Time-domain state-domain methods are common approaches in modern financial analysis.Economic conditions vary time,drift function depends on time and price level for a given state variable.In this paper,to consistently...Time-domain state-domain methods are common approaches in modern financial analysis.Economic conditions vary time,drift function depends on time and price level for a given state variable.In this paper,to consistently estimate the bivariate drift function,our purpose a new dynamic integrated estimator by combing time-and state-domain methods for estimating drift function.And we establish its asymptotic properties and illustrates it outperforms some old ones by simulations.展开更多
This paper considers a nonparametric diffusion process whose drift and diffusion coefficients are nonparametric functions of the state variable.A two-step approach to estimate the drift function of a jump-diffusion mo...This paper considers a nonparametric diffusion process whose drift and diffusion coefficients are nonparametric functions of the state variable.A two-step approach to estimate the drift function of a jump-diffusion model in noisy settings is proposed.The proposed estimator is shown to be consistent and asymptotically normal in the presence of finite activity jumps.Simulated experiments and a real data application are undertaken to assess the finite sample performance of the newly proposed method.展开更多
There are three parts in this article. In Section 1, we establish the model of branching chain with drift in space-time random environment （BCDSTRE）, i.e., the coupling of branching chain and random walk. In Section...There are three parts in this article. In Section 1, we establish the model of branching chain with drift in space-time random environment （BCDSTRE）, i.e., the coupling of branching chain and random walk. In Section 2, we prove that any BCDSTRE must be a Markov chain in time random environment when we consider the distribution of the particles in space as a random element. In Section 3, we calculate the first-order moments and the second-order moments of BCDSTRE.展开更多
基金Supported by the Natural Science Research Foundation of Education Department of Guizhou Province(20090080,2010076)Supported by the Project of Kaili University(Z1004)Supported by the Key Discipline Construction Program of Kaili University(KZD2009001)
文摘Time-domain state-domain methods are common approaches in modern financial analysis.Economic conditions vary time,drift function depends on time and price level for a given state variable.In this paper,to consistently estimate the bivariate drift function,our purpose a new dynamic integrated estimator by combing time-and state-domain methods for estimating drift function.And we establish its asymptotic properties and illustrates it outperforms some old ones by simulations.
基金the National Natural Science Foundation of China under Grant No.11961038Cultivating Project of National Natural Science Foundation(QianKeHe talent-development platform[2017]No.5723,QianKeHe talent-development platform[2017]No.5723-02)+7 种基金supported by the National Natural Science Foundation of China under Grant Nos.12071220,11701286supported by the National Natural Science Foundation of China under Grant Nos.11831008,11971235Young Talents Project of Science and Technology Research Program of Education Department in Guizhou Province(Qianjiao KYword[2018]364)Science and Technology Foundation of Guizhou Province(QianKeHejichu[2019]1286)Social Science Foundation of Jiangsu Province under Grant No.20EYC008the National Statistical Research Project of China under Grant No.2020LZ35the National Statistical Research Project of China under Grant No.2020LZ19Open Project of Jiangsu Key Laboratory of Financial Engineering under Grant No.NSK2021-12。
文摘This paper considers a nonparametric diffusion process whose drift and diffusion coefficients are nonparametric functions of the state variable.A two-step approach to estimate the drift function of a jump-diffusion model in noisy settings is proposed.The proposed estimator is shown to be consistent and asymptotically normal in the presence of finite activity jumps.Simulated experiments and a real data application are undertaken to assess the finite sample performance of the newly proposed method.
基金Supported by the NSFC(10371092,11771185,10871200)
文摘There are three parts in this article. In Section 1, we establish the model of branching chain with drift in space-time random environment （BCDSTRE）, i.e., the coupling of branching chain and random walk. In Section 2, we prove that any BCDSTRE must be a Markov chain in time random environment when we consider the distribution of the particles in space as a random element. In Section 3, we calculate the first-order moments and the second-order moments of BCDSTRE.
