Research on Portfolio Selection Model Based on Fuzzy High Order Moment

2020 ◽  
Author(s):  
Xiaolian Meng ◽  
Jiajun Huang
Keyword(s):  
Portfolio Selection ◽  
High Order ◽  
Selection Model ◽  
Order Moment ◽  
High Order Moment ◽  
Model Based ◽  
Portfolio Selection Model

Dynamic financial economic fluctuation model based on non-normal distribution

2020 ◽  
pp. 1-10
Author(s):  
Li Wang
Keyword(s):  
Normal Distribution ◽  
High Frequency ◽  
Extreme Value ◽  
High Order ◽  
High Frequency Data ◽  
Order Moment ◽  
Frequency Data ◽  
High Order Moment ◽  
Model Based ◽  
Skewness And Kurtosis

This paper discusses the modeling of financial volatility under the condition of non-normal distribution. In order to solve the problem that the traditional central moment cannot estimate the thick-tailed distribution, the L-moment which is widely used in the hydrological field is introduced, and the autoregressive conditional moment model is used for static and dynamic fitting based on the generalized Pareto distribution. In order to solve the dimension disaster of multidimensional conditional skewness and kurtosis modeling, the multidimensional skewness and kurtosis model based on distribution is established, and the high-order moment model is deduced. Finally, the problems existing in the traditional investment portfolio are discussed, and on this basis, the high-order moment portfolio is further studied. The results show that the key lies in the selection of the model and the assumption of asset probability distribution. Financial risk analysis can be effective only with a large sample. High-frequency data contain more information and can provide rich data resources. The conditional generalized extreme value distribution can well describe the time-varying characteristics of scale parameters and shape parameters and capture the conditional heteroscedasticity in the high-frequency extreme value time series. Better describe the persistence and aggregation of the extreme value of high frequency data as well as the peak and thick tail characteristics of its distribution.

scholarly journals A portfolio selection model based on the knapsack problem under uncertainty

PLoS ONE ◽  
2019 ◽  
Vol 14 (5) ◽  
pp. e0213652 ◽  
Author(s):  
Fereshteh Vaezi ◽  
Seyed Jafar Sadjadi ◽  
Ahmad Makui
Keyword(s):  
Portfolio Selection ◽  
Knapsack Problem ◽  
Selection Model ◽  
Model Based ◽  
Portfolio Selection Model

scholarly journals A Portfolio Selection Model Based on the Interval Number

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jiangshan Hu ◽  
Yunyun Sui ◽  
Fang Ma
Keyword(s):  
Portfolio Selection ◽  
Financial Market ◽  
Risk Function ◽  
Order Relation ◽  
Random Variable ◽  
Interval Number ◽  
Selection Model ◽  
Asset Return ◽  
Model Based ◽  
Portfolio Selection Model

Traditional portfolio theory uses probability theory to analyze the uncertainty of financial market. The assets’ return in a portfolio is regarded as a random variable which follows a certain probability distribution. However, it is difficult to estimate the assets return in the real financial market, so the interval distribution of asset return can be estimated according to the relevant suggestions of experts and decision makers, that is, the interval number is used to describe the distribution of asset return. Therefore, this paper establishes a portfolio selection model based on the interval number. In this model, the semiabsolute deviation risk function is used to measure the portfolio’s risk, and the solution of the model is obtained by using the order relation of the interval number. At the same time, a satisfactory solution of the model is obtained by using the concept of acceptability of the interval number. Finally, an example is given to illustrate the practicability of the model.

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