Comparison of Some Semi-parametric Methods in Partial Linear Single-Index Model

The research dealt with a comparative study between some semi-parametric estimation methods to the Partial linear Single Index Model using simulation. There are two approaches to model estimation two-stage procedure and MADE to estimate this model. Simulations were used to study the finite sample performance of estimating methods based on different Single Index models, error variances, and different sample sizes , and the mean average squared errors were used as a comparison criterion between the methods were used. The results showed a preference for the two-stage procedure depending on all the cases that were used

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A comparison of minimum variance and maximum Sharpe ratio portfolios for mainstream investors

The Journal of Risk Finance ◽

10.1108/jrf-02-2021-0021 ◽

2022 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Anja Vinzelberg ◽

Benjamin Rainer Auer

Keyword(s):

Minimum Variance ◽

Sharpe Ratio ◽

Optimization Techniques ◽

Estimation Methods ◽

Content Type ◽

Single Index ◽

Index Model ◽

Single Index Model ◽

Out Of Sample ◽

Asset Classes

PurposeMotivated by the recent theoretical rehabilitation of mean-variance analysis, the authors revisit the question of whether minimum variance (MinVar) or maximum Sharpe ratio (MaxSR) investment weights are preferable in practical portfolio formation.Design/methodology/approachThe authors answer this question with a focus on mainstream investors which can be modeled by a preference for simple portfolio optimization techniques, a tendency to cling to past asset characteristics and a strong interest in index products. Specifically, in a rolling-window approach, the study compares the out-of-sample performance of MinVar and MaxSR portfolios in two asset universes covering multiple asset classes (via investable indices and their subindices) and for two popular input estimation methods (full covariance and single-index model).FindingsThe authors find that, regardless of the setting, there is no statistically significant difference between MinVar and MaxSR portfolio performance. Thus, the choice of approach does not matter for mainstream investors. In addition, the analysis reveals that, contrary to previous research, using a single-index model does not necessarily improve out-of-sample Sharpe ratios.Originality/valueThe study is the first to provide an in-depth comparison of MinVar and MaxSR returns which considers (1) multiple asset classes, (2) a single-index model and (3) state-of-the-art bootstrap performance tests.

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Estimation for a partial-linear single-index model

The Annals of Statistics ◽

10.1214/09-aos712 ◽

2010 ◽

Vol 38 (1) ◽

pp. 246-274 ◽

Cited By ~ 92

Author(s):

Jane-Ling Wang ◽

Liugen Xue ◽

Lixing Zhu ◽

Yun Sam Chong

Keyword(s):

Single Index ◽

Index Model ◽

Single Index Model ◽

Partial Linear

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A novel partial-linear single-index model for time series data

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2018.12.012 ◽

2019 ◽

Vol 134 ◽

pp. 110-122 ◽

Cited By ~ 2

Author(s):

Lei Huang ◽

Hui Jiang ◽

Huixia Wang

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Single Index ◽

Index Model ◽

Single Index Model ◽

Partial Linear

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Quantile regression and variable selection of partial linear single-index model

Annals of the Institute of Statistical Mathematics ◽

10.1007/s10463-014-0457-x ◽

2014 ◽

Vol 67 (2) ◽

pp. 375-409 ◽

Cited By ~ 14

Author(s):

Yazhao Lv ◽

Riquan Zhang ◽

Weihua Zhao ◽

Jicai Liu

Keyword(s):

Variable Selection ◽

Quantile Regression ◽

Single Index ◽

Index Model ◽

Single Index Model ◽

Partial Linear ◽

Selection Of

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Variable selection for the partial linear single-index model

Acta Mathematicae Applicatae Sinica English Series ◽

10.1007/s10255-017-0666-1 ◽

2017 ◽

Vol 33 (2) ◽

pp. 373-388 ◽

Cited By ~ 7

Author(s):

Wu Wang ◽

Zhong-yi Zhu

Keyword(s):

Variable Selection ◽

Single Index ◽

Index Model ◽

Single Index Model ◽

Selection For ◽

Partial Linear

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Functional Partial Linear Single-index Model

Scandinavian Journal of Statistics ◽

10.1111/sjos.12178 ◽

2015 ◽

Vol 43 (1) ◽

pp. 261-274 ◽

Cited By ~ 16

Author(s):

Guochang Wang ◽

Xiang-Nan Feng ◽

Min Chen

Keyword(s):

Single Index ◽

Index Model ◽

Single Index Model ◽

Partial Linear

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Dynamic Influence Prediction of Social Network Based on Partial Autoregression Single Index Model

Discrete Dynamics in Nature and Society ◽

10.1155/2019/6237406 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Ya-hui Jia ◽

Taotao Song ◽

Shun-yao Wu ◽

Qi Zhang ◽

Yu-xia Su

Keyword(s):

Social Network ◽

Finite Sample ◽

Absolute Deviation ◽

Single Index ◽

Index Model ◽

Single Index Model ◽

Variables Selection ◽

Combine Network ◽

Profile Least Squares ◽

Smoothly Clipped Absolute Deviation

Everything is connected in the world. From small groups to global societies, the interactions among people, technology, and policies need sophisticated techniques to be perceived and forecasted. In social network, it has been concluded that the microblog users influence and microblog grade are nonlinearly dependent. However, to the best of our knowledge, the nonlinear influence predication of social network has not been explored in the existing literature. This article proposes a partial autoregression single index model to combine network structure (linear) and static covariates (nonparametric) flexibly. Compared with previous work, our model has fewer limits and more applications. The profile least squares estimation is employed to infer this semiparametric model, and variables selection is performed via the smoothly clipped absolute deviation penalty (SCAD). Simulations are conducted to demonstrate finite sample behaviors.

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