This study empirically explores the influence of financial development (FD) in an innovation-growth nexus. Specifically, the study considers how, through FD, innovation impacts countries' export products, export values and national incomes. The system Generalized Method of Moments technique and the dynamic common correlated effect estimator are used on data of 57 economies covering the period 2000 to 2019. First, the findings reveal that, on the full sample, FD and its interaction with R&D expenditure have both short- and long-run effects on economic performance, as they both cause increases in export product, export value and national income. However, within the full sample study, the direct impact of FD is more favorable than the indirect effect. Second, within the developed and the developing economies, the study reveals that FD indirectly influences economic performance by improving the relationship between R&D expenditures and export products, export values and the national incomes of these groups of economies, both in the short- and the long-run. However, considering the developing economies, the findings show that the indirect influence of FD is more favorable than the direct effect. As a result, this study argues that FD is relevant for improving the relationship between innovation and economic performance, for both developed and developing economies. Policymakers should, therefore, ensure efficiency and stability in their financial sector as they engage in R&D activities in order to be able to harness the export-growth benefits of innovation fully. Moreover, policies that ensure sustainable money supply should be encouraged, especially within the developing economies.
{"title":"Innovation and economic performance: The role of financial development","authors":"Gigamon Joseph Prah","doi":"10.3934/qfe.2022031","DOIUrl":"https://doi.org/10.3934/qfe.2022031","url":null,"abstract":"This study empirically explores the influence of financial development (FD) in an innovation-growth nexus. Specifically, the study considers how, through FD, innovation impacts countries' export products, export values and national incomes. The system Generalized Method of Moments technique and the dynamic common correlated effect estimator are used on data of 57 economies covering the period 2000 to 2019. First, the findings reveal that, on the full sample, FD and its interaction with R&D expenditure have both short- and long-run effects on economic performance, as they both cause increases in export product, export value and national income. However, within the full sample study, the direct impact of FD is more favorable than the indirect effect. Second, within the developed and the developing economies, the study reveals that FD indirectly influences economic performance by improving the relationship between R&D expenditures and export products, export values and the national incomes of these groups of economies, both in the short- and the long-run. However, considering the developing economies, the findings show that the indirect influence of FD is more favorable than the direct effect. As a result, this study argues that FD is relevant for improving the relationship between innovation and economic performance, for both developed and developing economies. Policymakers should, therefore, ensure efficiency and stability in their financial sector as they engage in R&D activities in order to be able to harness the export-growth benefits of innovation fully. Moreover, policies that ensure sustainable money supply should be encouraged, especially within the developing economies.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":"1 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70230858","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}
Shipping freight rates fluctuation is considered as one of the most important risk factors that participants face in the tanker shipping market (ship-owners, charterers, traders, hedge funds, banks and other financial institutions) in order to watch its evolution. This study examines freight rates for two of the most popular clean and dirty tanker routes; TC2 and TD3 from 22 May 2007 to 21 September 2015, using daily spot and future prices. The full data sample is divided into two sub periods, from 22 May 2007 to 13 August 2013 (in sample period) on which the model estimation section is based and from 14 August 2013 to 21 September 2015 (out of sample period) over which the Value at Risk is measured and backtesting process was performed. In all cases tested, there are observed high peaks and fat tails in all distributions. We apply a range of VaR models (parametric and non-parametric) in order to estimate the risk of the returns of TC2 route and TD3 route for spot, one month and three months future market. Backtesting tools are implemented in order to find the best fit model in terms of economic and statistical accuracy. Our empirical analysis concludes that the best fit models used for mitigating risk are simple GARCH model and non-parametric model. The above outcome seems to be valid a) for spot returns as well as for future returns and b) for short and long positions. In addition to the aforementioned conclusions, it is observed high freight rate risk at all routes. Our results are useful for risk management purposes for all the tanker shipping market participants and derivatives' counterparties.
{"title":"VaR as a mitigating risk tool in the maritime sector: An empirical approach on freight rates","authors":"Basdekis Charalampos, Katsampoxakis Ioannis, Gkolfinopoulos Alexandros","doi":"10.3934/qfe.2022007","DOIUrl":"https://doi.org/10.3934/qfe.2022007","url":null,"abstract":"Shipping freight rates fluctuation is considered as one of the most important risk factors that participants face in the tanker shipping market (ship-owners, charterers, traders, hedge funds, banks and other financial institutions) in order to watch its evolution. This study examines freight rates for two of the most popular clean and dirty tanker routes; TC2 and TD3 from 22 May 2007 to 21 September 2015, using daily spot and future prices. The full data sample is divided into two sub periods, from 22 May 2007 to 13 August 2013 (in sample period) on which the model estimation section is based and from 14 August 2013 to 21 September 2015 (out of sample period) over which the Value at Risk is measured and backtesting process was performed. In all cases tested, there are observed high peaks and fat tails in all distributions. We apply a range of VaR models (parametric and non-parametric) in order to estimate the risk of the returns of TC2 route and TD3 route for spot, one month and three months future market. Backtesting tools are implemented in order to find the best fit model in terms of economic and statistical accuracy. Our empirical analysis concludes that the best fit models used for mitigating risk are simple GARCH model and non-parametric model. The above outcome seems to be valid a) for spot returns as well as for future returns and b) for short and long positions. In addition to the aforementioned conclusions, it is observed high freight rate risk at all routes. Our results are useful for risk management purposes for all the tanker shipping market participants and derivatives' counterparties.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":"45 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70230087","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 study investigated the All Share Index (ALSI) returns and six different risk measures of the South African market for the sample period from 17 March 2000 to 17 March 2022. The risk measures analyzed were standard deviation (SD), absolute deviation (AD), lower semi absolute deviation (LSAD), lower semivariance (LSV), realized variance (RV) and the bias-adjusted realized variance (ARV). This study made an innovative contribution on a methodological and practical level, by being the first study to extend from the novel Bayesian approach by Jensen and Maheu (2018) to methods by Karabatsos (2017)—density regression, quantile regression and survival analysis. The extensions provided a full representation of the return distribution in relation to risk, through graphical analysis, producing novel insight into the risk-return topic. The most novel and innovative contribution of this study was the application of survival analysis which analyzed the "life" and "death" of the risk-return relationship. From the density regression, this study found that the chance of investors earning a superior return was substantial and that the probability of excess returns increased over time. From quantile regression, results revealed that returns have a negative relationship with the majority of the risk measures—SD, AD, LSAD and RV. However, a positive risk-return relationship was found by LSV and the ARV, with the latter having the steepest slope. Results were the most pronounced for the ARV, especially for the survival analysis. While ARV earned the highest returns, it had the shortest lifespan, which can be attributed to the volatile nature of the South African market. Thus, investors that seek short-term high-earning returns would examine ARV followed by LSV, whereas the remaining risk measures can be used for other purposes, such as diversification purposes or short selling.
{"title":"An innovative extended Bayesian analysis of the relationship between returns and different risk measures in South Africa","authors":"Nitesha Dwarika","doi":"10.3934/qfe.2022025","DOIUrl":"https://doi.org/10.3934/qfe.2022025","url":null,"abstract":"This study investigated the All Share Index (ALSI) returns and six different risk measures of the South African market for the sample period from 17 March 2000 to 17 March 2022. The risk measures analyzed were standard deviation (SD), absolute deviation (AD), lower semi absolute deviation (LSAD), lower semivariance (LSV), realized variance (RV) and the bias-adjusted realized variance (ARV). This study made an innovative contribution on a methodological and practical level, by being the first study to extend from the novel Bayesian approach by Jensen and Maheu (2018) to methods by Karabatsos (2017)—density regression, quantile regression and survival analysis. The extensions provided a full representation of the return distribution in relation to risk, through graphical analysis, producing novel insight into the risk-return topic. The most novel and innovative contribution of this study was the application of survival analysis which analyzed the \"life\" and \"death\" of the risk-return relationship. From the density regression, this study found that the chance of investors earning a superior return was substantial and that the probability of excess returns increased over time. From quantile regression, results revealed that returns have a negative relationship with the majority of the risk measures—SD, AD, LSAD and RV. However, a positive risk-return relationship was found by LSV and the ARV, with the latter having the steepest slope. Results were the most pronounced for the ARV, especially for the survival analysis. While ARV earned the highest returns, it had the shortest lifespan, which can be attributed to the volatile nature of the South African market. Thus, investors that seek short-term high-earning returns would examine ARV followed by LSV, whereas the remaining risk measures can be used for other purposes, such as diversification purposes or short selling.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":"1 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70230164","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 quantitatively reveals the meaning of structural breaks for risk management by analyzing US and major European banking sector stocks. Applying newly extended Glosten-Jagannathan-Runkle generalized autoregressive conditional heteroscedasticity models, we supply the following new evidence. First, we find that incorporating structural breaks is always effective in estimating banking stock volatilities. Second, we clarify that structural breaks partially explain the tail fatness of banking stock returns. Third, we find that when incorporating structural breaks, the estimated volatilities more accurately capture their downside risk, proving that structural breaks matter for risk management. Fourth, our news impact curve and model parameter analyses also uncover that when incorporating structural breaks, the asymmetry in volatility responses to return shocks is more accurately captured. This proves why the estimated volatilities by incorporating structural breaks better explain downside risk. In addition, we further reveal that the estimated volatilities obtained through incorporating structural breaks increase sharply during momentous events such as the Lehman crisis, the European debt crisis, Brexit, and the recent COVID-19 crisis. Moreover, we also clarify that the volatility spreads between models with and without structural breaks rise during the Lehman and COVID-19 crises. Finally, based on our findings, we derive many significant and beneficial interpretations, implications, and innovative views for risk management using artificial intelligence in the post-COVID-19 era.
{"title":"The meaning of structural breaks for risk management: new evidence, mechanisms, and innovative views for the post-COVID-19 era","authors":"Chikashi Tsuji","doi":"10.3934/qfe.2022012","DOIUrl":"https://doi.org/10.3934/qfe.2022012","url":null,"abstract":"This paper quantitatively reveals the meaning of structural breaks for risk management by analyzing US and major European banking sector stocks. Applying newly extended Glosten-Jagannathan-Runkle generalized autoregressive conditional heteroscedasticity models, we supply the following new evidence. First, we find that incorporating structural breaks is always effective in estimating banking stock volatilities. Second, we clarify that structural breaks partially explain the tail fatness of banking stock returns. Third, we find that when incorporating structural breaks, the estimated volatilities more accurately capture their downside risk, proving that structural breaks matter for risk management. Fourth, our news impact curve and model parameter analyses also uncover that when incorporating structural breaks, the asymmetry in volatility responses to return shocks is more accurately captured. This proves why the estimated volatilities by incorporating structural breaks better explain downside risk. In addition, we further reveal that the estimated volatilities obtained through incorporating structural breaks increase sharply during momentous events such as the Lehman crisis, the European debt crisis, Brexit, and the recent COVID-19 crisis. Moreover, we also clarify that the volatility spreads between models with and without structural breaks rise during the Lehman and COVID-19 crises. Finally, based on our findings, we derive many significant and beneficial interpretations, implications, and innovative views for risk management using artificial intelligence in the post-COVID-19 era.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":"1 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70230486","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}
An-Hsing Chang, Li-Kai Yang, R. Tsaih, Shih-Kuei Lin
In this study, we constructed the credit-scoring model of P2P loans by using several machine learning and artificial neural network (ANN) methods, including logistic regression (LR), a support vector machine, a decision tree, random forest, XGBoost, LightGBM and 2-layer neural networks. This study explores several hyperparameter settings for each method by performing a grid search and cross-validation to get the most suitable credit-scoring model in terms of training time and test performance. In this study, we get and clean the open P2P loan data from Lending Club with feature engineering concepts. In order to find significant default factors, we used an XGBoost method to pre-train all data and get the feature importance. The 16 selected features can provide economic implications for research about default prediction in P2P loans. Besides, the empirical result shows that gradient-boosting decision tree methods, including XGBoost and LightGBM, outperform ANN and LR methods, which are commonly used for traditional credit scoring. Among all of the methods, XGBoost performed the best.
{"title":"Machine learning and artificial neural networks to construct P2P lending credit-scoring model: A case using Lending Club data","authors":"An-Hsing Chang, Li-Kai Yang, R. Tsaih, Shih-Kuei Lin","doi":"10.3934/qfe.2022013","DOIUrl":"https://doi.org/10.3934/qfe.2022013","url":null,"abstract":"In this study, we constructed the credit-scoring model of P2P loans by using several machine learning and artificial neural network (ANN) methods, including logistic regression (LR), a support vector machine, a decision tree, random forest, XGBoost, LightGBM and 2-layer neural networks. This study explores several hyperparameter settings for each method by performing a grid search and cross-validation to get the most suitable credit-scoring model in terms of training time and test performance. In this study, we get and clean the open P2P loan data from Lending Club with feature engineering concepts. In order to find significant default factors, we used an XGBoost method to pre-train all data and get the feature importance. The 16 selected features can provide economic implications for research about default prediction in P2P loans. Besides, the empirical result shows that gradient-boosting decision tree methods, including XGBoost and LightGBM, outperform ANN and LR methods, which are commonly used for traditional credit scoring. Among all of the methods, XGBoost performed the best.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":"1 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70230530","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}
The Common Monetary Area (CMA) agreement has effectively granted the South African government sole discretion over monetary policy and implementation in the region. The effectiveness of this arrangement has long been under discussion given the heterogeneity of member countries. This paper uses a structural vector autoregressive (SVAR) to examine the efficacy of the interest rate channel in the CMA. Specifically, our analysis uses data from 2000M1-2018M12 to examine how economic output, inflation, money supply, domestic credit, and lending rate spread for each member country respond to shocks in the South African repo rate. The main findings indicate that a positive shock to the South African repo rate has a statistically significant negative impact on economic output and a positive effect on inflation at the 10 percent level for all countries in the CMA. The results also show that money supply, domestic credit, and lending rate spread respond asymmetrically across members countries.
{"title":"Efficacy of monetary policy in a currency union? Evidence from Southern Africa's Common Monetary Area","authors":"Bonang N. Seoela","doi":"10.3934/qfe.2022002","DOIUrl":"https://doi.org/10.3934/qfe.2022002","url":null,"abstract":"The Common Monetary Area (CMA) agreement has effectively granted the South African government sole discretion over monetary policy and implementation in the region. The effectiveness of this arrangement has long been under discussion given the heterogeneity of member countries. This paper uses a structural vector autoregressive (SVAR) to examine the efficacy of the interest rate channel in the CMA. Specifically, our analysis uses data from 2000M1-2018M12 to examine how economic output, inflation, money supply, domestic credit, and lending rate spread for each member country respond to shocks in the South African repo rate. The main findings indicate that a positive shock to the South African repo rate has a statistically significant negative impact on economic output and a positive effect on inflation at the 10 percent level for all countries in the CMA. The results also show that money supply, domestic credit, and lending rate spread respond asymmetrically across members countries.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":"1 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70229909","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}
The aim of this paper is to examine exchange rate volatility using GARCH models with a new innovation distribution, the Normal Tempered Stable. We estimated daily exchange rate volatility using different distributions (Normal, Student, NIG) in order to specify the performed model. In addition, a forecasting analysis is performed to check which distribution reveals the best out-of-sample results. We found that the estimated parameters of GARCH-NTS model outperform the GARCH-N and GARCH-t ones for all currencies. Besides, we asserted that GARCH-NTS and EGARCH-NTS are the preferred models in terms of out-of sample forecasting accuracy. Our results indicating the performance of GARCH models with NTS distribution contribute to increase the accuracy of risk measures which is very important for international traders and investors.
{"title":"Modeling exchange rate volatility: application of GARCH models with a Normal Tempered Stable distribution","authors":"Sahar Charfi, Farouk Mselmi","doi":"10.3934/qfe.2022009","DOIUrl":"https://doi.org/10.3934/qfe.2022009","url":null,"abstract":"The aim of this paper is to examine exchange rate volatility using GARCH models with a new innovation distribution, the Normal Tempered Stable. We estimated daily exchange rate volatility using different distributions (Normal, Student, NIG) in order to specify the performed model. In addition, a forecasting analysis is performed to check which distribution reveals the best out-of-sample results. We found that the estimated parameters of GARCH-NTS model outperform the GARCH-N and GARCH-t ones for all currencies. Besides, we asserted that GARCH-NTS and EGARCH-NTS are the preferred models in terms of out-of sample forecasting accuracy. Our results indicating the performance of GARCH models with NTS distribution contribute to increase the accuracy of risk measures which is very important for international traders and investors.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":"1 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70230186","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}
Mohammad Abdullah, M. A. H. Chowdhury, Uttam Karmaker, Md. Habibur Rahman Fuszder, Md. Asif Shahriar
This study examines the role of political stability in a firm's financial performance in Bangladesh. By considering 139 listed companies from the Dhaka Stock Exchange over the period of 2011 to 2020, we applied a dynamic generalized method of moments (GMM), dynamic quantile regression and dynamic threshold regression. The empirical evidence of this study shows a significant positive impact of political stability on Bangladeshi firms' financial performances. Using dynamic quantile regression, we found a positive impact of political stability in the firms' upper and lower quantiles. Additionally, we found the threshold effect of political stability on the firms' performance to have a score of 13.680. This study contributes theoretically and empirically by examining the importance of political stability on financial performance. For the investors, policymakers and other stakeholders, this study provides evidence of a threshold of political stability on a firm's financial performance.
{"title":"Role of the dynamics of political stability in firm performance: Evidence from Bangladesh","authors":"Mohammad Abdullah, M. A. H. Chowdhury, Uttam Karmaker, Md. Habibur Rahman Fuszder, Md. Asif Shahriar","doi":"10.3934/qfe.2022022","DOIUrl":"https://doi.org/10.3934/qfe.2022022","url":null,"abstract":"This study examines the role of political stability in a firm's financial performance in Bangladesh. By considering 139 listed companies from the Dhaka Stock Exchange over the period of 2011 to 2020, we applied a dynamic generalized method of moments (GMM), dynamic quantile regression and dynamic threshold regression. The empirical evidence of this study shows a significant positive impact of political stability on Bangladeshi firms' financial performances. Using dynamic quantile regression, we found a positive impact of political stability in the firms' upper and lower quantiles. Additionally, we found the threshold effect of political stability on the firms' performance to have a score of 13.680. This study contributes theoretically and empirically by examining the importance of political stability on financial performance. For the investors, policymakers and other stakeholders, this study provides evidence of a threshold of political stability on a firm's financial performance.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":"1 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70230625","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 study examined the nexus between foreign direct investment (FDI), financial development, and sustainable economic growth in Sudan during the period of the structural adjustment program and the full Islamization of the banking and financial system that took place in the 1980s. The research provides a comprehensive analysis using the most recent time series secondary data from 1990 to 2020 and the study employed co-integration, Granger causality, and VAR error correction technique to estimate the models, to clarify the claimed relationship between FDI and its effect on the financial sector and subsequently attending a sustainable economic development in Sudan. In this research, Augmented Dickey-Fuller (ADF) unit root tests are applied to test the stationarity of data and the data was found stationary at first difference. The results of the ARDL bounds showed the existence of a long-term relationship between the FDI and other independent variables but the short-term showed otherwise. The Granger causality test implies that the past values of FDI don't significantly contribute to the prediction of sustainable economic growth. Also, results show that there's evidence of observed causality running from the country's trade openness and the financial sector's development. The implication of these results shows there is a complementary relationship between sustainable economic growth and both financial development and trade openness in the short run. Interestingly, the findings of the study show that the effect of financial development on economic growth is further enhanced by the inflows of FDI.
{"title":"Nexus among foreign direct investment, financial development, and sustainable economic growth: Empirical aspects from Sudan","authors":"Mustafa Hassan Mohammad Adam","doi":"10.3934/qfe.2022028","DOIUrl":"https://doi.org/10.3934/qfe.2022028","url":null,"abstract":"This study examined the nexus between foreign direct investment (FDI), financial development, and sustainable economic growth in Sudan during the period of the structural adjustment program and the full Islamization of the banking and financial system that took place in the 1980s. The research provides a comprehensive analysis using the most recent time series secondary data from 1990 to 2020 and the study employed co-integration, Granger causality, and VAR error correction technique to estimate the models, to clarify the claimed relationship between FDI and its effect on the financial sector and subsequently attending a sustainable economic development in Sudan. In this research, Augmented Dickey-Fuller (ADF) unit root tests are applied to test the stationarity of data and the data was found stationary at first difference. The results of the ARDL bounds showed the existence of a long-term relationship between the FDI and other independent variables but the short-term showed otherwise. The Granger causality test implies that the past values of FDI don't significantly contribute to the prediction of sustainable economic growth. Also, results show that there's evidence of observed causality running from the country's trade openness and the financial sector's development. The implication of these results shows there is a complementary relationship between sustainable economic growth and both financial development and trade openness in the short run. Interestingly, the findings of the study show that the effect of financial development on economic growth is further enhanced by the inflows of FDI.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":"1 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70230659","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}
Longevity risk is the risk that members of a given population will live longer than expected. When it occurs, pension providers may have to pay pensions for longer than expected, significantly increasing their costs. While this risk is being adequately studied using the national mortality data provided by the Human Mortality Database, relatively few studies exist that analyse sub-national data. This manuscript proposes a comparative study of some stochastic mortality models to measure the longevity risk on Italian mortality data at the regional level. In particular, the use of the Lee-Carter and Li-Lee models is explored. The models are compared in fitting quality, forecasting accuracy and complexity. Numerical experiments and applications to immediate life annuity evaluation are presented.
{"title":"Longevity risk analysis: applications to the Italian regional data","authors":"Salvatore Scognamiglio","doi":"10.3934/qfe.2022006","DOIUrl":"https://doi.org/10.3934/qfe.2022006","url":null,"abstract":"Longevity risk is the risk that members of a given population will live longer than expected. When it occurs, pension providers may have to pay pensions for longer than expected, significantly increasing their costs. While this risk is being adequately studied using the national mortality data provided by the Human Mortality Database, relatively few studies exist that analyse sub-national data. This manuscript proposes a comparative study of some stochastic mortality models to measure the longevity risk on Italian mortality data at the regional level. In particular, the use of the Lee-Carter and Li-Lee models is explored. The models are compared in fitting quality, forecasting accuracy and complexity. Numerical experiments and applications to immediate life annuity evaluation are presented.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":"1 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70229885","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：