Pub Date : 2024-06-03DOI: 10.1186/s40854-024-00636-0
Elie Bouri, Mahdi Ghaemi Asl, Sahar Darehshiri, David Gabauer
This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional (Bitcoin and Ethereum) and Islamic (Stellar and Ripple) cryptocurrencies. Using a novel time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach combined with a high frequency (hourly) dataset ranging from 1st June 2018 to 22nd July 2022, we find that (i) good and bad spillovers are time-varying; (ii) bad volatility spillovers are more pronounced than good spillovers; (iii) a strong asymmetry in the volatility spillovers exists in the cryptocurrency market; and (iv) conventional cryptocurrencies dominate Islamic cryptocurrencies. Specifically, Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.
{"title":"Asymmetric connectedness between conventional and Islamic cryptocurrencies: Evidence from good and bad volatility spillovers","authors":"Elie Bouri, Mahdi Ghaemi Asl, Sahar Darehshiri, David Gabauer","doi":"10.1186/s40854-024-00636-0","DOIUrl":"https://doi.org/10.1186/s40854-024-00636-0","url":null,"abstract":"This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional (Bitcoin and Ethereum) and Islamic (Stellar and Ripple) cryptocurrencies. Using a novel time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach combined with a high frequency (hourly) dataset ranging from 1st June 2018 to 22nd July 2022, we find that (i) good and bad spillovers are time-varying; (ii) bad volatility spillovers are more pronounced than good spillovers; (iii) a strong asymmetry in the volatility spillovers exists in the cryptocurrency market; and (iv) conventional cryptocurrencies dominate Islamic cryptocurrencies. Specifically, Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"23 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141256030","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 : 2024-06-01DOI: 10.1186/s40854-023-00608-w
Cong Wang
In the field of empirical asset pricing, the challenges of high dimensionality, non-linear relationships, and interaction effects have led to the increasing popularity of machine learning (ML) methods. This study investigates the performance of ML methods when predicting different measures of stock returns from various factor models and investigates the feature importance and interaction effects among firm-specific variables and macroeconomic factors in this context. Our findings reveal that neural network models exhibit consistent performance across different stock return measures when they rely solely on firm-specific characteristic variables. However, the inclusion of macroeconomic factors from the financial market, real economic activities, and investor sentiment leads to substantial improvements in the model performance. Notably, the degree of improvement varies with the specific measures of stock returns under consideration. Furthermore, our analysis indicates that, after the inclusion of macroeconomic factors, there is a dissimilarity in model performance, variable importance, and interaction effects among macroeconomic and firm-specific variables, particularly concerning abnormal returns derived from the Fama–French three- and five-factor models compared with excess returns. This divergence is primarily attributed to the extent to which these factor models remove the variance associated with the macroeconomic variables. These findings collectively offer valuable insights into the efficacy of neural network models for stock return predictions and contribute to a deeper understanding of the intricate relationship between factor models, stock returns, and macroeconomic conditions in the domain of empirical asset pricing.
在实证资产定价领域，高维度、非线性关系和交互效应等挑战导致机器学习（ML）方法越来越受欢迎。本研究调查了 ML 方法在预测各种因素模型的不同股票回报率时的表现，并在此背景下调查了公司特定变量和宏观经济因素之间的特征重要性和交互效应。我们的研究结果表明，当神经网络模型仅依赖于公司特定特征变量时，它们在不同的股票回报率衡量标准中表现出一致的性能。但是，如果加入金融市场、实体经济活动和投资者情绪等宏观经济因素，模型的性能就会大幅提高。值得注意的是，改进的程度因所考虑的股票回报率的具体衡量标准而异。此外，我们的分析表明，在纳入宏观经济因素后，宏观经济变量和公司特定变量之间在模型性能、变量重要性和交互效应方面存在差异，特别是在法马-法兰克三因素和五因素模型得出的异常收益与超额收益之间。这种差异主要归因于这些因子模型在多大程度上消除了与宏观经济变量相关的方差。这些发现共同为神经网络模型预测股票收益的有效性提供了宝贵的见解，并有助于加深对实证资产定价领域中因子模型、股票收益和宏观经济条件之间错综复杂关系的理解。
{"title":"Stock return prediction with multiple measures using neural network models","authors":"Cong Wang","doi":"10.1186/s40854-023-00608-w","DOIUrl":"https://doi.org/10.1186/s40854-023-00608-w","url":null,"abstract":"In the field of empirical asset pricing, the challenges of high dimensionality, non-linear relationships, and interaction effects have led to the increasing popularity of machine learning (ML) methods. This study investigates the performance of ML methods when predicting different measures of stock returns from various factor models and investigates the feature importance and interaction effects among firm-specific variables and macroeconomic factors in this context. Our findings reveal that neural network models exhibit consistent performance across different stock return measures when they rely solely on firm-specific characteristic variables. However, the inclusion of macroeconomic factors from the financial market, real economic activities, and investor sentiment leads to substantial improvements in the model performance. Notably, the degree of improvement varies with the specific measures of stock returns under consideration. Furthermore, our analysis indicates that, after the inclusion of macroeconomic factors, there is a dissimilarity in model performance, variable importance, and interaction effects among macroeconomic and firm-specific variables, particularly concerning abnormal returns derived from the Fama–French three- and five-factor models compared with excess returns. This divergence is primarily attributed to the extent to which these factor models remove the variance associated with the macroeconomic variables. These findings collectively offer valuable insights into the efficacy of neural network models for stock return predictions and contribute to a deeper understanding of the intricate relationship between factor models, stock returns, and macroeconomic conditions in the domain of empirical asset pricing.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"64 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195596","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 : 2024-05-29DOI: 10.1186/s40854-024-00623-5
Mahdi Ghaemi Asl, David Roubaud
This study examines the nexus between the good and bad volatilities of three technological revolutions—financial technology (FinTech), the Internet of Things, and artificial intelligence and technology—as well as the two main conventional and Islamic cryptocurrency platforms, Bitcoin and Stellar, via three approaches: quantile cross-spectral coherence, quantile-VAR connectedness, and quantile-based non-linear causality-in-mean and variance analysis. The results are as follows: (1) under normal market conditions, in long-run horizons there is a significant positive cross-spectral relationship between FinTech's positive volatilities and Stellar’s negative volatilities; (2) Stellar’s negative and positive volatilities exhibit the highest net spillovers at the lower and upper tails, respectively; and (3) the quantile-based causality results indicate that Bitcoin’s good (bad) volatilities can lead to bad (good) volatilities in all three smart technologies operating between normal and bull market conditions. Moreover, the Bitcoin industry’s negative volatilities have a bilateral cause-and-effect relationship with FinTech’s positive volatilities. By analyzing the second moment, we found that Bitcoin's negative volatilities are the only cause variable that generates FinTech's good volatility in a unidirectional manner. As for Stellar, only bad volatilities have the potential to signal good volatilities for cutting-edge technologies in some middle quantiles, whereas good volatilities have no significant effect. Hence, the trade-off between Bitcoin and cutting-edge technologies, especially FinTech-related advancements, appear more broadly and randomly compared with the Stellar-innovative technologies nexus. The findings provide valuable insights for FinTech companies, blockchain developers, crypto-asset regulators, portfolio managers, and high-tech investors.
{"title":"Asymmetric interactions among cutting-edge technologies and pioneering conventional and Islamic cryptocurrencies: fresh evidence from intra-day-based good and bad volatilities","authors":"Mahdi Ghaemi Asl, David Roubaud","doi":"10.1186/s40854-024-00623-5","DOIUrl":"https://doi.org/10.1186/s40854-024-00623-5","url":null,"abstract":"This study examines the nexus between the good and bad volatilities of three technological revolutions—financial technology (FinTech), the Internet of Things, and artificial intelligence and technology—as well as the two main conventional and Islamic cryptocurrency platforms, Bitcoin and Stellar, via three approaches: quantile cross-spectral coherence, quantile-VAR connectedness, and quantile-based non-linear causality-in-mean and variance analysis. The results are as follows: (1) under normal market conditions, in long-run horizons there is a significant positive cross-spectral relationship between FinTech's positive volatilities and Stellar’s negative volatilities; (2) Stellar’s negative and positive volatilities exhibit the highest net spillovers at the lower and upper tails, respectively; and (3) the quantile-based causality results indicate that Bitcoin’s good (bad) volatilities can lead to bad (good) volatilities in all three smart technologies operating between normal and bull market conditions. Moreover, the Bitcoin industry’s negative volatilities have a bilateral cause-and-effect relationship with FinTech’s positive volatilities. By analyzing the second moment, we found that Bitcoin's negative volatilities are the only cause variable that generates FinTech's good volatility in a unidirectional manner. As for Stellar, only bad volatilities have the potential to signal good volatilities for cutting-edge technologies in some middle quantiles, whereas good volatilities have no significant effect. Hence, the trade-off between Bitcoin and cutting-edge technologies, especially FinTech-related advancements, appear more broadly and randomly compared with the Stellar-innovative technologies nexus. The findings provide valuable insights for FinTech companies, blockchain developers, crypto-asset regulators, portfolio managers, and high-tech investors. ","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"47 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141169505","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 : 2024-05-29DOI: 10.1186/s40854-024-00651-1
Saman Hatamerad, Hossain Asgharpur, Bahram Adrangi, Jafar Haghighat
This study examines the relationship between macroeconomic variables and stock price indices of four prominent OPEC oil-exporting members. Bayesian model averaging (BMA) and regularized linear regression (RLR) are employed to address uncertainties arising from different estimation models and variable selection. Jointness is utilized to determine the nature of relationships among variable pairs. The case study spans macroeconomic variables and stock prices from 1996 to 2018. BMA findings reveal a strong positive association between stock price indices and both consumer price index (CPI) and broad money growth in each analyzed OPEC country. Additionally, the study suggests a weak negative correlation between OPEC oil prices and the stock price index. RLR results align with BMA analysis, offering insights valuable for policymakers and international wealth managers.
{"title":"Stock price index analysis of four OPEC members: a Bayesian approach","authors":"Saman Hatamerad, Hossain Asgharpur, Bahram Adrangi, Jafar Haghighat","doi":"10.1186/s40854-024-00651-1","DOIUrl":"https://doi.org/10.1186/s40854-024-00651-1","url":null,"abstract":"This study examines the relationship between macroeconomic variables and stock price indices of four prominent OPEC oil-exporting members. Bayesian model averaging (BMA) and regularized linear regression (RLR) are employed to address uncertainties arising from different estimation models and variable selection. Jointness is utilized to determine the nature of relationships among variable pairs. The case study spans macroeconomic variables and stock prices from 1996 to 2018. BMA findings reveal a strong positive association between stock price indices and both consumer price index (CPI) and broad money growth in each analyzed OPEC country. Additionally, the study suggests a weak negative correlation between OPEC oil prices and the stock price index. RLR results align with BMA analysis, offering insights valuable for policymakers and international wealth managers.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"133 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141169571","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 : 2024-05-10DOI: 10.1186/s40854-024-00627-1
Muhammad Imran, Muhammad Kamran Khan, Shabbir Alam, Salman Wahab, Muhammad Tufail, Zhang Jijian
This study explores the complex relationships involving ecological footprints, energy use, carbon emissions, governance efficiency, economic prosperity, and financial stability in South Asian nations spanning the period from 2000 to 2022. Employing various methodologies such as cross-sectional dependence tests, co-integration analysis, and first- and second-generation unit-root tests, we use a panel Autoregressive Distributed Lag model, feasible generalized least squares, and Panel Corrected Standard Errors to ensure the robustness of our findings. We find noteworthy positive correlations between several variables, including heightened ecological consciousness, effective governance structures, increased GDP per capita, and amplified CO2 emissions. These relationships suggest potential pathways to strengthen the financial stability of the entire region; they also highlight the latent potential of embracing ecologically sustainable practices to fortify economic resilience. Our results also underscore the pivotal role of appropriate governance structures and higher income levels in bolstering financial stability in South Asian countries. Interestingly, we also find negative coefficients associated with the use of renewable energy, suggesting that escalating the adoption of renewable energy could create financial instability. This finding stresses the importance of diversification in energy strategies, cautioning policymakers to carefully consider the financial ramifications of potentially costly imports of renewable energy sources while seeking to reduce carbon emissions, emphasizing the need to strike a balance between ambitious sustainability goals and the pursuit of sustained economic robustness in the region. In considering the implications of these findings, it is crucial to consider each country’s broader socioeconomic context. Our results offer valuable insights for policymakers in developing renewable energy strategies.
本研究探讨了 2000 年至 2022 年期间南亚国家的生态足迹、能源使用、碳排放、治理效率、经济繁荣和金融稳定之间的复杂关系。我们采用了横截面依赖性检验、协整分析、第一代和第二代单位根检验等多种方法，使用了面板自回归分布滞后模型、可行的广义最小二乘法和面板校正标准误差，以确保研究结果的稳健性。我们发现几个变量之间存在值得注意的正相关关系，包括生态意识的提高、有效的治理结构、人均 GDP 的增加以及二氧化碳排放量的增加。这些关系为加强整个地区的金融稳定性提供了潜在的途径；它们还凸显了采用生态可持续实践来加强经济韧性的潜在潜力。我们的研究结果还强调了适当的治理结构和较高的收入水平在增强南亚国家金融稳定性方面的关键作用。有趣的是，我们还发现了与可再生能源的使用相关的负系数，这表明可再生能源的应用升级可能会造成金融不稳定。这一发现强调了能源战略多样化的重要性，告诫政策制定者在寻求减少碳排放的同时，应仔细考虑进口可再生能源可能带来的高成本金融后果，强调需要在雄心勃勃的可持续发展目标和追求该地区持续经济稳健性之间取得平衡。在考虑这些发现的影响时，关键是要考虑每个国家更广泛的社会经济背景。我们的研究结果为决策者制定可再生能源战略提供了宝贵的见解。
{"title":"The implications of the ecological footprint and renewable energy usage on the financial stability of South Asian countries","authors":"Muhammad Imran, Muhammad Kamran Khan, Shabbir Alam, Salman Wahab, Muhammad Tufail, Zhang Jijian","doi":"10.1186/s40854-024-00627-1","DOIUrl":"https://doi.org/10.1186/s40854-024-00627-1","url":null,"abstract":"This study explores the complex relationships involving ecological footprints, energy use, carbon emissions, governance efficiency, economic prosperity, and financial stability in South Asian nations spanning the period from 2000 to 2022. Employing various methodologies such as cross-sectional dependence tests, co-integration analysis, and first- and second-generation unit-root tests, we use a panel Autoregressive Distributed Lag model, feasible generalized least squares, and Panel Corrected Standard Errors to ensure the robustness of our findings. We find noteworthy positive correlations between several variables, including heightened ecological consciousness, effective governance structures, increased GDP per capita, and amplified CO2 emissions. These relationships suggest potential pathways to strengthen the financial stability of the entire region; they also highlight the latent potential of embracing ecologically sustainable practices to fortify economic resilience. Our results also underscore the pivotal role of appropriate governance structures and higher income levels in bolstering financial stability in South Asian countries. Interestingly, we also find negative coefficients associated with the use of renewable energy, suggesting that escalating the adoption of renewable energy could create financial instability. This finding stresses the importance of diversification in energy strategies, cautioning policymakers to carefully consider the financial ramifications of potentially costly imports of renewable energy sources while seeking to reduce carbon emissions, emphasizing the need to strike a balance between ambitious sustainability goals and the pursuit of sustained economic robustness in the region. In considering the implications of these findings, it is crucial to consider each country’s broader socioeconomic context. Our results offer valuable insights for policymakers in developing renewable energy strategies.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"1 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140932773","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 : 2024-05-08DOI: 10.1186/s40854-023-00596-x
Parisa Foroutan, Salim Lahmiri
The notion that investors shift to gold during economic market crises remains unverified for many cryptocurrency markets. This paper investigates the connectedness between the 10 most traded cryptocurrencies and gold as well as crude oil markets pre-COVID-19 and during COVID-19. Through the application of various statistical techniques, including cointegration tests, vector autoregressive models, vector error correction models, autoregressive distributed lag models, and Granger causality analyses, we explore the relationship between these markets and assess the safe-haven properties of gold and crude oil for cryptocurrencies. Our findings reveal that during the COVID-19 pandemic, gold is a strong safe-haven for Bitcoin, Litecoin, and Monero while demonstrating a weaker safe-haven potential for Bitcoin Cash, EOS, Chainlink, and Cardano. In contrast, gold only exhibits a strong safe-haven characteristic before the pandemic for Litecoin and Monero. Additionally, Brent crude oil emerges as a strong safe-haven for Bitcoin during COVID-19, while West Texas Intermediate and Brent crude oils demonstrate weaker safe-haven properties for Ether, Bitcoin Cash, EOS, and Monero. Furthermore, the Granger causality analysis indicates that before the COVID-19 pandemic, the causal relationship predominantly flowed from gold and crude oil toward the cryptocurrency markets; however, during the COVID-19 period, the direction of causality shifted, with cryptocurrencies exerting influence on the gold and crude oil markets. These findings provide subtle implications for policymakers, hedge fund managers, and individual or institutional cryptocurrency investors. Our results highlight the need to adapt risk exposure strategies during financial turmoil, such as the crisis precipitated by the COVID-19 pandemic.
{"title":"Connectedness of cryptocurrency markets to crude oil and gold: an analysis of the effect of COVID-19 pandemic","authors":"Parisa Foroutan, Salim Lahmiri","doi":"10.1186/s40854-023-00596-x","DOIUrl":"https://doi.org/10.1186/s40854-023-00596-x","url":null,"abstract":"The notion that investors shift to gold during economic market crises remains unverified for many cryptocurrency markets. This paper investigates the connectedness between the 10 most traded cryptocurrencies and gold as well as crude oil markets pre-COVID-19 and during COVID-19. Through the application of various statistical techniques, including cointegration tests, vector autoregressive models, vector error correction models, autoregressive distributed lag models, and Granger causality analyses, we explore the relationship between these markets and assess the safe-haven properties of gold and crude oil for cryptocurrencies. Our findings reveal that during the COVID-19 pandemic, gold is a strong safe-haven for Bitcoin, Litecoin, and Monero while demonstrating a weaker safe-haven potential for Bitcoin Cash, EOS, Chainlink, and Cardano. In contrast, gold only exhibits a strong safe-haven characteristic before the pandemic for Litecoin and Monero. Additionally, Brent crude oil emerges as a strong safe-haven for Bitcoin during COVID-19, while West Texas Intermediate and Brent crude oils demonstrate weaker safe-haven properties for Ether, Bitcoin Cash, EOS, and Monero. Furthermore, the Granger causality analysis indicates that before the COVID-19 pandemic, the causal relationship predominantly flowed from gold and crude oil toward the cryptocurrency markets; however, during the COVID-19 period, the direction of causality shifted, with cryptocurrencies exerting influence on the gold and crude oil markets. These findings provide subtle implications for policymakers, hedge fund managers, and individual or institutional cryptocurrency investors. Our results highlight the need to adapt risk exposure strategies during financial turmoil, such as the crisis precipitated by the COVID-19 pandemic.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"157 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140932614","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 : 2024-05-03DOI: 10.1186/s40854-024-00614-6
Sharif Mozumder, M. Kabir Hassan, M. Humayun Kabir
This study investigates the simplicity and adequacy of tail-based risk measures—value-at-risk (VaR) and expected shortfall (ES)—when applied to tail targeting of the extreme value (EV) model. We implement Lévy–VaR and ES risk measures as full density-based alternatives to the generalized Pareto VaR and the generalized Pareto ES of the tail-targeting EV model. Using data on futures contracts of S&P500, FTSE100, DAX, Hang Seng, and Nikkei 225 during the Global Financial Crisis of 2007–2008, we find that the simplicity of tail-based risk management with a tail-targeting EV model is more attractive. However, the performance of EV risk estimates is not necessarily superior to that of full density-based relatively complex Lévy risk estimates, which may not always give us more robust VaR and ES results, making the model inadequate from a practical perspective. There is randomness in the estimation performances under both approaches for different data ranges and coverage levels. Such mixed results imply that banks, financial institutions, and policymakers should find a way to compromise or trade-off between “simplicity” and user-defined “adequacy”.
本研究探讨了基于尾部的风险度量--风险价值（VaR）和预期缺口（ES）--在应用于极值（EV）模型的尾部目标时的简单性和充分性。我们将 Lévy-VaR 和 ES 风险度量作为基于全密度的替代方案，以取代尾部目标极值模型的广义帕累托 VaR 和广义帕累托 ES。利用 2007-2008 年全球金融危机期间 S&P500、FTSE100、DAX、恒生指数和日经 225 指数期货合约的数据，我们发现使用尾部目标 EV 模型进行基于尾部的风险管理的简易性更具吸引力。然而，EV 风险估计的表现并不一定优于基于全密度的相对复杂的 Lévy 风险估计，后者不一定总能给我们带来更稳健的 VaR 和 ES 结果，这使得该模型在实用性方面存在不足。对于不同的数据范围和覆盖水平，两种方法的估计结果都存在随机性。这种好坏参半的结果意味着银行、金融机构和政策制定者应该在 "简单性 "和用户定义的 "充分性 "之间找到一种折中或权衡的方法。
{"title":"An evaluation of the adequacy of Lévy and extreme value tail risk estimates","authors":"Sharif Mozumder, M. Kabir Hassan, M. Humayun Kabir","doi":"10.1186/s40854-024-00614-6","DOIUrl":"https://doi.org/10.1186/s40854-024-00614-6","url":null,"abstract":"This study investigates the simplicity and adequacy of tail-based risk measures—value-at-risk (VaR) and expected shortfall (ES)—when applied to tail targeting of the extreme value (EV) model. We implement Lévy–VaR and ES risk measures as full density-based alternatives to the generalized Pareto VaR and the generalized Pareto ES of the tail-targeting EV model. Using data on futures contracts of S&P500, FTSE100, DAX, Hang Seng, and Nikkei 225 during the Global Financial Crisis of 2007–2008, we find that the simplicity of tail-based risk management with a tail-targeting EV model is more attractive. However, the performance of EV risk estimates is not necessarily superior to that of full density-based relatively complex Lévy risk estimates, which may not always give us more robust VaR and ES results, making the model inadequate from a practical perspective. There is randomness in the estimation performances under both approaches for different data ranges and coverage levels. Such mixed results imply that banks, financial institutions, and policymakers should find a way to compromise or trade-off between “simplicity” and user-defined “adequacy”.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"140 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140830626","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 : 2024-05-01DOI: 10.1186/s40854-023-00595-y
Muhammad Anas, Syed Jawad Hussain Shahzad, Larisa Yarovaya
As the crypto-asset ecosystem matures, the use of high-frequency data has become increasingly common in decentralized finance literature. Using bibliometric analysis, we characterize the existing cryptocurrency literature that employs high-frequency data. We highlighted the most influential authors, articles, and journals based on 189 articles from the Scopus database from 2015 to 2022. This approach enables us to identify emerging trends and research hotspots with the aid of co-citation and cartographic analyses. It shows knowledge expansion through authors’ collaboration in cryptocurrency research with co-authorship analysis. We identify four major streams of research: (i) return prediction and measurement of cryptocurrency volatility, (ii) (in)efficiency of cryptocurrencies, (iii) price dynamics and bubbles in cryptocurrencies, and (iv) the diversification, safe haven, and hedging properties of Bitcoin. We conclude that highly traded cryptocurrencies’ investment features and economic outcomes are analyzed predominantly on a tick-by-tick basis. This study also provides recommendations for future studies.
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Pub Date : 2024-04-20DOI: 10.1186/s40854-024-00644-0
Htet Htet Htun, Michael Biehl, Nicolai Petkov
Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fluctuate. The random walk hypothesis and efficient market hypothesis essentially state that it is not possible to systematically, reliably predict future stock prices or forecast changes in the stock market overall. Nonetheless, machine learning (ML) techniques that use historical data have been applied to make such predictions. Previous studies focused on a small number of stocks and claimed success with limited statistical confidence. In this study, we construct feature vectors composed of multiple previous relative returns and apply the random forest (RF), support vector machine (SVM), and long short-term memory (LSTM) ML methods as classifiers to predict whether a stock can return 2% more than its index in the following 10 days. We apply this approach to all S&P 500 companies for the period 2017–2022. We assess performance using accuracy, precision, and recall and compare our results with a random choice strategy. We observe that the LSTM classifier outperforms RF and SVM, and the data-driven ML methods outperform the random choice classifier (p = 8.46e−17 for accuracy of LSTM). Thus, we demonstrate that the probability that the random walk and efficient market hypotheses hold in the considered context is negligibly small.
由于导致股票价格波动的因素众多，因此预测股票价格的变化极具挑战性。随机漫步假说和有效市场假说的本质是，不可能系统、可靠地预测未来股票价格或预测股市的整体变化。然而，使用历史数据的机器学习（ML）技术已被用于进行此类预测。以前的研究主要集中在少数股票上，并声称取得了成功，但统计置信度有限。在本研究中，我们构建了由之前多个相对回报率组成的特征向量，并应用随机森林（RF）、支持向量机（SVM）和长短期记忆（LSTM）ML 方法作为分类器，来预测一只股票在接下来的 10 天内的回报率是否能比其指数高出 2%。我们将这种方法应用于 2017-2022 年期间的所有标准普尔 500 指数公司。我们使用准确率、精确度和召回率评估性能，并将结果与随机选择策略进行比较。我们发现，LSTM 分类器的表现优于 RF 和 SVM，而数据驱动的 ML 方法的表现优于随机选择分类器（LSTM 的准确率 p = 8.46e-17）。因此，我们证明，在所考虑的情况下，随机漫步和有效市场假说成立的概率小到可以忽略不计。
{"title":"Forecasting relative returns for S&P 500 stocks using machine learning","authors":"Htet Htet Htun, Michael Biehl, Nicolai Petkov","doi":"10.1186/s40854-024-00644-0","DOIUrl":"https://doi.org/10.1186/s40854-024-00644-0","url":null,"abstract":"Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fluctuate. The random walk hypothesis and efficient market hypothesis essentially state that it is not possible to systematically, reliably predict future stock prices or forecast changes in the stock market overall. Nonetheless, machine learning (ML) techniques that use historical data have been applied to make such predictions. Previous studies focused on a small number of stocks and claimed success with limited statistical confidence. In this study, we construct feature vectors composed of multiple previous relative returns and apply the random forest (RF), support vector machine (SVM), and long short-term memory (LSTM) ML methods as classifiers to predict whether a stock can return 2% more than its index in the following 10 days. We apply this approach to all S&P 500 companies for the period 2017–2022. We assess performance using accuracy, precision, and recall and compare our results with a random choice strategy. We observe that the LSTM classifier outperforms RF and SVM, and the data-driven ML methods outperform the random choice classifier (p = 8.46e−17 for accuracy of LSTM). Thus, we demonstrate that the probability that the random walk and efficient market hypotheses hold in the considered context is negligibly small.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"53 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627681","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 the data envelopment analysis (DEA) literature, productivity change captured by the Malmquist productivity index, especially in terms of a deterministic environment and stochastic variability in inputs and outputs, has been somewhat ignored. Therefore, this study developed a firm-specific, DEA-based Malmquist index model to examine the efficiency and productivity change of banks in a stochastic environment. First, in order to estimate bank-specific efficiency, we employed a two-stage double bootstrap DEA procedure. Specifically, in the first stage, the technical efficiency scores of banks were calculated by the classic DEA model, while in the second stage, the double bootstrap DEA model was applied to determine the effect of the contextual variables on bank efficiency. Second, we applied a two-stage procedure for measuring productivity change in which the first stage included the estimation of stochastic technical efficiency and the second stage included the regression of the estimated efficiency scores on a set of explanatory variables that influence relative performance. Finally, an empirical investigation of the Iranian banking sector, consisting of 120 bank-year observations of 15 banks from 2014 to 2021, was performed to measure their efficiency and productivity change. Based on the findings, the explanatory variables (i.e., the nonperforming loan ratio and the number of branches) indicated an inverse relationship with stochastic technical efficiency and productivity change. The implication of the findings is that, in order to improve the efficiency and productivity of banks, it is important to optimize these factors.
在数据包络分析（DEA）文献中，Malmquist 生产率指数所反映的生产率变化，尤其是在确定性环境以及投入和产出的随机变异性方面的生产率变化，在某种程度上被忽视了。因此，本研究建立了一个基于 DEA 的特定企业 Malmquist 指数模型，以考察随机环境下银行的效率和生产率变化。首先，为了估计特定银行的效率，我们采用了两阶段双引导 DEA 程序。具体来说，在第一阶段，通过经典 DEA 模型计算银行的技术效率得分，而在第二阶段，应用双引导 DEA 模型确定环境变量对银行效率的影响。其次，我们采用了两阶段程序来衡量生产率的变化，其中第一阶段包括随机技术效率的估算，第二阶段包括将估算的效率分数与一系列影响相对绩效的解释变量进行回归。最后，对伊朗银行业进行了实证调查，包括对 15 家银行从 2014 年到 2021 年的 120 个银行年的观察，以衡量其效率和生产率变化。调查结果显示，解释变量（即不良贷款率和分支机构数量）与随机技术效率和生产率变化呈反向关系。研究结果的含义是，为了提高银行的效率和生产率，必须优化这些因素。
{"title":"A firm-specific Malmquist productivity index model for stochastic data envelopment analysis: an application to commercial banks","authors":"Alireza Amirteimoori, Tofigh Allahviranloo, Maryam Nematizadeh","doi":"10.1186/s40854-023-00583-2","DOIUrl":"https://doi.org/10.1186/s40854-023-00583-2","url":null,"abstract":"In the data envelopment analysis (DEA) literature, productivity change captured by the Malmquist productivity index, especially in terms of a deterministic environment and stochastic variability in inputs and outputs, has been somewhat ignored. Therefore, this study developed a firm-specific, DEA-based Malmquist index model to examine the efficiency and productivity change of banks in a stochastic environment. First, in order to estimate bank-specific efficiency, we employed a two-stage double bootstrap DEA procedure. Specifically, in the first stage, the technical efficiency scores of banks were calculated by the classic DEA model, while in the second stage, the double bootstrap DEA model was applied to determine the effect of the contextual variables on bank efficiency. Second, we applied a two-stage procedure for measuring productivity change in which the first stage included the estimation of stochastic technical efficiency and the second stage included the regression of the estimated efficiency scores on a set of explanatory variables that influence relative performance. Finally, an empirical investigation of the Iranian banking sector, consisting of 120 bank-year observations of 15 banks from 2014 to 2021, was performed to measure their efficiency and productivity change. Based on the findings, the explanatory variables (i.e., the nonperforming loan ratio and the number of branches) indicated an inverse relationship with stochastic technical efficiency and productivity change. The implication of the findings is that, in order to improve the efficiency and productivity of banks, it is important to optimize these factors.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"6 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612467","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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