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Portfolio Selection and Risk Analytics
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Portfolio Selection and Risk Analytics

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Markets".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 5011

Special Issue Editors

Prof. Dr. Nalin Chanaka Edirisinghe

E-Mail Website
Guest Editor
Lally School of Management, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
Interests: portfolio optimization; index tracking funds design; quadratic programming and market impact of trading; derivative pricing
Dr. Jaehwan Jeong

E-Mail Website
Guest Editor
Department of Management, Radford University, Radford, VA, USA
Interests: optimization; mathematical modeling; data analysis; indefinite quadratic mixed integer programming; financial optimization modeling; dimensionality reduction

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to explore and advance the field of portfolio risk management. In the face of the fast-paced evolution of financial markets in a data-rich environment, the need for effective risk management is critically important. This Special Issue seeks to gather innovative research and practical applications that contribute to the understanding and management of risks associated with investment portfolios.

The scope of this Special Issue encompasses a wide range of topics within portfolio selection and risk management disciplines, including but not limited to risk measurement and evaluation methodologies, asset allocation strategies, portfolio optimization models and solution, risk forecasting models, tail-risk management, and risk diversification approaches.

We invite researchers and industry practitioners to submit original contributions that shed light on novel concepts, empirical studies, theoretical frameworks, and practical insights in the domain of portfolio risk management. This Special Issue aspires to advance the knowledge and practice of portfolio management, leading to improved risk mitigation and superior portfolio performance in the increasingly complex financial investments landscape.

Prof. Dr. Nalin Chanaka Edirisinghe
Dr. Jaehwan Jeong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • portfolio optimization
  • risk modeling and analytics
  • trading strategies
  • asset allocation
  • investment risk management

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

26 pages, 1702 KiB  
Article
Time–Frequency Co-Movement of South African Asset Markets: Evidence from an MGARCH-ADCC Wavelet Analysis
by Fabian Moodley, Sune Ferreira-Schenk and Kago Matlhaku
J. Risk Financial Manag. 2024, 17(10), 471; https://doi.org/10.3390/jrfm17100471 - 18 Oct 2024
Viewed by 620
Abstract
The growing prominence of generating a well-diversified portfolio by holding securities from multi-asset markets has, over the years, drawn criticism. Various financial market events have caused asset markets to co-move, especially in emerging markets, which reduces portfolio diversification and enhances return losses. Consequently, [...] Read more.
The growing prominence of generating a well-diversified portfolio by holding securities from multi-asset markets has, over the years, drawn criticism. Various financial market events have caused asset markets to co-move, especially in emerging markets, which reduces portfolio diversification and enhances return losses. Consequently, this study examines the time–frequency co-movement of multi-asset classes in South Africa by using the Multivariate Generalized Autoregressive Conditional Heteroscedastic–Asymmetrical Dynamic Conditional Correlation (MGARCH-DCC) model, Maximal Overlap Discrete Wavelet Transformation (MODWT), and the Continuous Wavelet Transform (WTC) for the period 2007 to 2024. The findings demonstrate that the equity–bond, equity–property, equity–gold, bond–property, bond–gold, and property–gold markets depict asymmetrical time-varying correlations. Moreover, correlation in these asset pairs varies at investment periods (short-term, medium-term, and long-term), with historical events such as the 2007/2008 Global Financial Crisis (GFC) and the COVID-19 pandemic causing these asset pairs to co-move at different investment periods, which reduces diversification properties. The findings suggest that South African multi-asset markets co-move, affecting the diversification properties of holding multi-asset classes in a portfolio at different investment periods. Consequently, investors should consider the holding periods of each asset market pair in a portfolio as they dictate the level of portfolio diversification. Investors should also remember that there are lead–lag relationships and risk transmission between asset market pairs, enhancing portfolio volatility. This study assists investors in making more informed investment decisions and identifying optimal entry or exit points within South African multi-asset markets. Full article
(This article belongs to the Special Issue Portfolio Selection and Risk Analytics)
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34 pages, 1965 KiB  
Article
Portfolio Optimization with Sector Return Prediction Models
by Wolfgang Bessler and Dominik Wolff
J. Risk Financial Manag. 2024, 17(6), 254; https://doi.org/10.3390/jrfm17060254 - 20 Jun 2024
Cited by 1 | Viewed by 1747
Abstract
We analyze return predictability for U.S. sectors based on fundamental, macroeconomic, and technical indicators and analyze whether return predictions improve tactical asset allocation decisions. We study the out-of-sample predictive power of individual variables for forecasting sector returns and analyze multivariate predictive regression models, [...] Read more.
We analyze return predictability for U.S. sectors based on fundamental, macroeconomic, and technical indicators and analyze whether return predictions improve tactical asset allocation decisions. We study the out-of-sample predictive power of individual variables for forecasting sector returns and analyze multivariate predictive regression models, including OLS, regularized regressions, principal component regressions, the three-pass regression filter, and forecast combinations. Using an out-of-sample Black–Litterman portfolio optimization framework and employing predicted returns as investors’ ‘views’, we evaluate the benefits of sector return forecasts for investors. We find that portfolio optimization with sector return prediction models significantly outperforms portfolios using historical averages as well as passive benchmark portfolios. Full article
(This article belongs to the Special Issue Portfolio Selection and Risk Analytics)
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11 pages, 920 KiB  
Article
Practical Improvements to Mean-Variance Optimization for Multi-Asset Class Portfolios
by Marin Lolic
J. Risk Financial Manag. 2024, 17(5), 183; https://doi.org/10.3390/jrfm17050183 - 29 Apr 2024
Viewed by 2085
Abstract
In the more than 70 years since Markowitz introduced mean-variance optimization for portfolio construction, academics and practitioners have documented numerous weaknesses in the approach. In this paper, we propose two easily understandable improvements to mean-variance optimization in the context of multi-asset class portfolios, [...] Read more.
In the more than 70 years since Markowitz introduced mean-variance optimization for portfolio construction, academics and practitioners have documented numerous weaknesses in the approach. In this paper, we propose two easily understandable improvements to mean-variance optimization in the context of multi-asset class portfolios, each of which provides less extreme and more stable portfolio weights. The first method sacrifices a small amount of expected optimality for reduced weight concentration, while the second method randomly resamples the available assets. Additionally, we develop a process for testing the performance of portfolio construction approaches on simulated data assuming variable degrees of forecasting skill. Finally, we show that the improved methods achieve better out-of-sample risk-adjusted returns than standard mean-variance optimization for realistic investor skill levels. Full article
(This article belongs to the Special Issue Portfolio Selection and Risk Analytics)
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