特征线方法(Method of Characteristics,MOC)因其具备强大的几何处理能力,且在计算过程中亦能兼顾计算成本和计算精度,被广泛应用于高保真数值模拟计算中。常见的中子输运计算方法除MOC外,还包括碰撞概率法(Collision Probability metho...特征线方法(Method of Characteristics,MOC)因其具备强大的几何处理能力,且在计算过程中亦能兼顾计算成本和计算精度,被广泛应用于高保真数值模拟计算中。常见的中子输运计算方法除MOC外,还包括碰撞概率法(Collision Probability method,CP)和界面流法(Interface Current method,IC)等。本文从方法理论以及数值计算两方面将MOC、CP和IC进行比较分析,评估其在pin-by-pin计算中的能力。同时在MOC计算中,不同的参数选择会对计算成本和计算精度产生影响,因此有必要进行敏感性分析以寻求最佳参数。本文首先将三种计算方法从原理上进行比较分析,再基于2D C5G7-MOX基准题完成了数值计算及MOC参数敏感性初步分析。计算结果表明:MOC在计算精度、计算效率和内存开销上均优于CP和IC。MOC的计算耗时和内存开销分别为23.9 min和37.5 MB,与参考解的相对误差仅为6.04×10^(-4)。而CP和IC的计算耗时分别为MOC的56.7倍和15.6倍,内存开销分别为MOC的407.7倍和32.8倍。进一步通过参数敏感性分析发现:网格划分对计算内存开销以及计算时间的影响最大,而极角的选择对计算精度的影响最大,并且给出一组综合优化建议参数:网格划分6×6,极角为GAUS且数目为2,方位角个数为30。该组参数的计算耗时为45.4 min,内存开销为264.7 MB,相对误差为5.9×10^(-5),归一化后的栅元均方根误差为0.002 55。展开更多
This study examines the link between stocks and decentralized finance(DeFi)in terms of returns and volatility.Major G7 exchange-traded funds(ETFs)and various highly traded DeFi assets are considered to ensure the robu...This study examines the link between stocks and decentralized finance(DeFi)in terms of returns and volatility.Major G7 exchange-traded funds(ETFs)and various highly traded DeFi assets are considered to ensure the robustness of the empirical experiment.Specifically,this study applies the vector autoregression generalized autoregressive conditional heteroskedasticity(VAR-GARCH)model to examine the information transmission of these two markets on a two-way basis and the dynamic conditional correlation(DCC)-GARCH model to assess the bivariate correlation structure between each DeFi and ETF pair.The volatility spillover analysis proves a contagion effect occurred between different geographic markets,and even between markets of different natures and typologies,during the most turbulent moments of the COVID-19 crisis and the war in the Ukraine.Our results also reveal a weak positive correlation between most DeFi and ETF pairs and positive hedge ratios that approach unity during turbulent times.In addition,DeFi assets,except for the Bazaar(BZR)Protocol,can offer diversification gains when included in financial investment portfolios.These results are particularly relevant for portfolio managers and policy-makers when designing investment strategies,especially during periods of financial crisis.展开更多
文摘特征线方法(Method of Characteristics,MOC)因其具备强大的几何处理能力,且在计算过程中亦能兼顾计算成本和计算精度,被广泛应用于高保真数值模拟计算中。常见的中子输运计算方法除MOC外,还包括碰撞概率法(Collision Probability method,CP)和界面流法(Interface Current method,IC)等。本文从方法理论以及数值计算两方面将MOC、CP和IC进行比较分析,评估其在pin-by-pin计算中的能力。同时在MOC计算中,不同的参数选择会对计算成本和计算精度产生影响,因此有必要进行敏感性分析以寻求最佳参数。本文首先将三种计算方法从原理上进行比较分析,再基于2D C5G7-MOX基准题完成了数值计算及MOC参数敏感性初步分析。计算结果表明:MOC在计算精度、计算效率和内存开销上均优于CP和IC。MOC的计算耗时和内存开销分别为23.9 min和37.5 MB,与参考解的相对误差仅为6.04×10^(-4)。而CP和IC的计算耗时分别为MOC的56.7倍和15.6倍,内存开销分别为MOC的407.7倍和32.8倍。进一步通过参数敏感性分析发现:网格划分对计算内存开销以及计算时间的影响最大,而极角的选择对计算精度的影响最大,并且给出一组综合优化建议参数:网格划分6×6,极角为GAUS且数目为2,方位角个数为30。该组参数的计算耗时为45.4 min,内存开销为264.7 MB,相对误差为5.9×10^(-5),归一化后的栅元均方根误差为0.002 55。
基金supported by Ministerio de Ciencia e Innovacion(PID2021-128829NB-100)funded by MCIN/AEI/10.13039/501100011033+1 种基金by"ERDF A way of making Europe'as wellas the Junta de Comunidades de Castlla-La Mancha(SBPLY/21/180501/000086)the Universidad de Castlla-La Mancha(2022-GRIN-34491),both of which were co-financed with ERDF funds.
文摘This study examines the link between stocks and decentralized finance(DeFi)in terms of returns and volatility.Major G7 exchange-traded funds(ETFs)and various highly traded DeFi assets are considered to ensure the robustness of the empirical experiment.Specifically,this study applies the vector autoregression generalized autoregressive conditional heteroskedasticity(VAR-GARCH)model to examine the information transmission of these two markets on a two-way basis and the dynamic conditional correlation(DCC)-GARCH model to assess the bivariate correlation structure between each DeFi and ETF pair.The volatility spillover analysis proves a contagion effect occurred between different geographic markets,and even between markets of different natures and typologies,during the most turbulent moments of the COVID-19 crisis and the war in the Ukraine.Our results also reveal a weak positive correlation between most DeFi and ETF pairs and positive hedge ratios that approach unity during turbulent times.In addition,DeFi assets,except for the Bazaar(BZR)Protocol,can offer diversification gains when included in financial investment portfolios.These results are particularly relevant for portfolio managers and policy-makers when designing investment strategies,especially during periods of financial crisis.
