scholarly journals Optimal futures hedging strategies based on an improved kernel density estimation method

2021 ◽  
Author(s):  
Xing Yu ◽  
Xinxin Wang ◽  
Weiguo Zhang ◽  
Zijin Li
Keyword(s):  
Crude Oil ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Estimation Method ◽  
Kernel Density ◽  
Independent Random Variables ◽  
Time Varying ◽  
Hedging Strategy ◽  
Hedge Ratio ◽  
Distribution Assumption

Abstract In this paper, we study the hedging effectiveness of crude oil futures on the basis of the lower partial moments (LPMs). An improved kernel density estimation method is proposed to estimate the optimal hedge ratio. We investigate crude oil price hedging by contributing to the literature in the following two-fold: first, unlike the existing studies which focus on univariate kernel density method, we use bivariate kernel density to calculate the estimated LPMs, wherein the two bandwidths of the bivariate kernel density are not limited to the same, which is our main innovation point. According to the criterion of minimizing the mean integrated square error, we derive the conditions that the optimal bandwidths satisfy. In the process of derivation, we make a distribution assumption “locally” in order to simplify calculation, but this type of “local” distribution assumption is far better than “global” distribution assumption used in parameter method theoretically and empirically. Second, in order to meet the requirement of bivariate kernel density for independent random variables, we adopt ARCH models to obtain the independent noises with related to the returns of crude oil spot and futures. Genetic algorithm is used to tune the parameters that maximize Quasi-likelihood. Empirical results reveal that, at first, the hedging strategy based on the improved kernel density estimation method is of highly efficiency, then it achieves better performance than the hedging strategy based on the traditional parametric method. We also compare the risk control effectiveness of static hedge ratio vs. time-varying hedge ratio, and find that static hedging has a better performance than time-varying hedging.

scholarly journals Forecasting Hourly Power Load Considering Time Division: A Hybrid Model Based on K-means Clustering and Probability Density Forecasting Techniques

2019 ◽  
Vol 11 (24) ◽  
pp. 6954
Author(s):  
Fuqiang Li ◽  
Shiying Zhang ◽  
Wenxuan Li ◽  
Wei Zhao ◽  
Bingkang Li ◽  
...  
Keyword(s):  
Probability Density ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Clustering Algorithm ◽  
Estimation Method ◽  
Kernel Density ◽  
Support Vector ◽  
Average Width ◽  
Power Load ◽  
Density Forecasting

In comparison with traditional point forecasting method, probability density forecasting can reflect the load fluctuation more effectively and provides more information. This paper proposes a hybrid hourly power load forecasting model, which integrates K-means clustering algorithm, Salp Swarm Algorithm (SSA), Least Square Support Vector Machine (LSSVM), and kernel density estimation (KDE) method. Firstly, the loads at 24 times a day are grouped into three categories according to the K-means clustering algorithm, which correspond to the valley period, flat period, and peak period of the load, respectively. Secondly, the load point forecasting value is obtained by LSSVM method optimized by SSA algorithm. Furthermore, the kernel density estimation method is employed to fit the forecasting error of SSA-LSSVM in different time periods, and the probability density function of the error distribution is obtained. The final load probability density forecasting result is obtained by combining the point forecasting value and the error fitting result, and then the upper and lower limits of the confidence interval under the given confidence level are solved. In this paper, the performance of the model is evaluated by two indicators named interval coverage and interval average width. Meanwhile, in comparison with several other models, it can be concluded that the proposed model can effectively improve the forecasting effect.

Geospatial Crime Analysis to Determine Crime Density Using Kernel Density Estimation for the Indian Context

2020 ◽  
Vol 17 (1) ◽  
pp. 74-86
Author(s):  
Boppuru Rudra Prathap ◽  
K. Ramesha
Keyword(s):  
Law Enforcement ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Financial Burden ◽  
Estimation Method ◽  
Kernel Density ◽  
Law Enforcement Agencies ◽  
The Government ◽  
Geographical Locations

Crime is the most common social problem faced in a developing country. Crime affects the reputation of a nation and the quality of life of its citizens. Crime also affects the economy of the country, increasing the financial burden of the government due to the need for expenditure in the police force and judicial system. Various initiatives are taken by law enforcement to reduce the crime rate. One such initiative, real-time accurate crime predictions can help reduce the occurrence of crime. In this paper, a crime analytics platform is developed, which processes newsfeed data analysis for different types of crimes and identify crime hotspots using Kernel Density Estimation method. This system enables criminologists to understand the hidden relationships between crime and geographical locations. Interactive visualization features are available that enable law enforcement agencies to predict crime.

scholarly journals Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1551 ◽  
Author(s):  
Lixing Chen ◽  
Xueliang Huang ◽  
Hong Zhang
Keyword(s):  
Density Estimation ◽  
Electric Vehicles ◽  
Kernel Density Estimation ◽  
Estimation Method ◽  
Kernel Density ◽  
Data Transformation ◽  
Joint Estimation ◽  
Transformation Model ◽  
Inverse Transformation ◽  
Symmetric Kernel

The accurate modeling of the charging behaviors for electric vehicles (EVs) is the basis for the charging load modeling, the charging impact on the power grid, orderly charging strategy, and planning of charging facilities. Therefore, an accurate joint modeling approach of the arrival time, the staying time, and the charging capacity for the EVs charging behaviors in the work area based on ternary symmetric kernel density estimation (KDE) is proposed in accordance with the actual data. First and foremost, a data transformation model is established by considering the boundary bias of the symmetric KDE in order to carry out normal transformation on distribution to be estimated from all kinds of dimensions to the utmost extent. Then, a ternary symmetric KDE model and an optimum bandwidth model are established to estimate the transformed data. Moreover, an estimation evaluation model is also built to transform simulated data that are generated on a certain scale with the Monte Carlo method by means of inverse transformation, so that the fitting level of the ternary symmetric KDE model can be estimated. According to simulation results, a higher fitting level can be achieved by the ternary symmetric KDE method proposed in this paper, in comparison to the joint estimation method based on the edge KDE and the ternary t-Copula function. Moreover, data transformation can effectively eliminate the boundary effect of symmetric KDE.

scholarly journals A fast and objective multidimensional kernel density estimation method: fastKDE

2016 ◽  
Vol 101 ◽  
pp. 148-160 ◽  
Author(s):  
Travis A. O’Brien ◽  
Karthik Kashinath ◽  
Nicholas R. Cavanaugh ◽  
William D. Collins ◽  
John P. O’Brien
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Estimation Method ◽  
Kernel Density

Kernel density estimation in regional convergence studies

2016 ◽  
Vol 61 (10) ◽  
pp. 7-21
Author(s):  
Piotr Wójcik
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Estimation Method ◽  
Kernel Density ◽  
Regional Convergence ◽  
Lower Secondary School ◽  
Convergence Hypothesis ◽  
Educational Achievements ◽  
Science Part ◽  
Non Parametric

The aim of the article is to present a non-parametric kernel density estimation method as a tool used for empirical verification of the regional convergence hypothesis, including convergence of clubs. It is explained how kernel density estimation complements other methods applied to verify the phenomenon of convergence. The empirical part shows an application of the non-parametric density estimation to the analysis of regional convergence of educational achievements of Polish pupils, measured by the average results of the mathematical and natural science part of the lower-secondary school leaving exams on the level of municipalities in years 2002—2013. The results of the analysis indicate the existence of regional convergence of exam results for Polish municipalities. In case of the analysis for three-yearly periods convergence of clubs was observed — the municipalities with lowest exam results constitute a separate club of convergence.

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