arXiv:2106.12425
공식 코드 · Papers with Code
Portfolio Allocation under Asymmetric Dependence in Asset Returns using Local Gaussian Correlations
원문 초록
It is well known that there are asymmetric dependence structures between financial returns. In this paper we use a new nonparametric measure of local dependence, the local Gaussian correlation, to improve portfolio allocation. We extend the classical mean-variance framework, and show that the portfolio optimization is straightforward using our new approach, only relying on a tuning parameter (the bandwidth). The new method is shown to outperform the equally weighted (1/N) portfolio and the classical Markowitz portfolio for monthly asset returns data.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
skfolio: Portfolio Optimization in Python
A Scalable Gradient-Based Optimization Framework for Sparse Minimum-Variance Portfolio Selection
Integrating LLM-Generated Views into Mean-Variance Optimization Using the Black-Litterman Model
Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure
Semi-Decision-Focused Learning with Deep Ensembles: A Practical Framework for Robust Portfolio Optimization
A Deep Reinforcement Learning Framework for Dynamic Portfolio Optimization: Evidence from China's Stock Market
토론
이 논문과 연결된 포럼 글이 아직 없습니다.