Safety is essential when building a strong transportation system.As a key development direction in the global railway system,the intelligent railway has safety at its core,making safety a top priority while pursuing t...Safety is essential when building a strong transportation system.As a key development direction in the global railway system,the intelligent railway has safety at its core,making safety a top priority while pursuing the goals of efficiency,convenience,economy,and environmental friendliness.This paper describes the state of the art and proposes a system architecture for intelligent railway systems.It also focuses on the development of railway safety technology at home and abroad,and proposes the active safety method and technology system based on advanced theoretical methods such as the in-depth integration of cyber–physical systems(CPS),data-driven models,and intelligent computing.Finally,several typical applications are demonstrated to verify the advancement and feasibility of active safety technology in intelligent railway systems.展开更多
Convertible bonds are an important segment of the corporate bond market,however,as hybrid instruments,convertible bonds are difficult to value because they depend on variables related to the underlying stock,the fixed...Convertible bonds are an important segment of the corporate bond market,however,as hybrid instruments,convertible bonds are difficult to value because they depend on variables related to the underlying stock,the fixed-income part,and the interaction between these components.Besides,embedded options,such as conversion,call,and put provisions are often restricted to certain periods,may vary over time,and are subject to additional path-dependent features of the state variables.Moreover,the most challenging problem in convertible bond valuation is the underlying stock return process modeling as it retains various complex statistical properties.In this paper,we propose DeepPricing,a novel data-driven convertible bonds pricing model,which is inspired by the recent success of generative adversarial networks(GAN),to address the above challenges.The method introduces a new financial time-series generative adversarial networks(FinGAN),which is able to reproduce risk-neutral stock return process that retains the unique statistical properties such as the fat-tailed distributions,the long-range dependence,and the asymmetry structure etc.,and then transit to its risk-neutral distribution.Thus it is more flexible and accurate to capture the dynamics of the underlying stock return process and keep the rich set of real-world convertible bond specifications compared with previous model-driven models.The experiments on the Chinese convertible bond market demonstrate the effectiveness of DeepPricing model.Compared with the convertible bond market prices,our model has a better convertible bonds pricing performance than both model-driven models,i.e.Black-Scholes,the constant elasticity of variance,GARCH,and the state-of-the-art GAN-based models,i.e.FinGAN-MLP,FinGAN-LSTM.Moreover,our model has a better fitting capacity for higher-volatility convertible bonds and the overall convertible bond market implied volatility smirk,especially for equity-liked convertible bonds,convertible bonds trading in the bull market,and out-of-the-money convertible bonds.Furthermore,the Long-Short and Long-Only investment strategies based on our model earn a significant annualized return with 41.16%and 31.06%,respectively,for the equally-weighted portfolio during the sample period.展开更多
基金supported by the 2021 Chinese Academy of Engineering(CAE)International Top-level Forum on Engineering Science and Technology,“Safety and Governance of the High-Speed Railway”。
文摘Safety is essential when building a strong transportation system.As a key development direction in the global railway system,the intelligent railway has safety at its core,making safety a top priority while pursuing the goals of efficiency,convenience,economy,and environmental friendliness.This paper describes the state of the art and proposes a system architecture for intelligent railway systems.It also focuses on the development of railway safety technology at home and abroad,and proposes the active safety method and technology system based on advanced theoretical methods such as the in-depth integration of cyber–physical systems(CPS),data-driven models,and intelligent computing.Finally,several typical applications are demonstrated to verify the advancement and feasibility of active safety technology in intelligent railway systems.
基金supported by the Postdoctoral Science Foundation of China(Project No.2021M700055)。
文摘Convertible bonds are an important segment of the corporate bond market,however,as hybrid instruments,convertible bonds are difficult to value because they depend on variables related to the underlying stock,the fixed-income part,and the interaction between these components.Besides,embedded options,such as conversion,call,and put provisions are often restricted to certain periods,may vary over time,and are subject to additional path-dependent features of the state variables.Moreover,the most challenging problem in convertible bond valuation is the underlying stock return process modeling as it retains various complex statistical properties.In this paper,we propose DeepPricing,a novel data-driven convertible bonds pricing model,which is inspired by the recent success of generative adversarial networks(GAN),to address the above challenges.The method introduces a new financial time-series generative adversarial networks(FinGAN),which is able to reproduce risk-neutral stock return process that retains the unique statistical properties such as the fat-tailed distributions,the long-range dependence,and the asymmetry structure etc.,and then transit to its risk-neutral distribution.Thus it is more flexible and accurate to capture the dynamics of the underlying stock return process and keep the rich set of real-world convertible bond specifications compared with previous model-driven models.The experiments on the Chinese convertible bond market demonstrate the effectiveness of DeepPricing model.Compared with the convertible bond market prices,our model has a better convertible bonds pricing performance than both model-driven models,i.e.Black-Scholes,the constant elasticity of variance,GARCH,and the state-of-the-art GAN-based models,i.e.FinGAN-MLP,FinGAN-LSTM.Moreover,our model has a better fitting capacity for higher-volatility convertible bonds and the overall convertible bond market implied volatility smirk,especially for equity-liked convertible bonds,convertible bonds trading in the bull market,and out-of-the-money convertible bonds.Furthermore,the Long-Short and Long-Only investment strategies based on our model earn a significant annualized return with 41.16%and 31.06%,respectively,for the equally-weighted portfolio during the sample period.
