Optimal futures hedging strategies based on an improved kernel density estimation method-Reference-Cited by-同舟云学术

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

Published:2021-09-01 Issue:23 Volume:25 Page:14769-14783
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Yu Xing^ORCID,Wang Xinxin,Zhang Weiguo,Li Zijin

Funder

Funds for International Cooperation and Exchange of the National Natural Science Foundation of China

National Natural Science Foundation of China

Guangdong Basic and Applied Basic Research Foundation

The Raising initial capital for High-level Talents of Central China Normal University

Fundamental Research Funds for the Central Universities

Humanities and Social Science Planning Fund from Ministry of Education

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00500-021-06185-3.pdf

Reference34 articles.

1. Abdullah Y, Birdal S, Ufuk Y (2018) Maximum likelihood estimation for the parameters of skew normal distribution using genetic algorithm. Swarm and Evolutionary Comput 38(2):127–138

2. Alizadeh AH, Huang CY, Dellen SV (2015) A regime switching approach for hedging tanker shipping freight rates. Energy Econom 49(3):44–59

3. Alzghool R, Al-Zubi LM (2018) Semi-parametric estimation for ARCH models. Alexandria Eng J 57(1):367–373

4. Angle R (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50:987–1007

5. Backus D, Foresi S, Zin S (1998) Arbitrage opportunities in arbitrage-free models of bond pricing. J Business and Econom Statistics 16:13–24

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Time–frequency domain based optimization of hedging strategy: Evidence from CSI 500 spot and futures;Expert Systems with Applications;2024-03

2. An Efficient Self-Tuning Proposal Distribution for Random Variate Generation With Complex Density Representation;IEEE Access;2023