Nonparametric Kernel Method to Hedge Downside Risk

2019 ◽  
Vol 19 (4) ◽  
pp. 929-944
Author(s):  
Jinbo Huang ◽  
Ashley Ding ◽  
Yong Li
Keyword(s):  
Kernel Method ◽  
Downside Risk ◽  
Nonparametric Kernel ◽  
Nonparametric Kernel Method

scholarly journals Credibility Weighted Hazard Estimation

2000 ◽  
Vol 30 (2) ◽  
pp. 405-417 ◽  
Author(s):  
Jens Perch Nielsen ◽  
Bjørn Lunding Sandqvist
Keyword(s):  
Hilbert Space ◽  
Kernel Method ◽  
Sparse Data ◽  
Hazard Curve ◽  
Credibility Estimator ◽  
Space Projection ◽  
Hazard Estimation ◽  
Available Information ◽  
Nonparametric Kernel ◽  
Nonparametric Kernel Method

AbstractCredibility weighting is helpful in many insurance applications where sparse data crave information from other sources of data. In this paper we aim at estimating a hazard curve using the nonparametric kernel method, where a credibility weighting principle is used locally, so that areas of sparse data for one subgroup can be alleviated by available information from other subgroups. The credibility estimator is found through a Hilbert space projection formulation of Buhlmann-Straub's credibility approach.

scholarly journals Asymptotic Properties for Weighted Averages of Longitudinal Dependent Data

2018 ◽  
Vol 7 (6) ◽  
pp. 100
Author(s):  
Brahima Soro ◽  
Ouagnina Hili ◽  
Youssouf Diagana
Keyword(s):  
Longitudinal Data ◽  
Covariance Function ◽  
Kernel Method ◽  
Asymptotic Properties ◽  
Dependent Data ◽  
Independent Data ◽  
Function Estimation ◽  
Mixing Conditions ◽  
Weighted Averages ◽  
Nonparametric Kernel

This paper presents a set of normality general results for kernel weighted averages. We extend existing literature for independent data (Yao, 2007) to stationary dependent longitudinal data. The asymptotic properties of proposed weighted averages are investigate under α-mixing conditions. These results are useful for covariance function estimation based on nonparametric kernel method.

scholarly journals Nonparametric instrumental variables for identification of block-oriented systems

2013 ◽  
Vol 23 (3) ◽  
pp. 521-537 ◽  
Author(s):  
Grzegorz Mzyk
Keyword(s):  
Rate Of Convergence ◽  
Instrumental Variables ◽  
Least Squares Method ◽  
Kernel Method ◽  
Estimation Error ◽  
Identification Algorithm ◽  
Correlated Noise ◽  
The Matrix ◽  
Nonparametric Kernel ◽  
Special Case

Abstract A combined, parametric-nonparametric identification algorithm for a special case of NARMAX systems is proposed. The parameters of individual blocks are aggregated in one matrix (including mixed products of parameters). The matrix is estimated by an instrumental variables technique with the instruments generated by a nonparametric kernel method. Finally, the result is decomposed to obtain parameters of the system elements. The consistency of the proposed estimate is proved and the rate of convergence is analyzed. Also, the form of optimal instrumental variables is established and the method of their approximate generation is proposed. The idea of nonparametric generation of instrumental variables guarantees that the I.V. estimate is well defined, improves the behaviour of the least-squares method and allows reducing the estimation error. The method is simple in implementation and robust to the correlated noise.

Online Prediction Model Based on New Kernel Method

2014 ◽  
Vol 7 (1) ◽  
pp. 107
Author(s):  
Ilyes Elaissi ◽  
Okba Taouali ◽  
Messaoud Hassani
Keyword(s):  
Prediction Model ◽  
Kernel Method ◽  
Model Based ◽  
Online Prediction

An Extended Method of SIRMs Connected Fuzzy Inference Method Using Kernel Method

2009 ◽  
Vol E92-A (10) ◽  
pp. 2514-2521
Author(s):  
Hirosato SEKI ◽  
Fuhito MIZUGUCHI ◽  
Satoshi WATANABE ◽  
Hiroaki ISHII ◽  
Masaharu MIZUMOTO
Keyword(s):  
Fuzzy Inference ◽  
Kernel Method ◽  
Inference Method

Utilizing Downside Risk Measures

2014 ◽  
Vol 31 (3) ◽  
pp. 42-50 ◽  
Author(s):  
Michelle McCarthy
Keyword(s):  
Risk Measures ◽  
Downside Risk
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