Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. This paper evaluates the volatility of Bitcoin returns using three GARCH models (sGARCH, iGARCH, and tGARCH). The new development allows for the modeling of volatility clustering effects, the leptokurtic and the skewed distribution in the return series of Bitcoin. Comparative to the Students’t-distribution and the Generalized error distribution, the Normal Inverse Gaussian (NIG) distribution captured adequately the leptokurtic and skewness in all the GARCH models. The tGARCH model was the best model as it described the asymmetric occurrence of shocks in the Bitcoin market. That is, the response of investors to the same amount of good and bad news are distinct. From the empirical results, it can be concluded that tGARCH-NIG was the best model to estimate the volatility in the return series of Bitcoin. Generally, it would be optimal to use the NIG distribution in GARCH type models since time series of most cryptocurrency are leptokurtic.
{"title":"Modelling the volatility of Bitcoin returns using GARCH models","authors":"S. Gyamerah","doi":"10.3934/qfe.2019.4.739","DOIUrl":"https://doi.org/10.3934/qfe.2019.4.739","url":null,"abstract":"Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. This paper evaluates the volatility of Bitcoin returns using three GARCH models (sGARCH, iGARCH, and tGARCH). The new development allows for the modeling of volatility clustering effects, the leptokurtic and the skewed distribution in the return series of Bitcoin. Comparative to the Students’t-distribution and the Generalized error distribution, the Normal Inverse Gaussian (NIG) distribution captured adequately the leptokurtic and skewness in all the GARCH models. The tGARCH model was the best model as it described the asymmetric occurrence of shocks in the Bitcoin market. That is, the response of investors to the same amount of good and bad news are distinct. From the empirical results, it can be concluded that tGARCH-NIG was the best model to estimate the volatility in the return series of Bitcoin. Generally, it would be optimal to use the NIG distribution in GARCH type models since time series of most cryptocurrency are leptokurtic.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48319653","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 primary purpose of this study is to use a non-linear fiscal reaction function approach to investigate the fiscal fatigue phenomenon in Turkey. Quarterly data for the years of 2001 through 2018 were analyzed using fully modified ordinary least squares (FM-OLS) and threshold models. The results from both the FM-OLS and threshold models provide evidence about the presence of the fiscal fatigue phenomenon in Turkey. The primary balance has minimum and maximum values at debt levels of about 30% and 80%, respectively. The primary balance begins to decrease after the maximum value is achieved; negative values are reached at a debt level of about 110%. Therefore, it is recommended that Turkey focus on fiscal sustainability and the risk of external shocks by implementing a fiscal discipline policy. In addition, a primary surplus targeting principle should be implemented, to carry out the sustainability of the liabilities and the fiscal targets.
{"title":"Testing the fiscal fatigue phenomenon in Turkey using a long-run non-linear fiscal reaction function approach","authors":"Sevda Akar","doi":"10.3934/qfe.2019.4.645","DOIUrl":"https://doi.org/10.3934/qfe.2019.4.645","url":null,"abstract":"The primary purpose of this study is to use a non-linear fiscal reaction function approach to investigate the fiscal fatigue phenomenon in Turkey. Quarterly data for the years of 2001 through 2018 were analyzed using fully modified ordinary least squares (FM-OLS) and threshold models. The results from both the FM-OLS and threshold models provide evidence about the presence of the fiscal fatigue phenomenon in Turkey. The primary balance has minimum and maximum values at debt levels of about 30% and 80%, respectively. The primary balance begins to decrease after the maximum value is achieved; negative values are reached at a debt level of about 110%. Therefore, it is recommended that Turkey focus on fiscal sustainability and the risk of external shocks by implementing a fiscal discipline policy. In addition, a primary surplus targeting principle should be implemented, to carry out the sustainability of the liabilities and the fiscal targets.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2019-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41554539","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 real options approach has been supporting investment decision-making processes for years. However, on competitive markets, the real options games approach is the more suitable way. In this article, a real options game subject to analysis is the situation in which two companies with different market share are exploring an opportunity to implement a new investment project. It is known that competition on the market reduces the scope of benefits a company can gain whilst implementing the project. In this paper, we show that this reduction can be mitigated by taking into account payoff transfer designated as a bargaining solution. We discuss three main types of games between companies that we can observe on the market; then we analyze their bargaining solutions, and finally—come up with recommendations to companies. A firm that dominates its respective market usually benefits by implementing the most advantageous strategy, but in certain situations it should pay special attention to its weaker competitor’s opportunities and try to anticipate its movements. In the paper, we show that with high project risk and significant asymmetry in the firms’ market share, a weaker company may still hold all the cards.
{"title":"Real options bargaining games","authors":"E. Rychłowska‑Musiał","doi":"10.3934/qfe.2019.4.624","DOIUrl":"https://doi.org/10.3934/qfe.2019.4.624","url":null,"abstract":"The real options approach has been supporting investment decision-making processes for years. However, on competitive markets, the real options games approach is the more suitable way. In this article, a real options game subject to analysis is the situation in which two companies with different market share are exploring an opportunity to implement a new investment project. It is known that competition on the market reduces the scope of benefits a company can gain whilst implementing the project. In this paper, we show that this reduction can be mitigated by taking into account payoff transfer designated as a bargaining solution. We discuss three main types of games between companies that we can observe on the market; then we analyze their bargaining solutions, and finally—come up with recommendations to companies. A firm that dominates its respective market usually benefits by implementing the most advantageous strategy, but in certain situations it should pay special attention to its weaker competitor’s opportunities and try to anticipate its movements. In the paper, we show that with high project risk and significant asymmetry in the firms’ market share, a weaker company may still hold all the cards.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2019-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70228654","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 article aims to develop mathematical methods and algorithms that automatically build nonlinear models of planning and management of economic objects based on the use of empirical samples (observations). We call the relevant new information technology "Building Optimization Models from Data (BOMD)". The offered technology BOMD allows to obtain an objective control models that reflect the real economic processes. This is its main advantage over commonly employed subjective approach to management. To solve the problems posed in the article, the methods of artificial intelligence were used, in particular, the training of neural networks and construction of decision trees. If the learning sample contains simultaneously the values of the objective function and the values of characteristic function of constraints, it is proposed to use an approach based on the training of two neural networks: NN1 — for the synthesis of the objective function and NN2 — for the synthesis of the approximating characteristic function of constraints (instead of a neural network NN2, a decision tree can be used). The solution of the problem presented by such synthesized neural model may end up finding, generally speaking, a local conditional extremum. To find the global extremum of the multiextremal neural objective function, a heuristic algorithm based on a preliminary classification of the search area by using the decision tree is developed. Presented in the paper approach to an extraction of conditionally optimization model from the data for the case when there is no information on the points not belonging to the set of admissible solutions is fundamentally novel. In this case, a heuristic algorithm for approximating the region of admissible solutions based on the allocation of regular (non-random) empty segments of the search area is developed.
{"title":"BOMD: Building Optimization Models from Data (Neural Networks based Approach)","authors":"V. Donskoy","doi":"10.3934/qfe.2019.4.608","DOIUrl":"https://doi.org/10.3934/qfe.2019.4.608","url":null,"abstract":"This article aims to develop mathematical methods and algorithms that automatically build nonlinear models of planning and management of economic objects based on the use of empirical samples (observations). We call the relevant new information technology \"Building Optimization Models from Data (BOMD)\". The offered technology BOMD allows to obtain an objective control models that reflect the real economic processes. This is its main advantage over commonly employed subjective approach to management. To solve the problems posed in the article, the methods of artificial intelligence were used, in particular, the training of neural networks and construction of decision trees. If the learning sample contains simultaneously the values of the objective function and the values of characteristic function of constraints, it is proposed to use an approach based on the training of two neural networks: NN1 — for the synthesis of the objective function and NN2 — for the synthesis of the approximating characteristic function of constraints (instead of a neural network NN2, a decision tree can be used). The solution of the problem presented by such synthesized neural model may end up finding, generally speaking, a local conditional extremum. To find the global extremum of the multiextremal neural objective function, a heuristic algorithm based on a preliminary classification of the search area by using the decision tree is developed. Presented in the paper approach to an extraction of conditionally optimization model from the data for the case when there is no information on the points not belonging to the set of admissible solutions is fundamentally novel. In this case, a heuristic algorithm for approximating the region of admissible solutions based on the allocation of regular (non-random) empty segments of the search area is developed.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2019-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41897219","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}
Although, the Small and Medium-sized enterprises are the most numerous members in clusters, many of their representatives do not know benefits from this type of cooperation and they perceive it as a risky in general. Whereby, the clusters present one form of sustainable development of SMEs business. The focus of this paper is to provide an analytical framework in which the SMEs' perception of selected risk indicators is investigated by characterizing the financial risks related to cluster cooperation. While risk indicators are presented through five main categories of financial risks. To gain the main aim of this paper following research methods were used:Chi square test to compare statistical significant differences between the stated groups of respondents, Z-score to investigate the statistically significant differences in individual responses, Pareto analysis to determine the most important risk indicators and Key risk matrix to assess the level of risk indicators. The results have showed, that most important risk indicators are from category of business risks and they related with main issues of cluster cooperation:human factor failure, legal risk and risk related to lose of own reputation. These results highlighted the importance of financial risks that may be necessary for SMEs, other stakeholders and policy makers to overcome the barriers in development of cluster cooperation. The added value of this paper is the comparison of SMEs' perception of risk indicators from several points of view and final evaluation is connected and compared with the perception of those SMEs that have or had experience with cluster cooperation.
{"title":"The SMEs perception of financial risks in the context of cluster cooperation","authors":"Katarína Havierniková, M. Kordoš","doi":"10.3934/qfe.2019.3.586","DOIUrl":"https://doi.org/10.3934/qfe.2019.3.586","url":null,"abstract":"Although, the Small and Medium-sized enterprises are the most numerous members in clusters, many of their representatives do not know benefits from this type of cooperation and they perceive it as a risky in general. Whereby, the clusters present one form of sustainable development of SMEs business. The focus of this paper is to provide an analytical framework in which the SMEs' perception of selected risk indicators is investigated by characterizing the financial risks related to cluster cooperation. While risk indicators are presented through five main categories of financial risks. To gain the main aim of this paper following research methods were used:Chi square test to compare statistical significant differences between the stated groups of respondents, Z-score to investigate the statistically significant differences in individual responses, Pareto analysis to determine the most important risk indicators and Key risk matrix to assess the level of risk indicators. The results have showed, that most important risk indicators are from category of business risks and they related with main issues of cluster cooperation:human factor failure, legal risk and risk related to lose of own reputation. These results highlighted the importance of financial risks that may be necessary for SMEs, other stakeholders and policy makers to overcome the barriers in development of cluster cooperation. The added value of this paper is the comparison of SMEs' perception of risk indicators from several points of view and final evaluation is connected and compared with the perception of those SMEs that have or had experience with cluster cooperation.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2019-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41602826","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}
In this paper, we use a generalized dynamic factor model to reconstruct the global economic policy uncertainty index developed by Davis (2016), and we investigate the dynamic dependence structure between global and national economic policy uncertainty using the time-varying copula approach. Based on this novel index, we find that global economic policy uncertainty has overall experienced a "Low-High-Low" trend during the period April 2003 to November 2018, and there are spikes in connection with notable political events and developments around the world. The results also suggest that there generally exists positive dependence between global and national economic policy uncertainty, and the magnitude of dependency in developed countries is much higher than that in developing countries. In addition, the degree of international economic policy incoordination has increased significantly after the 2008-2009 global financial crisis.
{"title":"Reconstruction and dynamic dependence analysis of global economic policy uncertainty","authors":"Yue Liu, Yuhang Zheng, B. Drakeford","doi":"10.3934/qfe.2019.3.550","DOIUrl":"https://doi.org/10.3934/qfe.2019.3.550","url":null,"abstract":"In this paper, we use a generalized dynamic factor model to reconstruct the global economic policy uncertainty index developed by Davis (2016), and we investigate the dynamic dependence structure between global and national economic policy uncertainty using the time-varying copula approach. Based on this novel index, we find that global economic policy uncertainty has overall experienced a \"Low-High-Low\" trend during the period April 2003 to November 2018, and there are spikes in connection with notable political events and developments around the world. The results also suggest that there generally exists positive dependence between global and national economic policy uncertainty, and the magnitude of dependency in developed countries is much higher than that in developing countries. In addition, the degree of international economic policy incoordination has increased significantly after the 2008-2009 global financial crisis.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2019-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46308332","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 present paper investigates the relationship between investor herding behavior and stock return under short-sale price restrictions. Detailed intra-day orders are applied to calculate herding measurements before and after a staged lifting of short-sale restrictions on May 16th, 2005 and September 23rd, 2013 in Taiwan. The former targeted the constituent stocks of the Taiwan 50 Index only, while the latter targeted all listed firms on the Taiwan Stock Exchange. Using the Chow test and the dynamic structural change point test to confirm the effect of ending these restrictions, the results found that short-sale restrictions increased individual herding behavior in the context of the stocks of small firms, especially those stocks in the low quantile of stock returns. Conversely, herding behavior was not shown to significantly impact the stock returns of large firms either before or after the end of short-sale price constraints for either individual or institutional investors.
{"title":"The relationship between herding behavior and firm size before and after the elimination of short-sale price restrictions","authors":"Chiao‐yi Chang, F. Shie, Shu-Ling Yang","doi":"10.3934/qfe.2019.3.526","DOIUrl":"https://doi.org/10.3934/qfe.2019.3.526","url":null,"abstract":"The present paper investigates the relationship between investor herding behavior and stock return under short-sale price restrictions. Detailed intra-day orders are applied to calculate herding measurements before and after a staged lifting of short-sale restrictions on May 16th, 2005 and September 23rd, 2013 in Taiwan. The former targeted the constituent stocks of the Taiwan 50 Index only, while the latter targeted all listed firms on the Taiwan Stock Exchange. Using the Chow test and the dynamic structural change point test to confirm the effect of ending these restrictions, the results found that short-sale restrictions increased individual herding behavior in the context of the stocks of small firms, especially those stocks in the low quantile of stock returns. Conversely, herding behavior was not shown to significantly impact the stock returns of large firms either before or after the end of short-sale price constraints for either individual or institutional investors.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45790383","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 present study examines the impact of financial development on economic growth in India by using Auto-Regressive Distributed Lag (ARDL) model and the Generalized Method of Moment (GMM) model over 15 years from June 2003 to February 2018. The results of the study indicate that financial development has a positive and significant impact on economic progression both in short run and long run in India. The CUSUM test confirms the long-run stability relationship among them. Therefore, the study proposed that financial sector deepening should push ahead to augment economic growth of India. The outcomes of the study contribute to understanding the factors, which determine the economic and financial development competitive position. To foster economic and financial development, the suitable and harmonizing action required by both firms and policymakers. This study has a substantial impact on global and institutional investors to make better investment decisions in India.
{"title":"Does measure of financial development matter for economic growth in India?","authors":"N. Tripathy","doi":"10.3934/QFE.2019.3.508","DOIUrl":"https://doi.org/10.3934/QFE.2019.3.508","url":null,"abstract":"The present study examines the impact of financial development on economic growth in India by using Auto-Regressive Distributed Lag (ARDL) model and the Generalized Method of Moment (GMM) model over 15 years from June 2003 to February 2018. The results of the study indicate that financial development has a positive and significant impact on economic progression both in short run and long run in India. The CUSUM test confirms the long-run stability relationship among them. Therefore, the study proposed that financial sector deepening should push ahead to augment economic growth of India. The outcomes of the study contribute to understanding the factors, which determine the economic and financial development competitive position. To foster economic and financial development, the suitable and harmonizing action required by both firms and policymakers. This study has a substantial impact on global and institutional investors to make better investment decisions in India.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43283598","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 paper proposes to treat financial models using techniques of quantum mechanics. The methodology relies on the Dirac matrix formalism and the Feynman path integral approach. This leads us to reexamine in this framework the classical option pricing models of Cox-Ross-Rubinstein and Black-Scholes. Moreover, financial data are classified with respect to the spectrum of a certain observable and then analyzed to identify price jumps using supervised machine learning tools.
{"title":"Quantum option pricing and data analysis","authors":"Wenyan Hao, C. Lefèvre, Muhsin Tamturk, S. Utev","doi":"10.3934/QFE.2019.3.490","DOIUrl":"https://doi.org/10.3934/QFE.2019.3.490","url":null,"abstract":"The paper proposes to treat financial models using techniques of quantum mechanics. The methodology relies on the Dirac matrix formalism and the Feynman path integral approach. This leads us to reexamine in this framework the classical option pricing models of Cox-Ross-Rubinstein and Black-Scholes. Moreover, financial data are classified with respect to the spectrum of a certain observable and then analyzed to identify price jumps using supervised machine learning tools.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2019-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45050041","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}
N. Wang, I. Ozodiegwu, Shaoqing Gong, Kesheng Wang, Xin Xie
This study aimed to estimate the prevalence of uninsurance among California adults and Asian Americans, and to examine the associations of social-behavioral variables with uninsurance. A total of 24,136 adults (aged 18–64) including 2,060 Asian Americans were selected from the combined 2013–2014 California Health Interview Survey. Weighted univariate and multivariate logistic regression analyses were used to estimate the associations of potential factors with uninsurance. To evaluate the relationship of independent variables, the oblique principal component cluster analysis (OPCCA) was used to classify 9 variables into disjoint clusters. For Whites, African Americans, Latinos, and Asians, the prevalence of uninsurance was 8.5%, 10.3%, 24.7%, and 12.6%, respectively. Among Asians, the prevalence of uninsurance was 15.5%, 9.2%, 6.2%, 20.8% and 12.1% for Chinese, Filipinos, Japanese, Koreans, and Vietnamese, respectively. In the whole sample, multivariate logistic regression analysis revealed that being male, non-citizen, lower education, higher poverty, and current smoking were associated with uninsurance. Among Asians, compared to Koreans, being Filipinos and Vietnamese were associated with lower odds of being uninsured; meanwhile being male, non-citizen, lower education, and higher poverty were significantly associated with increased odds of uninsurance. Elder age groups and current smoking were significantly associated with increased odds of uninsurance in bivariate analysis; however, such associations disappeared after adjusting for other factors. Nine independent variables were divided into 2 clusters, where the variables in the same cluster were strongly correlated but had weak correlations with the variables in the other cluster. In conclusion, there are differences in the prevalence of uninsurance between Asians and Whites, and among Asian subgroups. Being male, non-citizen, lower education, higher poverty and current smoking were positively significantly associated with uninsurance.
{"title":"Multivariate analyses of social-behavioral factors with health insurance coverage among Asian Americans in California","authors":"N. Wang, I. Ozodiegwu, Shaoqing Gong, Kesheng Wang, Xin Xie","doi":"10.3934/QFE.2019.3.473","DOIUrl":"https://doi.org/10.3934/QFE.2019.3.473","url":null,"abstract":"This study aimed to estimate the prevalence of uninsurance among California adults and Asian Americans, and to examine the associations of social-behavioral variables with uninsurance. A total of 24,136 adults (aged 18–64) including 2,060 Asian Americans were selected from the combined 2013–2014 California Health Interview Survey. Weighted univariate and multivariate logistic regression analyses were used to estimate the associations of potential factors with uninsurance. To evaluate the relationship of independent variables, the oblique principal component cluster analysis (OPCCA) was used to classify 9 variables into disjoint clusters. For Whites, African Americans, Latinos, and Asians, the prevalence of uninsurance was 8.5%, 10.3%, 24.7%, and 12.6%, respectively. Among Asians, the prevalence of uninsurance was 15.5%, 9.2%, 6.2%, 20.8% and 12.1% for Chinese, Filipinos, Japanese, Koreans, and Vietnamese, respectively. In the whole sample, multivariate logistic regression analysis revealed that being male, non-citizen, lower education, higher poverty, and current smoking were associated with uninsurance. Among Asians, compared to Koreans, being Filipinos and Vietnamese were associated with lower odds of being uninsured; meanwhile being male, non-citizen, lower education, and higher poverty were significantly associated with increased odds of uninsurance. Elder age groups and current smoking were significantly associated with increased odds of uninsurance in bivariate analysis; however, such associations disappeared after adjusting for other factors. Nine independent variables were divided into 2 clusters, where the variables in the same cluster were strongly correlated but had weak correlations with the variables in the other cluster. In conclusion, there are differences in the prevalence of uninsurance between Asians and Whites, and among Asian subgroups. Being male, non-citizen, lower education, higher poverty and current smoking were positively significantly associated with uninsurance.","PeriodicalId":45226,"journal":{"name":"Quantitative Finance and Economics","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47866287","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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