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
Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of si...Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.展开更多
Losses due to hazards are inevitable and numerical simulations for estimations are complex.This study proposes a model for estimating correlated seismic damages and losses of a water supply pipeline system as an alter...Losses due to hazards are inevitable and numerical simulations for estimations are complex.This study proposes a model for estimating correlated seismic damages and losses of a water supply pipeline system as an alternative for numerical simulations.The common approach in other research shows average damage spots per mesh estimated statistically independent to one another.Spatially distributed lifeline systems,such as water supply pipelines,are interconnected,and seismic spatial variability affects the damages across the region;thus,spatial correlation of damage spots is an important factor in target areas for portfolio loss estimation.Generally,simulations are used to estimate possible losses;however,these assume each damage behaves independently and uncorrelated.This paper assumed that damages per mesh behave in a Poisson distribution to avoid over-dispersion and eliminate negative losses in estimations.The purpose of this study is to obtain a probabilistic portfolio loss model of an extensive water supply area.The proposed model was compared to the numerical simulation data with the correlated Poisson distribution.The application of the Normal To Anything(NORTA)obtained correlations for Poisson Distributions.The proposed probabilistic portfolio loss model,based on the generalized linear model and central limit theory,estimated the possible losses,such as the Probable Maximum Loss(PML,90%non-exceedance)or Normal Expected Loss(NEL,50%non-exceedance).The proposed model can be used in other lifeline systems as well,though additional investigation is needed for confirmation.From the estimations,a seismic physical portfolio loss for the water supply system was presented.The portfolio was made to show possible outcomes for the system.The proposed method was tested and analyzed using an artificial field and a location-based scenario of a water supply pipeline system.This would aid in pre-disaster planning and would require only a few steps and time.展开更多
Modern financial theory, commonly known as portfolio theory, provides an analytical framework for the investment decision to be made under uncertainty. It is a well-established proposition in portfolio theory that whe...Modern financial theory, commonly known as portfolio theory, provides an analytical framework for the investment decision to be made under uncertainty. It is a well-established proposition in portfolio theory that whenever there is an imperfect correlation between returns risk is reduced by maintaining only a portion of wealth in any asset, or by selecting a portfolio according to expected returns and correlations between returns. The major improvement of the portfolio approaches over prior received theory is the incorporation of 1) the riskiness of an asset and 2) the addition from investing in any asset. The theme of this paper is to discuss how to propose a new mathematical model like that provided by Markowitz, which helps in choosing a nearly perfect portfolio and an efficient input/output. Besides applying this model to reality, the researcher uses game theory, stochastic and linear programming to provide the model proposed and then uses this model to select a perfect portfolio in the Cairo Stock Exchange. The results are fruitful and the researcher considers this model a new contribution to previous models.展开更多
In recent years, digital investment portfolios have become a significant area of interest in the field of machine learning. To tackle the issue of neglecting the momentum effect in risk asset prices within the follow-...In recent years, digital investment portfolios have become a significant area of interest in the field of machine learning. To tackle the issue of neglecting the momentum effect in risk asset prices within the follow-the-winner strategy and to evaluate the significance of this effect, a novel measure of risk asset price momentum trend is introduced for online investment portfolio research. Firstly, a novel approach is introduced to quantify the momentum trend effect, which is determined by the product of the slope of the linear regression model and the absolute value of the linear correlation coefficient. Secondly, a new investment portfolio optimization problem is established based on the prediction of future returns. Thirdly, the Lagrange multiplier method is used to obtain the analytical solution of the optimization model, and the soft projection optimization algorithm is used to map the analytical solution to obtain the investment portfolio of the model. Finally, experiments are conducted on five benchmark datasets and compared with popular investment portfolio algorithms. The empirical findings indicate that the algorithm we are introduced is capable of generating higher investment returns, thereby establishing its efficacy for the management of the online investment portfolios.展开更多
This study employs a variety of machine learning models and a wide range of economic and financial variables to enhance the forecasting accuracy of the Korean won–U.S.dollar(KRW/USD)exchange rate and the U.S.and Kore...This study employs a variety of machine learning models and a wide range of economic and financial variables to enhance the forecasting accuracy of the Korean won–U.S.dollar(KRW/USD)exchange rate and the U.S.and Korean stock market returns.We construct international asset allocation portfolios based on these forecasts and evaluate their performance.Our analysis finds that the Elastic Net and LASSO regression models outperform traditional benchmark models in predicting exchange rate and stock market returns,as evidenced by their superior out-of-sample R-squared values.We also identify the key factors crucial for improving the accuracy of forecasting the KRW/USD exchange rate and stock market returns.Furthermore,a machine learning-driven global portfolio that accounts for exchange rate fluctuations demonstrated superior performance.Global portfolios constructed using LASSO(Sharpe ratio=3.45)and Elastic Net(Sharpe ratio=3.48)exhibit a notable performance advantage over traditional benchmark portfolios.This suggests that machine learning models outperform traditional global portfolio construction methods.展开更多
International portfolio management is influenced by the existence of“frictions”,factors or events that interfere with trade,which are linked in financial literature to market-specific factors,such as available infor...International portfolio management is influenced by the existence of“frictions”,factors or events that interfere with trade,which are linked in financial literature to market-specific factors,such as available information,restrictions,investor protection,or market liquidity.Given the wide variety of factors that can be included in these categories,scientific studies typically focus on a reduced number of indicators at a time in order to offer an in depth analysis of their impact.We offer a consolidated view of the perspectives observed in financial literature by proposing a novel index for market frictions that includes all these four components and rank fifteen post-communist East European capital markets based on their index values.We then constructed various scenarios by assuming different levels of importance for the criteria used in index construction.By employing grey clustering analysis,we cluster these capital markets into three categories—strongly recommended,recommended with some reserve,and not recommended—based on the importance given by the decision maker to these factors.The results show that some of the studied markets are in the same cluster,irrespective of the chosen scenario.The only market always included in the“strongly recommended”category is Hungary,indicating that it is a good investment option for international participants.Bulgaria and Slovakia are always regarded as“recommended with reserve”markets,whereas the Republic of Moldova is part of the“not recommended”category.The other markets show a degree of variability that can be explained by different investor perspectives.This study contributes to the existing literature by combining the advantages of grey clustering and portfolio analysis.Investors can use this approach during the decision-making process related to their investments.展开更多
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.
文摘Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.
文摘Losses due to hazards are inevitable and numerical simulations for estimations are complex.This study proposes a model for estimating correlated seismic damages and losses of a water supply pipeline system as an alternative for numerical simulations.The common approach in other research shows average damage spots per mesh estimated statistically independent to one another.Spatially distributed lifeline systems,such as water supply pipelines,are interconnected,and seismic spatial variability affects the damages across the region;thus,spatial correlation of damage spots is an important factor in target areas for portfolio loss estimation.Generally,simulations are used to estimate possible losses;however,these assume each damage behaves independently and uncorrelated.This paper assumed that damages per mesh behave in a Poisson distribution to avoid over-dispersion and eliminate negative losses in estimations.The purpose of this study is to obtain a probabilistic portfolio loss model of an extensive water supply area.The proposed model was compared to the numerical simulation data with the correlated Poisson distribution.The application of the Normal To Anything(NORTA)obtained correlations for Poisson Distributions.The proposed probabilistic portfolio loss model,based on the generalized linear model and central limit theory,estimated the possible losses,such as the Probable Maximum Loss(PML,90%non-exceedance)or Normal Expected Loss(NEL,50%non-exceedance).The proposed model can be used in other lifeline systems as well,though additional investigation is needed for confirmation.From the estimations,a seismic physical portfolio loss for the water supply system was presented.The portfolio was made to show possible outcomes for the system.The proposed method was tested and analyzed using an artificial field and a location-based scenario of a water supply pipeline system.This would aid in pre-disaster planning and would require only a few steps and time.
文摘Modern financial theory, commonly known as portfolio theory, provides an analytical framework for the investment decision to be made under uncertainty. It is a well-established proposition in portfolio theory that whenever there is an imperfect correlation between returns risk is reduced by maintaining only a portion of wealth in any asset, or by selecting a portfolio according to expected returns and correlations between returns. The major improvement of the portfolio approaches over prior received theory is the incorporation of 1) the riskiness of an asset and 2) the addition from investing in any asset. The theme of this paper is to discuss how to propose a new mathematical model like that provided by Markowitz, which helps in choosing a nearly perfect portfolio and an efficient input/output. Besides applying this model to reality, the researcher uses game theory, stochastic and linear programming to provide the model proposed and then uses this model to select a perfect portfolio in the Cairo Stock Exchange. The results are fruitful and the researcher considers this model a new contribution to previous models.
文摘In recent years, digital investment portfolios have become a significant area of interest in the field of machine learning. To tackle the issue of neglecting the momentum effect in risk asset prices within the follow-the-winner strategy and to evaluate the significance of this effect, a novel measure of risk asset price momentum trend is introduced for online investment portfolio research. Firstly, a novel approach is introduced to quantify the momentum trend effect, which is determined by the product of the slope of the linear regression model and the absolute value of the linear correlation coefficient. Secondly, a new investment portfolio optimization problem is established based on the prediction of future returns. Thirdly, the Lagrange multiplier method is used to obtain the analytical solution of the optimization model, and the soft projection optimization algorithm is used to map the analytical solution to obtain the investment portfolio of the model. Finally, experiments are conducted on five benchmark datasets and compared with popular investment portfolio algorithms. The empirical findings indicate that the algorithm we are introduced is capable of generating higher investment returns, thereby establishing its efficacy for the management of the online investment portfolios.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2022S1A5A8055710).
文摘This study employs a variety of machine learning models and a wide range of economic and financial variables to enhance the forecasting accuracy of the Korean won–U.S.dollar(KRW/USD)exchange rate and the U.S.and Korean stock market returns.We construct international asset allocation portfolios based on these forecasts and evaluate their performance.Our analysis finds that the Elastic Net and LASSO regression models outperform traditional benchmark models in predicting exchange rate and stock market returns,as evidenced by their superior out-of-sample R-squared values.We also identify the key factors crucial for improving the accuracy of forecasting the KRW/USD exchange rate and stock market returns.Furthermore,a machine learning-driven global portfolio that accounts for exchange rate fluctuations demonstrated superior performance.Global portfolios constructed using LASSO(Sharpe ratio=3.45)and Elastic Net(Sharpe ratio=3.48)exhibit a notable performance advantage over traditional benchmark portfolios.This suggests that machine learning models outperform traditional global portfolio construction methods.
文摘International portfolio management is influenced by the existence of“frictions”,factors or events that interfere with trade,which are linked in financial literature to market-specific factors,such as available information,restrictions,investor protection,or market liquidity.Given the wide variety of factors that can be included in these categories,scientific studies typically focus on a reduced number of indicators at a time in order to offer an in depth analysis of their impact.We offer a consolidated view of the perspectives observed in financial literature by proposing a novel index for market frictions that includes all these four components and rank fifteen post-communist East European capital markets based on their index values.We then constructed various scenarios by assuming different levels of importance for the criteria used in index construction.By employing grey clustering analysis,we cluster these capital markets into three categories—strongly recommended,recommended with some reserve,and not recommended—based on the importance given by the decision maker to these factors.The results show that some of the studied markets are in the same cluster,irrespective of the chosen scenario.The only market always included in the“strongly recommended”category is Hungary,indicating that it is a good investment option for international participants.Bulgaria and Slovakia are always regarded as“recommended with reserve”markets,whereas the Republic of Moldova is part of the“not recommended”category.The other markets show a degree of variability that can be explained by different investor perspectives.This study contributes to the existing literature by combining the advantages of grey clustering and portfolio analysis.Investors can use this approach during the decision-making process related to their investments.
