developments to avoid unexpected consequences.
{"title":"Realized Volatility Spillover Connectedness among the Leading European Currencies after the End of the Sovereign-Debt Crisis: A QVAR Approach","authors":"Michail Nerantzidis, Nikolaos Stoupos, Panayiotis Tzeremes","doi":"10.3390/jrfm17080337","DOIUrl":"https://doi.org/10.3390/jrfm17080337","url":null,"abstract":"This paper examines the time-varying spillover effects and connectedness between the euro and other EU and non-EU currencies after the end of the sovereign-debt crisis. We employ the Quantile Vector Autoregression connectedness approach using intraday data for seven currencies (the euro, the British pound, the Swiss franc, the Polish zloty, the Hungarian forint, the Czech koruna, and the Norwegian krone) spanning from 1 January 2016 to 30 November 2022. The results indicate that, almost in all quantiles, the currencies of Eastern European Group countries (i.e., Czech Republic, Hungary, and Poland) are net contributors of information spillovers to other currencies, while currencies of non-EU countries (Switzerland, UK, and Norway) are net takers. Further, we find that the euro is the highest transmitter of net information spillovers to all other currencies until 2021. Interestingly, after 2021, the euro changes to net information spillover taker from all other currencies; highlighting that external shocks (e.g., COVID-19, the energy crisis) have significant risk spillover effects on the European currency market. Policymakers and market participants could benefit from knowing which currency drives developments to avoid unexpected consequences.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"36 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932058","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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