Transitioning from outcrossing to self-fertilization is a widespread reproductive strategy in plants,especially in environments where pollination is limited.Despite its prevalence,this transition has rarely been exami...Transitioning from outcrossing to self-fertilization is a widespread reproductive strategy in plants,especially in environments where pollination is limited.Despite its prevalence,this transition has rarely been examined using transplant experiments,and previous studies have overlooked the contribution of the male parent in elucidating mating diversity.In this study,six transplanted populations were generated to investigate the relationship of the pollination environment with plant mating patterns and fecundity in Primula oreodoxa,a species that exhibits both distyly(predominantly outcrossing)and homostyly(predominantly selfing),based on data from 3582 individuals and 11 SSR markers.Homostylous plants had fruit and seed sets comparable to those of distylous plants at lower elevations but exhibited a clear reproductive advantage at higher elevations,particularly compared with the S morph.As elevation increased,the populational selfing rates increased,and the genetic diversity among the progeny was reduced.Furthermore,the visitation frequency of long-tongued pollinators was negatively and positively correlated with the selfing rate and number of mates,respectively,in the L and S morphs.In contrast,short-tongued pollinator visitation showed opposite correlations with the selfing rate and number of mates in homostylous morphs.In most populations,individuals functioned consistently as both female and male,and mating occurred randomly,suggesting a breakdown of the distyly polymorphism.Overall,our results provide experimental validation of the reproductive advantages of homostyly at high elevations by revealing that pollinator visitation shapes the selfing rate and mating diversity within populations,potentially driving the divergence of mating systems along environmental gradients.展开更多
Portfolio theory has been extensively studied and applied in finance.To determine the optimal portfolio weight under the global minimum variance strategy,it is necessary to estimate both the covariance matrix and its ...Portfolio theory has been extensively studied and applied in finance.To determine the optimal portfolio weight under the global minimum variance strategy,it is necessary to estimate both the covariance matrix and its inverse.However,the high dimensionality and heavy-tailed nature of financial data pose significant challenges to this estimation.In this study,we propose a method to estimate the Gini covariance matrix by introducing a low-rank and sparse correlation structure,as an alternative to the traditional sample covariance matrix.Our approach employs a factor model to capture the low-rank structure,combined with thresholding rules to achieve the final estimation.We demonstrate the consistency of our estimators and validate our approach through simulation experiments and empirical portfolio analyses.Simulation results show that our method is highly applicable across a variety of distributional scenarios.Furthermore,empirical portfolio analysis indicates that our method can construct portfolios with superior performance.展开更多
This study assesses the portfolio concentration of socially responsible investment(SRI)pension funds,which may be subject to a potentially limited asset universe and have a higher concentration and lower performance t...This study assesses the portfolio concentration of socially responsible investment(SRI)pension funds,which may be subject to a potentially limited asset universe and have a higher concentration and lower performance than conventional funds.Nonetheless,in contrast to previous studies on SRI funds,this study considers the informationadvantage theory,positing that skilled managers should increase their concentration in assets in which they possess valuable information,departing from optimization models to achieve outperformance.This study first compares actual fund concentration with concentration obtained from several traditional and modern portfolio optimization techniques(minimum variance,global minimum variance,optimal portfolio,naïve diversification,risk parity,and reward-to-risk timing)to understand whether SRI pension funds concentrate portfolios and deviate from optimization model solutions.Unlike previous studies,the actual fund assets are considered in the optimization models to take into account the real investment profiles of SRI funds.The results indicate that SRI pension funds are less concentrated than conventional funds,and SRI and conventional pension funds largely diversify their portfolios,presenting lower concentration than portfolios formed with the optimization models.Furthermore,concentration strategies positively influence performance in SRI and conventional funds,revealing the use of information advantage.However,SRI and conventional fund managers present poor skills(picking,timing,and trading)to exploit information advantages due to overconfidence issues,which affect performance with concentration strategies.This situation may be modified if SRI funds follow modern optimization models and conventional funds follow traditional optimization models,improving managers’performance and skills.展开更多
Portfolio selection based on the global minimum variance(GMV)model remains a significant focus in financial research.The covariance matrix,central to the GMV model,determines portfolio weights,and its accurate estimat...Portfolio selection based on the global minimum variance(GMV)model remains a significant focus in financial research.The covariance matrix,central to the GMV model,determines portfolio weights,and its accurate estimation is key to effective strategies.Based on the decomposition form of the covariance matrix.This paper introduces semi-variance for improved financial asymmetric risk measurement;addresses asymmetry in financial asset correlations using distance,asymmetric,and Chatterjee correlations to refine covariance matrices;and proposes three new covariance matrix models to enhance risk assessment and portfolio selection strategies.Testing with data from 30 stocks across various sectors of the Chinese market confirms the strong performance of the proposed strategies.展开更多
This study investigates the return dynamics,volatility structure,and risk characteristics of five representative S&P 500 stocks:Johnson&Johnson,Microsoft,NVIDIA,Coca-Cola,and Home Depot,using ARMA-GARCH models...This study investigates the return dynamics,volatility structure,and risk characteristics of five representative S&P 500 stocks:Johnson&Johnson,Microsoft,NVIDIA,Coca-Cola,and Home Depot,using ARMA-GARCH models.Descriptive statistics and diagnostic tests confirm non-normality,negative skewness,fat tails,and volatility clustering,providing strong justification for conditional mean-variance modelling.Optimal model specifications are selected via the Bayesian Information Criterion,with EGARCH frameworks generally outperforming alternative GARCH variants in capturing asymmetric volatility responses.Rolling-window forecasts for 2024Q1 show that the models generate stable and reliable volatility predictions for low-volatility stocks(JNJ,KO),while performance is weaker for highly volatile stocks(NVDA),highlighting structural limitations under extreme market shifts.To evaluate risk management implications,one percent Value-at-Risk and expected shortfall were computed and backtested.Results indicated conservative tail-risk forecasts,with violation rates well within acceptable thresholds.Portfolio applications are further explored by constructing the Global Minimum Variance Portfolio(GMVP)and the Maximum Sharpe Ratio(Max SR)portfolio using rolling covariance estimates.Out-of-sample backtesting demonstrated that the GMVP delivered low volatility but modest returns,whereas the Max SR portfolio achieved significantly higher performance,consistent with the risk-return trade-off.Overall,the findings confirm that ARMA-GARCH models are effective tools for modelling conditional volatility and informing dynamic asset allocation.However,their limited adaptability to jump risk and nonlinear structural breaks underscores the need for more advanced modelling approaches in high-volatility environments.展开更多
The rapid advancement of quantum computing has sparked a considerable increase in research attention to quantum technologies.These advances span fundamental theoretical inquiries into quantum information and the explo...The rapid advancement of quantum computing has sparked a considerable increase in research attention to quantum technologies.These advances span fundamental theoretical inquiries into quantum information and the exploration of diverse applications arising from this evolving quantum computing paradigm.The scope of the related research is notably diverse.This paper consolidates and presents quantum computing research related to the financial sector.The finance applications considered in this study include portfolio optimization,fraud detection,and Monte Carlo methods for derivative pricing and risk calculation.In addition,we provide a comprehensive analysis of quantum computing’s applications and effects on blockchain technologies,particularly in relation to cryptocurrencies,which are central to financial technology research.As discussed in this study,quantum computing applications in finance are based on fundamental quantum physics principles and key quantum algorithms.This review aims to bridge the research gap between quantum computing and finance.We adopt a two-fold methodology,involving an analysis of quantum algorithms,followed by a discussion of their applications in specific financial contexts.Our study is based on an extensive review of online academic databases,search tools,online journal repositories,and whitepapers from 1952 to 2023,including CiteSeerX,DBLP,Research-Gate,Semantic Scholar,and scientific conference publications.We present state-of-theart findings at the intersection of finance and quantum technology and highlight open research questions that will be valuable for industry practitioners and academicians as they shape future research agendas.展开更多
The current portfolio model for property-liability insurance company is only single period that can not meet the practical demands of portfolio management, and the purpose of this paper is to develop a multiperiod mod...The current portfolio model for property-liability insurance company is only single period that can not meet the practical demands of portfolio management, and the purpose of this paper is to develop a multiperiod model for its portfolio problem. The model is a multistage stochastic programming which considers transaction costs, cash flow between time periods, and the matching of asset and liability; it does not depend on the assumption for normality of return distribution. Additionally, an investment constraint is added. The numerical example manifests that the multiperiod model can more effectively assist the property-liability insurer to determine the optimal composition of insurance and investment portfolio and outperforms the single period one.展开更多
基金funded by grants from the National Natural Science Foundation of China(31800314,32370239,U160323)the foundation of South China Botanical Garden,Chinese Academy of Sciences(QNXM-06)to SY and the Doctoral Research Foundation of China West Normal University(412994).
文摘Transitioning from outcrossing to self-fertilization is a widespread reproductive strategy in plants,especially in environments where pollination is limited.Despite its prevalence,this transition has rarely been examined using transplant experiments,and previous studies have overlooked the contribution of the male parent in elucidating mating diversity.In this study,six transplanted populations were generated to investigate the relationship of the pollination environment with plant mating patterns and fecundity in Primula oreodoxa,a species that exhibits both distyly(predominantly outcrossing)and homostyly(predominantly selfing),based on data from 3582 individuals and 11 SSR markers.Homostylous plants had fruit and seed sets comparable to those of distylous plants at lower elevations but exhibited a clear reproductive advantage at higher elevations,particularly compared with the S morph.As elevation increased,the populational selfing rates increased,and the genetic diversity among the progeny was reduced.Furthermore,the visitation frequency of long-tongued pollinators was negatively and positively correlated with the selfing rate and number of mates,respectively,in the L and S morphs.In contrast,short-tongued pollinator visitation showed opposite correlations with the selfing rate and number of mates in homostylous morphs.In most populations,individuals functioned consistently as both female and male,and mating occurred randomly,suggesting a breakdown of the distyly polymorphism.Overall,our results provide experimental validation of the reproductive advantages of homostyly at high elevations by revealing that pollinator visitation shapes the selfing rate and mating diversity within populations,potentially driving the divergence of mating systems along environmental gradients.
基金supported by the Postdoctoral Fellowship Program of CPSF(GZC20241651)the National Natural Science Foundation of China(12501391)the Natural Science Foundation of Anhui Province(2408085QA005).
文摘Portfolio theory has been extensively studied and applied in finance.To determine the optimal portfolio weight under the global minimum variance strategy,it is necessary to estimate both the covariance matrix and its inverse.However,the high dimensionality and heavy-tailed nature of financial data pose significant challenges to this estimation.In this study,we propose a method to estimate the Gini covariance matrix by introducing a low-rank and sparse correlation structure,as an alternative to the traditional sample covariance matrix.Our approach employs a factor model to capture the low-rank structure,combined with thresholding rules to achieve the final estimation.We demonstrate the consistency of our estimators and validate our approach through simulation experiments and empirical portfolio analyses.Simulation results show that our method is highly applicable across a variety of distributional scenarios.Furthermore,empirical portfolio analysis indicates that our method can construct portfolios with superior performance.
基金supported by Government of Aragon[Grant S38_20R]Ibercaja and University of Zaragoza[Grants JIUZ-2021-SOC-03 and JIUZ2022-CSJ-24]Ministerio de Ciencia e Innovación and FEDER[PID2022-136818NB-100].
文摘This study assesses the portfolio concentration of socially responsible investment(SRI)pension funds,which may be subject to a potentially limited asset universe and have a higher concentration and lower performance than conventional funds.Nonetheless,in contrast to previous studies on SRI funds,this study considers the informationadvantage theory,positing that skilled managers should increase their concentration in assets in which they possess valuable information,departing from optimization models to achieve outperformance.This study first compares actual fund concentration with concentration obtained from several traditional and modern portfolio optimization techniques(minimum variance,global minimum variance,optimal portfolio,naïve diversification,risk parity,and reward-to-risk timing)to understand whether SRI pension funds concentrate portfolios and deviate from optimization model solutions.Unlike previous studies,the actual fund assets are considered in the optimization models to take into account the real investment profiles of SRI funds.The results indicate that SRI pension funds are less concentrated than conventional funds,and SRI and conventional pension funds largely diversify their portfolios,presenting lower concentration than portfolios formed with the optimization models.Furthermore,concentration strategies positively influence performance in SRI and conventional funds,revealing the use of information advantage.However,SRI and conventional fund managers present poor skills(picking,timing,and trading)to exploit information advantages due to overconfidence issues,which affect performance with concentration strategies.This situation may be modified if SRI funds follow modern optimization models and conventional funds follow traditional optimization models,improving managers’performance and skills.
基金National Natural Science Foundation of China(Project No.:12201579)。
文摘Portfolio selection based on the global minimum variance(GMV)model remains a significant focus in financial research.The covariance matrix,central to the GMV model,determines portfolio weights,and its accurate estimation is key to effective strategies.Based on the decomposition form of the covariance matrix.This paper introduces semi-variance for improved financial asymmetric risk measurement;addresses asymmetry in financial asset correlations using distance,asymmetric,and Chatterjee correlations to refine covariance matrices;and proposes three new covariance matrix models to enhance risk assessment and portfolio selection strategies.Testing with data from 30 stocks across various sectors of the Chinese market confirms the strong performance of the proposed strategies.
文摘This study investigates the return dynamics,volatility structure,and risk characteristics of five representative S&P 500 stocks:Johnson&Johnson,Microsoft,NVIDIA,Coca-Cola,and Home Depot,using ARMA-GARCH models.Descriptive statistics and diagnostic tests confirm non-normality,negative skewness,fat tails,and volatility clustering,providing strong justification for conditional mean-variance modelling.Optimal model specifications are selected via the Bayesian Information Criterion,with EGARCH frameworks generally outperforming alternative GARCH variants in capturing asymmetric volatility responses.Rolling-window forecasts for 2024Q1 show that the models generate stable and reliable volatility predictions for low-volatility stocks(JNJ,KO),while performance is weaker for highly volatile stocks(NVDA),highlighting structural limitations under extreme market shifts.To evaluate risk management implications,one percent Value-at-Risk and expected shortfall were computed and backtested.Results indicated conservative tail-risk forecasts,with violation rates well within acceptable thresholds.Portfolio applications are further explored by constructing the Global Minimum Variance Portfolio(GMVP)and the Maximum Sharpe Ratio(Max SR)portfolio using rolling covariance estimates.Out-of-sample backtesting demonstrated that the GMVP delivered low volatility but modest returns,whereas the Max SR portfolio achieved significantly higher performance,consistent with the risk-return trade-off.Overall,the findings confirm that ARMA-GARCH models are effective tools for modelling conditional volatility and informing dynamic asset allocation.However,their limited adaptability to jump risk and nonlinear structural breaks underscores the need for more advanced modelling approaches in high-volatility environments.
基金Gerhard Hellstern is partly funded by the Ministry of Economic Affairs,Labour and Tourism Baden-Württemberg in the frame of the Competence Center Quantum Computing Baden-Württemberg(QORA Ⅱ).
文摘The rapid advancement of quantum computing has sparked a considerable increase in research attention to quantum technologies.These advances span fundamental theoretical inquiries into quantum information and the exploration of diverse applications arising from this evolving quantum computing paradigm.The scope of the related research is notably diverse.This paper consolidates and presents quantum computing research related to the financial sector.The finance applications considered in this study include portfolio optimization,fraud detection,and Monte Carlo methods for derivative pricing and risk calculation.In addition,we provide a comprehensive analysis of quantum computing’s applications and effects on blockchain technologies,particularly in relation to cryptocurrencies,which are central to financial technology research.As discussed in this study,quantum computing applications in finance are based on fundamental quantum physics principles and key quantum algorithms.This review aims to bridge the research gap between quantum computing and finance.We adopt a two-fold methodology,involving an analysis of quantum algorithms,followed by a discussion of their applications in specific financial contexts.Our study is based on an extensive review of online academic databases,search tools,online journal repositories,and whitepapers from 1952 to 2023,including CiteSeerX,DBLP,Research-Gate,Semantic Scholar,and scientific conference publications.We present state-of-theart findings at the intersection of finance and quantum technology and highlight open research questions that will be valuable for industry practitioners and academicians as they shape future research agendas.
文摘The current portfolio model for property-liability insurance company is only single period that can not meet the practical demands of portfolio management, and the purpose of this paper is to develop a multiperiod model for its portfolio problem. The model is a multistage stochastic programming which considers transaction costs, cash flow between time periods, and the matching of asset and liability; it does not depend on the assumption for normality of return distribution. Additionally, an investment constraint is added. The numerical example manifests that the multiperiod model can more effectively assist the property-liability insurer to determine the optimal composition of insurance and investment portfolio and outperforms the single period one.
