<p><b>Correction: Financ Innov 10, 43 (2024)</b> <b>https://doi.org/10.1186/s40854-023-00578-z</b></p><p>Following publication of the original article (Amirteimoori et al. 2024), the authors reported a typesetting error in the affiliation of author Tofigh Allahviranloo.</p><p>Due to a typesetting error, author Tofigh Allahviranloo was mistakenly assigned to affiliation 2:</p><p>Department of Business Administration, Faculty of Business and Economics, University of Goettingen, 37073, Gӧttingen, Germany.</p><p>The correct affiliation for author Tofigh Allahviranloo should be affiliation 1:</p><p>Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey.</p><p>The original article (Amirteimoori et al. 2024) has been updated.</p><ul data-track-component="outbound reference"><li><p>Amirteimoori A, Allahviranloo T, Arabmaldar A (2024) Scale elasticity and technical efficiency measures in two-stage network production processes: an application to the insurance sector. Financ Innov 10:43. https://doi.org/10.1186/s40854-023-00578-z</p><p>Article Google Scholar </p></li></ul><p>Download references<svg aria-hidden="true" focusable="false" height="16" role="img" width="16"><use xlink:href="#icon-eds-i-download-medium" xmlns:xlink="http://www.w3.org/1999/xlink"></use></svg></p><h3>Authors and Affiliations</h3><ol><li><p>Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey</p><p>Alireza Amirteimoori & Tofigh Allahviranloo</p></li><li><p>Department of Business Administration, Faculty of Business and Economics, University of Goettingen, 37073, Gӧttingen, Germany</p><p>Aliasghar Arabmaldar</p></li></ol><span>Authors</span><ol><li><span>Alireza Amirteimoori</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Tofigh Allahviranloo</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Aliasghar Arabmaldar</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li></ol><h3>Corresponding author</h3><p>Correspondence to Alireza Amirteimoori.</p><h3>Publisher's Note</h3><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p><p><b>Open Access</b> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Co
更正：Financ Innov 10, 43 (2024) https://doi.org/10.1186/s40854-023-00578-zFollowing 原文（Amirteimoori et al. 2024）发表时，作者报告了作者 Tofigh Allahviranloo 所属单位的排版错误。由于排版错误，作者 Tofigh Allahviranloo 被错误地分配到了所属单位 2:Department of Business Administration, Faculty of Business and Economics, University of Goettingen, 37073, Gӧttingen, Germany.The original article (Amirteimoori et al. 2024) has been updated.Amirteimoori A, Allahviranloo T, Arabmaldar A (2024) Scale elasticity and technical efficiency measures in two-stage network production processes: an application to the insurance sector. Financ Innov 10:43.Financ Innov 10:43. https://doi.org/10.1186/s40854-023-00578-zArticle Google Scholar Download referencesAuthors and AffiliationsFaculty of Engineering and Natural Sciences, Istinye University, Istanbul, TurkeyAlireza Amirteimoori &；Tofigh AllahviranlooDepartment of Business Administration, Faculty of Business and Economics, University of Goettingen, 37073, Gӧttingen、GermanyAliasghar ArabmaldarAuthorsAlireza AmirteimooriView author publications您也可以在 PubMed Google ScholarTofigh AllahviranlooView author publications您也可以在 PubMed Google ScholarAliasghar ArabmaldarView author publications您也可以在 PubMed Google ScholarCorresponding authorCorrespondence to Alireza Amirteimoori.开放获取本文采用知识共享署名 4.0 国际许可协议进行许可，该协议允许以任何媒介或格式使用、共享、改编、分发和复制，只要您适当注明原作者和来源，提供知识共享许可协议的链接，并说明是否进行了修改。本文中的图片或其他第三方材料均包含在文章的知识共享许可协议中，除非在材料的署名栏中另有说明。如果材料未包含在文章的知识共享许可协议中，且您打算使用的材料不符合法律规定或超出许可使用范围，您需要直接从版权所有者处获得许可。要查看该许可的副本，请访问 http://creativecommons.org/licenses/by/4.0/.Reprints and permissionsCite this articleAmirteimoori, A., Allahviranloo, T. & Arabmaldar, A. Correction：两阶段网络生产流程中的规模弹性和技术效率措施：保险业的应用。Financ Innov 10, 50 (2024). https://doi.org/10.1186/s40854-024-00624-4Download citationPublished: 06 February 2024DOI: https://doi.org/10.1186/s40854-024-00624-4Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative
{"title":"Correction: Scale elasticity and technical efficiency measures in two-stage network production processes: an application to the insurance sector","authors":"Alireza Amirteimoori, Tofigh Allahviranloo, Aliasghar Arabmaldar","doi":"10.1186/s40854-024-00624-4","DOIUrl":"https://doi.org/10.1186/s40854-024-00624-4","url":null,"abstract":"<p><b>Correction: Financ Innov 10, 43 (2024)</b> <b>https://doi.org/10.1186/s40854-023-00578-z</b></p><p>Following publication of the original article (Amirteimoori et al. 2024), the authors reported a typesetting error in the affiliation of author Tofigh Allahviranloo.</p><p>Due to a typesetting error, author Tofigh Allahviranloo was mistakenly assigned to affiliation 2:</p><p>Department of Business Administration, Faculty of Business and Economics, University of Goettingen, 37073, Gӧttingen, Germany.</p><p>The correct affiliation for author Tofigh Allahviranloo should be affiliation 1:</p><p>Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey.</p><p>The original article (Amirteimoori et al. 2024) has been updated.</p><ul data-track-component=\"outbound reference\"><li><p>Amirteimoori A, Allahviranloo T, Arabmaldar A (2024) Scale elasticity and technical efficiency measures in two-stage network production processes: an application to the insurance sector. Financ Innov 10:43. https://doi.org/10.1186/s40854-023-00578-z</p><p>Article Google Scholar </p></li></ul><p>Download references<svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"><use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"></use></svg></p><h3>Authors and Affiliations</h3><ol><li><p>Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey</p><p>Alireza Amirteimoori & Tofigh Allahviranloo</p></li><li><p>Department of Business Administration, Faculty of Business and Economics, University of Goettingen, 37073, Gӧttingen, Germany</p><p>Aliasghar Arabmaldar</p></li></ol><span>Authors</span><ol><li><span>Alireza Amirteimoori</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Tofigh Allahviranloo</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Aliasghar Arabmaldar</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li></ol><h3>Corresponding author</h3><p>Correspondence to Alireza Amirteimoori.</p><h3>Publisher's Note</h3><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p><p><b>Open Access</b> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Co","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"30 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139773646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-06DOI: 10.1186/s40854-023-00581-4
Onur Polat
This study examines the time-varying asymmetric interlinkages between nine US sectoral returns from January 2020 to January 2023. To this end, we used the time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach of Adekoya et al. (Resour Policy 77:102728, 2022a, Resour Policy 78:102877, 2022b) and analyzed the time-varying transmitting/receiving roles of sectors, considering the positive and negative impacts of the spillovers. We further estimate negative spillovers networks at two burst times (the declaration of the COVID-19 pandemic by the World Health Organization on 11 March 2020 and the start of Russian-Ukrainian war on 24 February 2022, respectively). Moreover, we performed a portfolio back-testing analysis to determine the time-varying portfolio allocations and hedging the effectiveness of different portfolio construction techniques. Our results reveal that (i) the sectoral return series are strongly interconnected, and negative spillovers dominate the study period; (ii) US sectoral returns are more sensitive to negative shocks, particularly during the burst times; (iii) the overall, positive, and negative connectedness indices reached their maximums on March 16, 2020; (iv) the industry sector is the largest transmitter/recipient of return shocks on average; and (v) the minimum correlation and connectedness portfolio approaches robustly capture asymmetries. Our findings provide suggestions for investors, portfolio managers, and policymakers regarding optimal portfolio strategies and risk supervision.
{"title":"Interlinkages across US sectoral returns: time-varying interconnectedness and hedging effectiveness","authors":"Onur Polat","doi":"10.1186/s40854-023-00581-4","DOIUrl":"https://doi.org/10.1186/s40854-023-00581-4","url":null,"abstract":"This study examines the time-varying asymmetric interlinkages between nine US sectoral returns from January 2020 to January 2023. To this end, we used the time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach of Adekoya et al. (Resour Policy 77:102728, 2022a, Resour Policy 78:102877, 2022b) and analyzed the time-varying transmitting/receiving roles of sectors, considering the positive and negative impacts of the spillovers. We further estimate negative spillovers networks at two burst times (the declaration of the COVID-19 pandemic by the World Health Organization on 11 March 2020 and the start of Russian-Ukrainian war on 24 February 2022, respectively). Moreover, we performed a portfolio back-testing analysis to determine the time-varying portfolio allocations and hedging the effectiveness of different portfolio construction techniques. Our results reveal that (i) the sectoral return series are strongly interconnected, and negative spillovers dominate the study period; (ii) US sectoral returns are more sensitive to negative shocks, particularly during the burst times; (iii) the overall, positive, and negative connectedness indices reached their maximums on March 16, 2020; (iv) the industry sector is the largest transmitter/recipient of return shocks on average; and (v) the minimum correlation and connectedness portfolio approaches robustly capture asymmetries. Our findings provide suggestions for investors, portfolio managers, and policymakers regarding optimal portfolio strategies and risk supervision.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"20 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139773642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1186/s40854-023-00577-0
Werner Kristjanpoller
Determining which variables affect price realized volatility has always been challenging. This paper proposes to explain how financial assets influence realized volatility by developing an optimal day-to-day forecast. The methodological proposal is based on using the best econometric and machine learning models to forecast realized volatility. In particular, the best forecasting from heterogeneous autoregressive and long short-term memory models are used to determine the influence of the Standard and Poor’s 500 index, euro–US dollar exchange rate, price of gold, and price of Brent crude oil on the realized volatility of natural gas. These financial assets influenced the realized volatility of natural gas in 87.4% of the days analyzed; the euro–US dollar exchange rate was the primary financial asset and explained 40.1% of the influence. The results of the proposed daily analysis differed from those of the methodology used to study the entire period. The traditional model, which studies the entire period, cannot determine temporal effects, whereas the proposed methodology can. The proposed methodology allows us to distinguish the effects for each day, week, or month rather than averages for entire periods, with the flexibility to analyze different frequencies and periods. This methodological capability is key to analyzing influences and making decisions about realized volatility.
{"title":"A hybrid econometrics and machine learning based modeling of realized volatility of natural gas","authors":"Werner Kristjanpoller","doi":"10.1186/s40854-023-00577-0","DOIUrl":"https://doi.org/10.1186/s40854-023-00577-0","url":null,"abstract":"Determining which variables affect price realized volatility has always been challenging. This paper proposes to explain how financial assets influence realized volatility by developing an optimal day-to-day forecast. The methodological proposal is based on using the best econometric and machine learning models to forecast realized volatility. In particular, the best forecasting from heterogeneous autoregressive and long short-term memory models are used to determine the influence of the Standard and Poor’s 500 index, euro–US dollar exchange rate, price of gold, and price of Brent crude oil on the realized volatility of natural gas. These financial assets influenced the realized volatility of natural gas in 87.4% of the days analyzed; the euro–US dollar exchange rate was the primary financial asset and explained 40.1% of the influence. The results of the proposed daily analysis differed from those of the methodology used to study the entire period. The traditional model, which studies the entire period, cannot determine temporal effects, whereas the proposed methodology can. The proposed methodology allows us to distinguish the effects for each day, week, or month rather than averages for entire periods, with the flexibility to analyze different frequencies and periods. This methodological capability is key to analyzing influences and making decisions about realized volatility.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"36 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139578578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In performance analysis with tools such as data envelopment analysis, calculations of scale properties of the frontier points are studied using both qualitative and quantitative approaches. When the production process is a bit complicated, the calculation needs to be modified. Most existing studies are focused on a single-stage production process under the constant or variable returns to scale specification. However, some processes have two-stage structures, and, in such processes, the concepts of scale elasticity and returns to scale are inextricably related to the conditions of the stages of production. Thus, an evaluation of efficiency, scale elasticity, and returns to scale is sensitive to stages. In this study, we introduced a procedure to calculate technical efficiency and scale elasticity in a two-stage parallel-series production system. Then, our proposed technical efficiency and scale elasticity programs are applied to real data on 20 insurance companies in Iran. After applying our estimations to a real-world insurance industry, we found that, (i) overall, the total inputs of insurers in the life insurance sector should be reduced by 9%. Moreover, the inputs of nonlife insurers should be reduced by 50%. The final output in the investment sector must be increased by 48%. (ii) There are inefficiencies among all insurers in the investment sector, and to improve technical efficiency, the income from investments should be increased significantly. (iii) Finally, the efficiency and elasticity characterizations of insurers are directly subject to stages.
{"title":"Scale elasticity and technical efficiency measures in two-stage network production processes: an application to the insurance sector","authors":"Alireza Amirteimoori, Tofigh Allahviranloo, Aliasghar Arabmaldar","doi":"10.1186/s40854-023-00578-z","DOIUrl":"https://doi.org/10.1186/s40854-023-00578-z","url":null,"abstract":"In performance analysis with tools such as data envelopment analysis, calculations of scale properties of the frontier points are studied using both qualitative and quantitative approaches. When the production process is a bit complicated, the calculation needs to be modified. Most existing studies are focused on a single-stage production process under the constant or variable returns to scale specification. However, some processes have two-stage structures, and, in such processes, the concepts of scale elasticity and returns to scale are inextricably related to the conditions of the stages of production. Thus, an evaluation of efficiency, scale elasticity, and returns to scale is sensitive to stages. In this study, we introduced a procedure to calculate technical efficiency and scale elasticity in a two-stage parallel-series production system. Then, our proposed technical efficiency and scale elasticity programs are applied to real data on 20 insurance companies in Iran. After applying our estimations to a real-world insurance industry, we found that, (i) overall, the total inputs of insurers in the life insurance sector should be reduced by 9%. Moreover, the inputs of nonlife insurers should be reduced by 50%. The final output in the investment sector must be increased by 48%. (ii) There are inefficiencies among all insurers in the investment sector, and to improve technical efficiency, the income from investments should be increased significantly. (iii) Finally, the efficiency and elasticity characterizations of insurers are directly subject to stages.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"59 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139578671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-27DOI: 10.1186/s40854-023-00580-5
Tonuchi E. Joseph, Atif Jahanger, Joshua Chukwuma Onwe, Daniel Balsalobre-Lorente
This study examined the interconnectedness and volatility correlation between cryptocurrency and traditional financial markets in the five largest African countries, addressing concerns about potential spillover effects, especially the high volatility and lack of regulation in the cryptocurrency market. The study employed both diagonal BEKK-GARCH and DCC-GARCH to analyze the existence of spillover effects and correlation between both markets. A daily time series dataset from January 1, 2017, to December 31, 2021, was employed to analyze the contagion effect. Our findings reveal a significant spillover effect from cryptocurrency to the African traditional financial market; however, the percentage spillover effect is still low but growing. Specifically, evidence is insufficient to suggest a spillover effect from cryptocurrency to Egypt and Morocco’s financial markets, at least in the short run. Evidence in South Africa, Nigeria, and Kenya indicates a moderate but growing spillover effect from cryptocurrency to the financial market. Similarly, we found no evidence of a spillover effect from the African financial market to the cryptocurrency market. The conditional correlation result from the DCC-GARCH revealed a positive low to moderate correlation between cryptocurrency volatility and the African financial market. Specifically, the DCC-GARCH revealed a greater integration in both markets, especially in the long run. The findings have policy implications for financial regulators concerning the dynamics of both markets and for investors interested in portfolio diversification within the two markets.
{"title":"The implication of cryptocurrency volatility on five largest African financial system stability","authors":"Tonuchi E. Joseph, Atif Jahanger, Joshua Chukwuma Onwe, Daniel Balsalobre-Lorente","doi":"10.1186/s40854-023-00580-5","DOIUrl":"https://doi.org/10.1186/s40854-023-00580-5","url":null,"abstract":"This study examined the interconnectedness and volatility correlation between cryptocurrency and traditional financial markets in the five largest African countries, addressing concerns about potential spillover effects, especially the high volatility and lack of regulation in the cryptocurrency market. The study employed both diagonal BEKK-GARCH and DCC-GARCH to analyze the existence of spillover effects and correlation between both markets. A daily time series dataset from January 1, 2017, to December 31, 2021, was employed to analyze the contagion effect. Our findings reveal a significant spillover effect from cryptocurrency to the African traditional financial market; however, the percentage spillover effect is still low but growing. Specifically, evidence is insufficient to suggest a spillover effect from cryptocurrency to Egypt and Morocco’s financial markets, at least in the short run. Evidence in South Africa, Nigeria, and Kenya indicates a moderate but growing spillover effect from cryptocurrency to the financial market. Similarly, we found no evidence of a spillover effect from the African financial market to the cryptocurrency market. The conditional correlation result from the DCC-GARCH revealed a positive low to moderate correlation between cryptocurrency volatility and the African financial market. Specifically, the DCC-GARCH revealed a greater integration in both markets, especially in the long run. The findings have policy implications for financial regulators concerning the dynamics of both markets and for investors interested in portfolio diversification within the two markets.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"3 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139578672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-26DOI: 10.1186/s40854-023-00565-4
Perry Sadorsky, Irene Henriques
Non-fungible tokens (NFTs) are one-of-a-kind digital assets that are stored on a blockchain. Examples of NFTs include art (e.g., image, video, animation), collectables (e.g., autographs), and objects from games (e.g., weapons and poisons). NFTs provide content creators and artists a way to promote and sell their unique digital material online. NFT coins underpin the ecosystems that support NFTs and are a new and emerging asset class and, as a new and emerging asset class, NFT coins are not immune to economic uncertainty. This research seeks to address the following questions. What is the time and frequency relationship between economic uncertainty and NFT coins? Is the relationship similar across different NFT coins? As an emerging asset, do NFT coins exhibit explosive behavior and if so, what role does economic uncertainty play in their formation? Using a new Twitter-based economic uncertainty index and a related equity market uncertainty index it is found that wavelet coherence between NFT coin prices (ENJ, MANA, THETA, XTZ) and economic uncertainty or market uncertainty is strongest during the periods January 2020 to July 2020 and January 2022 to July 2022. Periods of high significance are centered around the 64-day scale. During periods of high coherence, economic and market uncertainty exhibit an out of phase relationship with NFT coin prices. Network connectedness shows that the highest connectedness occurred during 2020 and 2022 which is consistent with the findings from wavelet analysis. Infectious disease outbreaks (COVID-19), NFT coin price volatility, and Twitter-based economic uncertainty determine bubbles in NFT coin prices.
{"title":"Time and frequency dynamics between NFT coins and economic uncertainty","authors":"Perry Sadorsky, Irene Henriques","doi":"10.1186/s40854-023-00565-4","DOIUrl":"https://doi.org/10.1186/s40854-023-00565-4","url":null,"abstract":"Non-fungible tokens (NFTs) are one-of-a-kind digital assets that are stored on a blockchain. Examples of NFTs include art (e.g., image, video, animation), collectables (e.g., autographs), and objects from games (e.g., weapons and poisons). NFTs provide content creators and artists a way to promote and sell their unique digital material online. NFT coins underpin the ecosystems that support NFTs and are a new and emerging asset class and, as a new and emerging asset class, NFT coins are not immune to economic uncertainty. This research seeks to address the following questions. What is the time and frequency relationship between economic uncertainty and NFT coins? Is the relationship similar across different NFT coins? As an emerging asset, do NFT coins exhibit explosive behavior and if so, what role does economic uncertainty play in their formation? Using a new Twitter-based economic uncertainty index and a related equity market uncertainty index it is found that wavelet coherence between NFT coin prices (ENJ, MANA, THETA, XTZ) and economic uncertainty or market uncertainty is strongest during the periods January 2020 to July 2020 and January 2022 to July 2022. Periods of high significance are centered around the 64-day scale. During periods of high coherence, economic and market uncertainty exhibit an out of phase relationship with NFT coin prices. Network connectedness shows that the highest connectedness occurred during 2020 and 2022 which is consistent with the findings from wavelet analysis. Infectious disease outbreaks (COVID-19), NFT coin price volatility, and Twitter-based economic uncertainty determine bubbles in NFT coin prices.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"25 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139578675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-25DOI: 10.1186/s40854-023-00549-4
Changyu Liu, Wei Li, Le Chang, Qiang Ji
Greenwashing behaviors (GWBs) in green finance products (GFPs) by enterprises seriously hinder the realization of environmental protection goals. However, methods for effectively regulating GWBs in GFPs are unclear. This study constructed a tripartite evolutionary game model to analyze the formation and governance mechanisms of GWBs in GFPs among regulatory authorities, enterprises, and investors. Subsequently, the stability equilibrium strategy and key factors influencing the system equilibrium were discussed. Several interesting conclusions were drawn. First, we demonstrated that an interdependence mechanism exists among three game agents who mutually influence each other. The larger the probability of regulatory authorities choosing active supervision and investors adopting feedback, the more enterprises are willing to carry out green projects. Second, three corresponding governance modes for GWBs were put forward following the developmental stages of GFPs. Among these, the collaboration mode is the most effective in incentivizing enterprises to implement green projects. Third, based on sensitivity simulations, the initial willingness of the tripartite stakeholders, investor feedback cost, investor compensation, the penalty for greenwashing enterprises, and the reputational benefit of enterprises are critical factors that influence evolutionary results. Finally, targeted countermeasures were provided for regulatory authorities to prevent enterprises from engaging in GWBs.
{"title":"How to govern greenwashing behaviors in green finance products: a tripartite evolutionary game approach","authors":"Changyu Liu, Wei Li, Le Chang, Qiang Ji","doi":"10.1186/s40854-023-00549-4","DOIUrl":"https://doi.org/10.1186/s40854-023-00549-4","url":null,"abstract":"Greenwashing behaviors (GWBs) in green finance products (GFPs) by enterprises seriously hinder the realization of environmental protection goals. However, methods for effectively regulating GWBs in GFPs are unclear. This study constructed a tripartite evolutionary game model to analyze the formation and governance mechanisms of GWBs in GFPs among regulatory authorities, enterprises, and investors. Subsequently, the stability equilibrium strategy and key factors influencing the system equilibrium were discussed. Several interesting conclusions were drawn. First, we demonstrated that an interdependence mechanism exists among three game agents who mutually influence each other. The larger the probability of regulatory authorities choosing active supervision and investors adopting feedback, the more enterprises are willing to carry out green projects. Second, three corresponding governance modes for GWBs were put forward following the developmental stages of GFPs. Among these, the collaboration mode is the most effective in incentivizing enterprises to implement green projects. Third, based on sensitivity simulations, the initial willingness of the tripartite stakeholders, investor feedback cost, investor compensation, the penalty for greenwashing enterprises, and the reputational benefit of enterprises are critical factors that influence evolutionary results. Finally, targeted countermeasures were provided for regulatory authorities to prevent enterprises from engaging in GWBs.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"37 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139578676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-24DOI: 10.1186/s40854-023-00559-2
Jules Clement Mba
This study evaluates the sensitivity and robustness of the systemic risk measure, Conditional Value-at-Risk (CoVaR), estimated using the vine copula and APARCH-DCC models. We compute the CoVaR for the two portfolios across five allocation strategies. The novel vine copula captures the complex dependence patterns and tail dynamics. The APARCH DCC incorporates volatility clustering, skewness, and kurtosis. The results reveal that the CoVaR estimates vary based on portfolio strategy, with higher values for the cryptocurrency portfolio. However, CoVaR appears relatively robust across strategies compared to ΔCoVaR. The cryptocurrency portfolio has a greater overall vulnerability. The findings demonstrate the value of CoVaR estimated via the vine copula and APARCH-DCC in assessing portfolio systemic risk. This advanced approach provides nuanced insights into strengthening risk management practices. Future research could explore the sensitivity of the CoVaR to different weighting schemes, such as equal versus market-weighted portfolios. Incorporating the Gram–Charlier expansion of normal density into the APARCH specification enables a nonparametric, data-driven fitting of the residual distribution. Furthermore, comparing the CoVaR to another systemic risk measure could provide further insights into its reliability as a systemic risk measure.
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Pub Date : 2024-01-22DOI: 10.1186/s40854-023-00546-7
Matteo Mazzarano
Decarbonization is often misunderstood in financial studies. Furthermore, its implications for investment opportunities and growth are even less known. The study investigates the link between energy indicators and Tobin's Quotient (TQ) in listed companies globally, finding that the carbon content of energy presents a negative yet modest effect on financial performance. Furthermore, we investigated the effect carbon prices in compliance markets have on TQ for exempted and non-exempt firms, finding that Energy efficiency measures yield greater effects in the latter group. Conversely, it is also true that carbon prices marginally reduce TQ more in non-exempt firms. This implies that auction-mechanisms create burdens that companies are eager to relinquish by reducing emissions. However, reducing GHG yields positive effects on TQ only as long as it results in energy efficiency improvements.
{"title":"Financial markets implications of the energy transition: carbon content of energy use in listed companies","authors":"Matteo Mazzarano","doi":"10.1186/s40854-023-00546-7","DOIUrl":"https://doi.org/10.1186/s40854-023-00546-7","url":null,"abstract":"Decarbonization is often misunderstood in financial studies. Furthermore, its implications for investment opportunities and growth are even less known. The study investigates the link between energy indicators and Tobin's Quotient (TQ) in listed companies globally, finding that the carbon content of energy presents a negative yet modest effect on financial performance. Furthermore, we investigated the effect carbon prices in compliance markets have on TQ for exempted and non-exempt firms, finding that Energy efficiency measures yield greater effects in the latter group. Conversely, it is also true that carbon prices marginally reduce TQ more in non-exempt firms. This implies that auction-mechanisms create burdens that companies are eager to relinquish by reducing emissions. However, reducing GHG yields positive effects on TQ only as long as it results in energy efficiency improvements.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"1 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139514787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-21DOI: 10.1186/s40854-023-00541-y
Serkan Eti, Hasan Dinçer, Hasan Meral, Serhat Yüksel, Yaşar Gökalp
The purpose of this study is to determine the essential indicators to improve insurtech systems and select the most critical alternative to increase insurtech-based investments in European countries. A novel fuzzy decision-making model is generated by integrating entropy and additive ratio assessment (ARAS) techniques with spherical fuzzy sets. First, the indicators are weighted using spherical fuzzy entropy. Then, the alternatives are ranked using spherical fuzzy ARAS. The alternatives are also ranked with the spherical fuzzy technique for order of preference by similarity to the ideal solution methodology. The main contribution of this study is that it would help investors to take the right actions to increase the performance of insurtech investments without incurring high costs. Another important novelty is that a new fuzzy decision-making model is proposed to solve this problem. The results of the two models are quite similar, proving the validity and coherency of the findings. It is found that pricing is the most critical factor that affects the performance of insurtech investments. Insurtech companies are required to make accurate pricing by conducting risk analyses to increase their profits and minimize their risks. Additionally, according to the ranking results, big data are the most appropriate way to improve the performance of insurtech investments in Europe. Big data analytics helps companies learn more about the behavior of their customers. By analyzing data about their customers’ past transactions, companies can provide more convenient services to them. This would increase customer satisfaction and enable companies to achieve long-term customer loyalty.
本研究的目的是确定改进保险科技系统的基本指标，并选择最关键的备选方案，以增加欧洲国家基于保险科技的投资。通过将熵和加法比率评估（ARAS）技术与球形模糊集相结合，建立了一个新颖的模糊决策模型。首先，使用球形模糊熵对指标进行加权。然后，使用球形模糊 ARAS 对备选方案进行排序。此外，还利用球形模糊技术，通过与理想解决方案方法的相似性对备选方案进行优先排序。本研究的主要贡献在于，它有助于投资者采取正确的行动，在不付出高昂成本的情况下提高保险科技投资的绩效。另一个重要的新颖之处在于提出了一个新的模糊决策模型来解决这一问题。两个模型的结果非常相似，证明了研究结果的有效性和一致性。研究发现，定价是影响保险科技投资绩效的最关键因素。保险科技公司需要通过进行风险分析来准确定价，以增加利润并将风险降至最低。此外，根据排名结果，大数据是提高欧洲保险科技投资绩效的最合适方式。大数据分析可以帮助公司更多地了解客户的行为。通过分析客户过去的交易数据，公司可以为客户提供更便捷的服务。这将提高客户满意度，使公司获得长期的客户忠诚度。
{"title":"Insurtech in Europe: identifying the top investment priorities for driving innovation","authors":"Serkan Eti, Hasan Dinçer, Hasan Meral, Serhat Yüksel, Yaşar Gökalp","doi":"10.1186/s40854-023-00541-y","DOIUrl":"https://doi.org/10.1186/s40854-023-00541-y","url":null,"abstract":"The purpose of this study is to determine the essential indicators to improve insurtech systems and select the most critical alternative to increase insurtech-based investments in European countries. A novel fuzzy decision-making model is generated by integrating entropy and additive ratio assessment (ARAS) techniques with spherical fuzzy sets. First, the indicators are weighted using spherical fuzzy entropy. Then, the alternatives are ranked using spherical fuzzy ARAS. The alternatives are also ranked with the spherical fuzzy technique for order of preference by similarity to the ideal solution methodology. The main contribution of this study is that it would help investors to take the right actions to increase the performance of insurtech investments without incurring high costs. Another important novelty is that a new fuzzy decision-making model is proposed to solve this problem. The results of the two models are quite similar, proving the validity and coherency of the findings. It is found that pricing is the most critical factor that affects the performance of insurtech investments. Insurtech companies are required to make accurate pricing by conducting risk analyses to increase their profits and minimize their risks. Additionally, according to the ranking results, big data are the most appropriate way to improve the performance of insurtech investments in Europe. Big data analytics helps companies learn more about the behavior of their customers. By analyzing data about their customers’ past transactions, companies can provide more convenient services to them. This would increase customer satisfaction and enable companies to achieve long-term customer loyalty.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"1 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139509257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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