检索结果-内蒙古大学图书馆

A nonparametric kernel regression approach for pricing options on stock market index

APPLIED ECONOMICS 2016年第10期48卷 902-913页

作者： Kung, James J. Ming Chuan Univ Dept Int Business Taipei Taiwan

Previous options studies typically assume that the dynamics of the underlying asset price follow a geometric Brownian motion (GBM) when pricing options on stocks, stock indices, currencies or futures. However, there is mounting empirical evidence that the volatility of asset price or return is far from constant. This article, in contrast to studies that use parametric approach for option pricing, employs nonparametric kernel regression to deal with changing volatility and, accordingly, prices options on stock index. Specifically, we first estimate nonparametrically the volatility of asset return in the GBM based on the Nadaraya-Watson (N-W) kernel estimator. Then, based on the N-W estimates for the volatility, we use Monte Carlo simulation to compute option prices under different settings. Finally, we compare the index option prices under our nonparametric model with those under the Black-Scholes model and the Stein-Stein model.

关键词： Geometric Brownian motion stock index option nonparametric kernel regression Nadaraya-Watson kernel estimator Black-Scholes model Stein-Stein model C14 C15 G13

来源：评论

学校读者我要写书评

暂无评论

Pattern Recognition Based on the nonparametric kernel regression Method in A-share Market

Pattern Recognition Based on the Nonparametric Kernel Regres...

引用

International Conference on Soft Computing Techniques and Engineering Application (ICSCTEA)

作者： Sun, Huaiyu Zhu, Mi He, Feng Wuhan Univ Econ & Management Sch Wuhan 430072 Peoples R China China Investment Secur Co Ltd Res Dept Shenzhen Peoples R China UBS SDIC Fund Management Co Ltd Prod Design Dept Shenzhen Peoples R China

ISBN: (纸本)9788132216957;9788132216940

Fourteen kinds of technical pattern in A-share market have been automatically identified in this paper, using nonparametric kernel regression method and quantitative recognition rules that are constrained and improved to make the model more feasible and effective for investment practice. K-S test shows that the distribution of the stock yield following the recognized patterns statistically differs from that of random sampling. Clustering analysis also suggests that most of the identified technical patterns have distinguishable effects on the subsequent stock price movement. These findings provide useful guidance for the development of innovative investment models in high-frequency automatic stock trading.

关键词： Technical pattern recognition nonparametric kernel regression

来源：评论

学校读者我要写书评

暂无评论

An econometric identification of ineffective common-cold advertising

引用

INTERNATIONAL JOURNAL OF PHARMACEUTICAL AND HEALTHCARE MARKETING 2023年第4期17卷 476-494页

作者： Vinod, Hrishikesh Jetta, Kurt Rengifo, Minaya Eric Fordham Univ Dept Econ New York NY 10458 USA TABS Grp Shelton CT USA

PurposeThis study aims to highlight potential savings in advertising budgets. Design/methodology/approachThis study uses modern computer-based tools including stochastic dominance to check if advertising expenses are increasing sales by using modern causality assessment tools which allow for nonlinearities and use sophisticated assessment of causal impact of ads on sales. FindingsThis study identifies specific media spots where ad budget savings are possible. The marketing managers can take the next step to make small-scale local experiments to reassess this study's findings. Research limitations/implicationsThis study is a statistical observational assessment not based on controlled experiments. Practical implicationsThe authors have tools to identify ineffective advertising which can produce huge savings for the organization. The over-the-counter cold remedies have become important due to the pandemic. The tools have wider applicability in marketing research. Social implicationsLess wasteful expenses always benefit the society. Originality/valueTo the best of the authors' knowledge, this may be the first such attempt to use sophisticated causal identification tools. Remedies for the common cold sold by seven major US retailers help identify specific retailers and specific media with negative returns on investment.

关键词： nonparametric kernel regression Stochastic dominance Marketing Media outlets Common cold remedy nonparametric methods

来源：评论

学校读者我要写书评

暂无评论

Sufficient dimension reduction and instrument search for data with nonignorable nonresponse

引用

BERNOULLI 2021年第2期27卷 930-945页

作者： Zhao, Puying Wang, Lei Shaw, Jun Yunnan Univ Dept Stat Kunming Yunnan Peoples R China Nankai Univ Sch Stat & Data Sci Tianjin Peoples R China Univ Wisconsin Dept Stat Madison WI 53706 USA

Consider a response variable subject to nonignorable nonresponse and a fully observed covariate vector. The purpose of our study is threefold. First, we study how to extend nonparametric sufficient dimension reduction to data with nonignorable nonresponse. Second, we utilize sufficient dimension reduction to search an instrument, a linear function of covariates that is related to the response variable but can be excluded from the propensity of nonignorable nonresponse, for the purpose of identifying unknown parameters in a semiparametric propensity and a nonparametric distribution of response variable and covariates. Third, we establish asymptotic results for parameter estimators based on sufficient dimension reduction and instrument search, and investigate the effect on the limiting distribution of parameter estimators due to instrument search. We evaluate the performance of proposed estimators in a Monte Carlo study and illustrate our method in an application to AIDS Clinical Trials Group Protocol 175 data.

关键词： Covariate dimension reduction estimation identifiability instrument nonparametric kernel regression semiparametric propensity

来源：评论

学校读者我要写书评

暂无评论

How public and government matter in industrial pollution mitigation performance: Evidence from China

引用

JOURNAL OF CLEANER PRODUCTION 2021年 306卷 127099-127099页

作者： Dong, Feng Pan, Yuling Li, Yangfan Zhang, Shuaiqing China Univ Min & Technol Sch Econ & Management Xuzhou 221116 Jiangsu Peoples R China

Every country must face the problem of controlling industrial pollution during industrialization. Although low-carbon development has become important to global economic development, industrial pollution remains a problem in rapidly industrializing developing countries. With China as a case study, this paper utilizes the Malmquist index based on game cross-efficiency to measure the industrial pollution mitigation performance (IPMP) in each province of China. Labor and money variables related to industrial pollution mitigation are taken as the input indexes, and the reduction rate of various industrial pollution emissions are taken as the output indexes. From the perspectives of the public and the gov-ernment, this paper further explores the influencing factors on the IPMP. Considering that there may be a nonlinear relationship between the influencing factors and the IPMP, nonparametric kernel regression is employed to explore the effect by the influencing factors, and the nonparametric bivariate density estimation is used to verify the regression results. The main conclusions of this study are as follows: (1) Due to the concentration of heavy industry, the IPMP is low in northern China. In contrast, the IPMP in central and western China is relatively high. (2) Public supervision has a positive impact on the IPMP. However, the results of heterogeneity analysis show that this effect is different in eastern, central and western China. As a result, under a high degree of public supervision, the regional IPMP will be signif-icantly improved. (3) Public concentration has a positive "U"-shaped impact on the IPMP. The hetero-geneity analysis shows that eastern China also has this "U"-shaped characteristic, while the public concentration of central and western China has a positive impact on the IPMP;that is, similar to the public supervision, under a high degree of public concentration, the regional IPMP will be significantly improved. (4) In areas where the enforcement of e

关键词： Industrial pollution mitigation performance Malmquist index nonparametric kernel regression China

来源：评论

学校读者我要写书评

暂无评论

Semiparamertic Dimension Reduction Model and Applications

Semiparamertic Dimension Reduction Model and Applications

引用

作者： Zhao, Ge The Pennsylvania State University

学位级别：Ph.D., Doctor of Philosophy

In the robust nonparametric kernel regression context, we prescribe a data driven method to select the trimming parameter and the bandwidth robustly. The estimator is obtained through solving estimating equations, and it controls the effect from outlying observations through a combination of weighting and trimming. We show asymptotic consistency, establish the estimation bias, variance properties and derive the asymptotic distribution of the resulting estimator. The finite sample performance of the estimator is illustrated through both simulation studies and analysis on a problem related to wind power generation, which motivated this study at the first place. We propose a general index model for survival data, which generalizes many commonly used semiparametric survival models and belongs to the framework of dimension reduction. Using a combination of geometric approach in semiparametrics and martingale treatment in survival data analysis, we devise estimation procedures that are feasible and do not require covariate-independent censoring as assumed in many dimension reduction methods for censored survival data. We establish the root-n consistency and asymptotic normality of the proposed estimators and derive the most efficient estimator in this class for the general index model. Numerical experiments are carried out to demonstrate the empirical performance of the proposed estimators and an application to an AIDS data further illustrates the usefulness of the work. Kidney transplantation is the most effective renal replacement therapy for renal failure patients. With the severe shortage of kidney supplies and for the clinical effectiveness of transplantation, it would be crucial to design objective measures, such as the Estimated Post-Transplant Survival (EPTS) score, to quantify the benefit that a renal failure patient would gain from a potential transplantation by comparing the expected residual lives of the same patient with and without transplant. However, in th

关键词： nonparametric kernel regression Trimming parameter Bandwidth selection Residual life increment potential Mean residual life model Estimated Post-Transplant Survival Estimator

来源：评论

学校读者我要写书评

暂无评论

A fast and manufacture-friendly optical proximity correction based on machine learning

引用

MICROELECTRONIC ENGINEERING 2017年 168卷 15-26页

作者： Ma, Xu Jiang, Shangliang Wang, Jie Wu, Bingliang Song, Zhiyang Li, Yanqiu Beijing Inst Technol Key Lab Photoelect Imaging Technol & Syst Minist Educ China Beijing 100081 Peoples R China Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94706 USA

Pixel-based optical proximity correction (PBOPC) is currently a key resolution enhancement technique to push the resolution limit of optical lithography. However, the increasing scale, density and complexity of modern integrated circuits pose new challenges to both of the OPC computational intensity and mask manufacturability. This paper aims at developing a practical OPC algorithm based on a machine learning technique to effectively reduce the PBOPC runtime and mask complexity. We first divide the target layout into small regions around corners and edge fragments. Using a nonparametric kernel regression technique, these small regions are then filled in by the weighted linear combination of a subset of training OPC examples selected from the pre-calculated libraries. To keep balance between the image fidelity and mask complexity, we use an edge-based OPC (EBOPC) library to synthesize the OPC patterns in non-critical areas, while use another PBOPC library for hotspots. In addition, a post-processing method is developed to refine the regressed OPC pattern so as to guarantee the final image fidelity and mask manufacturability. Experimental results show that, compared to a currently professional PBOPC software, the proposed algorithm can achieve approximately two-fold speedup and more manufacture-friendly OPC patterns. (C) 2016 Published by Elsevier B.V.

关键词： Optical lithography Optical proximity correction Machine learning nonparametric kernel regression Manufacturability

来源：评论

学校读者我要写书评

暂无评论

nonparametric causal inference from observational time series through marginal integration

引用

ECONOMETRICS AND STATISTICS 2017年 2卷 81-105页

作者： Li, Shu Ernest, Jan Buehlmann, Peter Swiss Fed Inst Technol Seminar Stat Ramistr 101 CH-8092 Zurich Switzerland

Causal inference from observational data is an ambitious but highly relevant task, with diverse applications ranging from natural to social sciences. Within the scope of non-parametric time series, causal inference defined through interventions is largely unexplored, although time order simplifies the problem substantially. A marginal integration scheme is considered for inferring causal effects from observational time series data, MINT-T (marginal integration in time series), which is an adaptation for time series of a previously proposed method for the case of independent data. This approach for stationary stochastic processes is fully nonparametric and, assuming no instantaneous effects consistently recovers the total causal effect of a single intervention with optimal one-dimensional nonparametric convergence rate n(-2/5) assuming regularity conditions and twice differentiability of a certain corresponding regression function. Therefore, MINT-T remains largely unaffected by the curse of dimensionality as long as smoothness conditions hold in higher dimensions and it is feasible for a large class of stationary time series, including nonlinear and multivariate processes. For the case with instantaneous effects, we provide a procedure which guards against false positive causal statements. (c) 2016 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.

关键词： Application to economics Backdoor adjustment nonparametric kernel regression Structural equation models Total causal effects Instantaneous effects

来源：评论

学校读者我要写书评

暂无评论

Real time air quality forecasting using integrated parametric and non-parametric regression techniques

引用

ATMOSPHERIC ENVIRONMENT 2015年 103卷 53-65页

作者： Donnelly, Aoife Misstear, Bruce Broderick, Brian Univ Dublin Trinity Coll Coll Green Dept Civil Struct & Environm Engn Dublin 2 Ireland

This paper presents a model for producing real time air quality forecasts with both high accuracy and high computational efficiency. Temporal variations in nitrogen dioxide (NO2) levels and historical correlations between meteorology and NO2 levels are used to estimate air quality 48 h in advance. nonparametric kernel regression is used to produce linearized factors describing variations in concentrations with wind speed and direction and, furthermore, to produce seasonal and diurnal factors. The basis for the model is a multiple linear regression which uses these factors together with meteorological parameters and persistence as predictors. The model was calibrated at three urban sites and one rural site and the final fitted model achieved R values of between 0.62 and 0.79 for hourly forecasts and between 0.67 and 0.84 for daily maximum forecasts. Model validation using four model evaluation parameters, an index of agreement (IA), the correlation coefficient (R), the fraction of values within a factor of 2 (FAC2) and the fractional bias (FB), yielded good results. The IA for 24 hr forecasts of hourly NO2 was between 0.77 and 0.90 at urban sites and 0.74 at the rural site, while for daily maximum forecasts it was between 0.89 and 0.94 for urban sites and 0.78 for the rural site. R values of up to 0.79 and 0.81 and FAC2 values of 0.84 and 0.96 were observed for hourly and daily maximum predictions, respectively. The model requires only simple input data and very low computational resources. It found to be an accurate and efficient means of producing real time air quality forecasts. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Nitrogen dioxide nonparametric kernel regression Air quality forecasting Statistical modelling

来源：评论

学校读者我要写书评

暂无评论

Probability Density Estimation Based on nonparametric Local kernel regression

Probability Density Estimation Based on Nonparametric Local ...

引用

7th International Symposium on Neural Networks

作者： Han, Min Liang, Zhi-ping Dalian Univ Technol Sch Elect & Informat Engn Dalian 116023 Liaoning Peoples R China

ISBN: (纸本)9783642132773

In this research, a local kernel regression method was proposed to improve the computational efficiency after analyzing the kernel weights oh the non-parametric kernel regression. Based on the correlation between the distribution function and the probability density function, together with the nonparametric local kernel regression we developed a new probability density estimation method. With the proper setting of the sparse factor, the number of the kernels involved in the kernel smooth was controlled, and the density was estimated with highly fitness and smoothness. According to the simulations, we can see that the proposed method shows a very well performance both in the accuracy and the efficiency.

关键词： nonparametric kernel regression probability density estimation cumulative distribution functions

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：