---
title: fBetas and Portfolio Optimization with f-Divergence Risk Measures
url: https://www.ml-quant.com/papers/repec/taf-quantf-v-23-y-2023-i-10-p-1483-1496/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:taf:quantf:v:23:y:2023:i:10:p:1483-1496
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F14697688.2023.2230629%3Bh%3Drepec%3Ataf%3Aquantf%3Av%3A23%3Ay%3A2023%3Ai%3A10%3Ap%3A1483-1496
featured: 2023-10-12
citations: unknown
topic: Portfolio & Allocation
---


# fBetas and Portfolio Optimization with f-Divergence Risk Measures

The paper presents a new f-Beta for portfolio optimization, comparing its performance with Standard Beta and Drawdown Betas using selected stocks against the S&P 500 market index.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F14697688.2023.2230629%3Bh%3Drepec%3Ataf%3Aquantf%3Av%3A23%3Ay%3A2023%3Ai%3A10%3Ap%3A1483-1496
- Identifier: RePEc:taf:quantf:v:23:y:2023:i:10:p:1483-1496
- Released: 2023-10-12
- First featured: Quant Letter No. 20 (2023-10-12): https://www.ml-quant.com/issues/2023-10-12/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [End-to-End, Decision-based, Cardinality-Constrained Portfolio Optimization](https://www.ml-quant.com/papers/ssrn/4598167/): The article introduces a comprehensive framework for portfolio optimization using a neural network, and compares three different problem-solving methods within this framework.
- [Covariance matrix filtering and portfolio optimisation: the average oracle vs non-linear shrinkage and all the variants of DCC-NLS](https://www.ml-quant.com/papers/arxiv/2309.17219/): The Average Oracle, a fast covariance filtering method, outperforms complex methods, yielding superior Sharpe ratios in large-scale experiments.
- [Topological Portfolio Selection and Optimization](https://www.ml-quant.com/papers/arxiv/2310.14881/): The paper suggests the use of Statistically Robust Information Filtering Network (SR-IFN) to minimize noise in empirical covariance estimation, improving portfolio optimization by aiding in the selection of diversified, high-performing portfolios.
- [A Comparative Study of Portfolio Optimization Methods for the Indian Stock Market](https://www.ml-quant.com/papers/arxiv/2310.14748/): The chapter evaluates and compares the MVP, HRP, and HERC portfolio optimization methods using data from 15 sectors of the Indian stock market.
- [A monotone numerical integration method for mean-variance portfolio optimization under jump-diffusion models](https://www.ml-quant.com/papers/arxiv/2309.05977/): The research introduces an efficient numerical integration method for portfolio optimization, demonstrating its computational efficiency and accuracy, and its convergence to the unique solution of the optimization problem.
- [A Unified Framework for Fast Large-Scale Portfolio Optimization](https://www.ml-quant.com/papers/arxiv/2303.12751/): A new large-scale portfolio optimization framework, using shrinkage and regularization techniques, has been tested and proven effective using 50 years of US company return data.