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[2018.8.13-8.31] 数学与系统科学青年精英学术活动季
举办时间:八月    地点:数学院南楼N204

  为增进国内外优秀青年科研人才与系统科学研究所的相互了解与学术合作,系统科学研究所将于2018年6—8月举办为期一个季度的“数学与系统科学青年精英学术活动季”。活动期间,将邀请20位左右近年获得博士学位的系统科学相关领域优秀青年学者来访,并作短课程和学术报告。以下是短课程、学术报告和报告人的相关信息:

      

程旭(美国宾夕法尼亚大学)

8月15日上午9:30-10:15

An Averaging GMM Estimator Robust to Model Misspecification

【Abstract】This paper studies the averaging generalized method of moments (GMM) estimator that combines a conservative GMM estimator based on valid moment conditions and an aggressive GMM estimator based on both valid and possibly misspecified moment conditions, where the weight is the sample analog of an infeasible optimal weight. We establish asymptotic theory on uniform approximation of the upper and lower bounds of the finite-sample truncated risk difference between two estimators, which is used to compare the averaging GMM estimator and the conservative GMM estimator. Under some sufficient conditions, we show that the asymptotic lower bound of the truncated risk difference between the averaging estimator and the conservative estimator is strictly less than zero, while the asymptotic upper bound is zero uniformly over any degree of misspecification. Extending seminal results on the James-Stein estimator, this uniform dominance is established in non-Gaussian semiparametric nonlinear models. The simulation results support our theoretical findings.

 

崔丽媛(香港城市大学)

8月15日10:30-15:00

Solving Asset Pricing Models via 2 Stage Penalized B-spline Regression

【Abstract】This study proposes a novel nonparametric estimation approach to solving structural asset pricing models, which  allows  the  true  dynamics  of  state  variables  to  determine  equilibrium asset prices. Unlike  most  numerical  solution  methods,  our  method  offers  a  more  robust  estimate of the model solution for asset prices without misspecification errors  about  the  true underlying processes of state variables or the form of unknown functions while also taking into account  investors’  preferences.  Through  a  2  stage  penalized  B-spline  regression,  we  establish the asymptotics of the estimation for a broad class of stationary Markov state variables. Our estimator overcomes the ill-posed inverse problem which is typical in nonparametric instrumental variable regression, and achieves the optimal convergence rate. We design a fast generalized cross-validation  procedure  to  tune  the  penalty  parameter  effectively  for  ease  of  practical  use. In addition to being robust to the choice  of  the  spline  basis,  our  approach  exhibits  superior accuracy in small samples.  As an application, we estimate a misspecification-free implied dividend yield from a rational  model  and  re-investigate  return  predictability.  We  find  that  high implied  dividend  yield  significantly  predicts  lower  future  cash  flows  and  higher  interest  rates at short horizons; however, we find no evidence for its ability to forecast returns in the period from 1947-2017.

 

冯亮(重庆大学)

8月15日11:15-12:00

Transfer Optimization & Multi-Task Optimization

【Abstract】传统的智能优化算法,例如进化算法,群体智能优化算法等,都基于随机初始化,并针对某一个给定优化问题进行独立求解。由于该类算法基于种群迭代搜索,其优化效率较低下。在国内外研究中,出现了很多优秀的研究来提升传统智能优化算法的效率,例如利用surrogate model的研究、自适应智能优化算法的研究、多种群智能优化算法的研究等。由于优化任务往往不是独立存在,一个优化问题的求解通过适当的处理,通过能提升其在相关的问题求解的过程。目前的文献中,已经出现不少学者利用历史问题的信息来帮助新问题的求解。利用迁移学习来挖掘历史或者相关任务的有用信息,以帮助在相关问题上的智能优化算法求解是一个较新的课题。本报告将主要从两个方面来介绍报告人在基于迁移学习的智能优化算法方面的研究。一是从历史优化问题中挖掘可利用的具有通用属性的知识,帮助智能优化算法在新问题上的求解。二是在线的多任务智能优化模型介绍,通过在线的任务间的有用信息共享,实现多任务智能优化求解,提升智能优化的效率。

 

茹弘(新加坡南洋理工大学)

8月15日13:30-14:15

What Do A Billion Observations Say About Distance and Relationship Lending?

【Abstract】Using one billion observations on the locations of bank branches and firms in China, we find strong evidence of a novel U-shaped relationship between lender-borrower distance and soft information. Lending intensities decrease with distance within a short range but increase with distance beyond that. Distant borrowers have fewer explicit third-party loan guarantees but provide more implicit guarantees from their connected firms that borrow from the same bank. This firm network facilitates lenders to obtain soft information and manage risks. The default ratios are significantly lower for firms that are either nearby or distant from the lending bank branches. Moreover, we observe directly the lenders’ soft information by tracing out whether banks downgrade internal loan ratings before the delinquency. Banks can predict the delinquency more accurately for the firms in either short or long distance.

 

汤铃(北京航空航天大学)

8月15日14:15-15:00

中国碳交易市场建模与机制设计研究

【Abstract】为碳减排目标的有效实现,我国于2011年10月正式确定北京、天津、重庆、上海、湖北、广东和深圳七省高为全国首批实施碳排放交易试点,并于2017年底建成全国性的电力行业碳交易市场。在对现有碳市场机制与政策的充分分析基础上,采用多种实验仿真技术,如可计算一般均衡(CGE)模型、多主体(Multi-agent)模型、投入产出模型等,构建了我国碳交易机制仿真模型,以定量测算碳交易机制对我国经济与环境的影响,试图寻求促进我国经济与环境协调发展的碳交易机制设计,具体涉及:碳权总额、碳权拍卖总额、碳权分配方法、碳权拍卖方法、市场监管政策、惩罚政策、技术补贴政策等。

 

田静(澳大利亚塔斯马尼亚大学)

8月15日14:15-15:00

An Unobserved Component Modeling Approach to Evaluating Multi-horizon Forecasts

【Abstract】We propose a state space modeling framework to evaluate a set of forecasts that target the same variable but are updated along the forecast horizon. The approach decomposes forecast errors into three distinct horizon-specific processes, namely, bias, rational error and implicit error, and attributes forecast revisions to corrections for these forecast errors. We conduct Monte Carlo simulations to show the ability of the modeling approach to identify the correct error compositions across horizons. By evaluating multi-horizon daily maximum temperature forecasts for Melbourne, Australia, we demonstrate how this modeling framework analyzes the dynamics of the forecast revision structure across horizons. Understanding forecast revisions is critical for weather forecast users to determine the optimal timing for their planning decision.

 

王玉东(南京理工大学)

8月15日14:15-15:00

Forecasting Stock Returns: Some New Evidence

【Abstract】We develop a novel method to impose constraints on univariate predictive regressions of stock returns. Unlike the previous approaches in the literature, we implement our constraints directly on the predictor, setting it to zero whenever its value falls within the variable’s past 24-month high and low. Empirically, we find that relative to standard unconstrained predictive regressions, our approach leads to significantly larger forecasting gains. We also show how a simple equal-weighted combination of our constrained forecasts leads to further improvements in forecast accuracy, generating forecasts that are more accurate than those obtained using existing constrained methods. Further analysis confirms that these findings are robust to the presence of model instabilities and structural breaks.

 
 

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