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Risks
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Portfolio Optimization, Risk and Factor Analysis
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Portfolio Optimization, Risk and Factor Analysis

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 18074

Special Issue Editors

Prof. Dr. Qiji Zhu

E-Mail Website
Guest Editor
Department of Mathematics, Western Michigan University, Kalamazoo, MI 49008, USA
Interests: financial mathematics and risk management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Stanislaus Maier-Paape

E-Mail Website
Guest Editor
Institut für Mathematik, RWTH Aachen University, D-52062 Aachen, Germany
Interests: asset allocation; risk management; portfolio optimization and quantitative finance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Portfolio optimization as well as related risk and factor analysis are central themes in financial mathematics. The pioneering work of Markowitz (optimal) portfolio theory has had a great impact on both financial theory and applications. Early portfolio theory focused on the trade-off between mean as an indication for the reward and variation as a risk measure.

On the other hand, factor analysis aims to pinpoint the source of returns and studies whether or not these returns come from overall market exposure.

Commonly used factors are, e.g., low volatility, momentum, value or size. While typical factor models use a preselection of factor baskets of stocks in order to generate their edge, portfolio optimization applies optimization techniques to calculate portfolio weights from risk factors (like volatility or drawdown) and return factors (like momentum).

The two topics are intrinsically related by the key factors, and combined approaches might lead to new perspectives.

The research submitted to this Special Issue should be supported by a statistical approach, including but not restricted to absolute/relative performance of applying the proposed method to typical benchmarks, such as indexes or equal weight portfolios. An important but often neglected question could be, for instance, how stable the relevant factor is in terms of prediction quality (not only with respect to performance but also for the factor itself) or the dependence of factor performance on market phases.

This Special Issue aims to stimulate discussions on new developments of the portfolio theory with emphasis on new optimization techniques and related factor analysis. We therefore welcome and encourage the submission of high-quality papers related, but not limited to, the following topics:

- Portfolio optimization
- Analysis of risk measures
- Factor analysis and baskets
- Applied risk management
- Asset allocation in theory and practice
- Application in finance and elsewhere
- Combinations of the above

Prof. Dr. Qiji Zhu
Prof. Dr. Stanislaus Maier-Paape
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. Risks 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 1800 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 theory
  • applied finance
  • risk measures
  • factor analysis
  • asset allocation

Published Papers (5 papers)

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Research

12 pages, 642 KiB  
Article
An Unhedgeable Black–Scholes–Merton Implicit Option?
by Alfredo M. Pereira and M. Sean Tarter
Risks 2022, 10(7), 134; https://doi.org/10.3390/risks10070134 - 29 Jun 2022
Viewed by 1537
Abstract
In this paper, we focus on an implicit assumption in the BSM framework that limits the scope of market network connections to seeking gains in the currency basis, i.e., on trading strategies between the numeraire and the stock and between the numeraire and [...] Read more.
In this paper, we focus on an implicit assumption in the BSM framework that limits the scope of market network connections to seeking gains in the currency basis, i.e., on trading strategies between the numeraire and the stock and between the numeraire and the option, separately. We relax this assumption and derive the equivalent of the standard BSM approach under a more general market network framework in order to assess its implications. In doing so, we find that it is not possible to hedge on an implicit option that allows one to directly trade the option and stock. This represents a potential challenge to the BSM framework, since the missing market network connection provides a potentially useful mechanism for risk-bearing portfolio managers to alter their portfolios. Full article
(This article belongs to the Special Issue Portfolio Optimization, Risk and Factor Analysis)
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16 pages, 983 KiB  
Article
Revisiting Investability of Heritage Properties through Indexation and Portfolio Frontier Analysis
by Chin Tiong Cheng, Gabriel Hoh Teck Ling, Yee-Siang Gan, Wai Fang Wong and Kong Seng Lai
Risks 2021, 9(5), 91; https://doi.org/10.3390/risks9050091 - 10 May 2021
Cited by 2 | Viewed by 2105
Abstract
In recent years, the soaring prices of heritage properties in Georgetown, Penang have gained the attention of practitioners and investors. The practitioners claim that the prices of heritage properties within the core and buffer zones in Georgetown have increased more than 300% since [...] Read more.
In recent years, the soaring prices of heritage properties in Georgetown, Penang have gained the attention of practitioners and investors. The practitioners claim that the prices of heritage properties within the core and buffer zones in Georgetown have increased more than 300% since the city was recognized as a UNESCO World Heritage site in 2008. Such heritage properties containing historical or art elements that lead to forming a diversified portfolio could exert a low correlation of returns with conventional assets. In addition, rehabilitation of heritage properties requires high restoration costs and conversion fees. Despite the above claims, there is an absence of empirical studies relating to heritage investability, particularly to prove whether the heritage properties are truly worth investing in. Thus, this study incorporates a self-developed heritage properties Index (PIHPI_HR) into the conventional investment portfolio for assessing diversification effects. This study has collected 853 units of transacted properties for constructing a 10-year price index (PIHPI_HR). Subsequently, its diversification effect was examined through the Efficient Frontier (EF), derived from the Modern Portfolio Theory (MPT). The findings have proven the optimization of the conventional portfolio by enabling investments in heritage properties where the return is higher than other investment assets at the same risk level. This study also unveiled the price movement of heritage properties together with their investment value, which is deemed to be useful for institutional investors and the public to formulate sustainable investment strategies in the future. Full article
(This article belongs to the Special Issue Portfolio Optimization, Risk and Factor Analysis)
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18 pages, 2523 KiB  
Article
Global Stock Selection with Hidden Markov Model
by Nguyet Nguyen and Dung Nguyen
Risks 2021, 9(1), 9; https://doi.org/10.3390/risks9010009 - 31 Dec 2020
Cited by 5 | Viewed by 3945
Abstract
Hidden Markov model (HMM) is a powerful machine-learning method for data regime detection, especially time series data. In this paper, we establish a multi-step procedure for using HMM to select stocks from the global stock market. First, the five important factors of a [...] Read more.
Hidden Markov model (HMM) is a powerful machine-learning method for data regime detection, especially time series data. In this paper, we establish a multi-step procedure for using HMM to select stocks from the global stock market. First, the five important factors of a stock are identified and scored based on its historical performances. Second, HMM is used to predict the regimes of six global economic indicators and find the time periods in the past during which these indicators have a combination of regimes that is similar to those predicted. Then, we analyze the five stock factors of the All country world index (ACWI) in the identified time periods to assign a weighted score for each stock factor and to calculate the composite score of the five factors. Finally, we make a monthly selection of 10% of the global stocks that have the highest composite scores. This strategy is shown to outperform those relying on either ACWI, any single stock factor, or the simple average of the five stock factors. Full article
(This article belongs to the Special Issue Portfolio Optimization, Risk and Factor Analysis)
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23 pages, 373 KiB  
Article
Portfolio Construction by Using Different Risk Models: A Comparison among Diverse Economic Scenarios
by Ahmed Imran Hunjra, Suha Mahmoud Alawi, Sisira Colombage, Uroosa Sahito and Mahnoor Hanif
Risks 2020, 8(4), 126; https://doi.org/10.3390/risks8040126 - 30 Nov 2020
Cited by 9 | Viewed by 5551
Abstract
We aim to construct portfolios by employing different risk models and compare their performance in order to understand their appropriateness for effective portfolio management for investors. Mean variance (MV), semi variance (SV), mean absolute deviation (MaD) and conditional value at risk (CVaR) are [...] Read more.
We aim to construct portfolios by employing different risk models and compare their performance in order to understand their appropriateness for effective portfolio management for investors. Mean variance (MV), semi variance (SV), mean absolute deviation (MaD) and conditional value at risk (CVaR) are considered as risk measures. The price data were extracted from the Pakistan stock exchange, Bombay stock exchange and Dhaka stock exchange under diverse economic conditions such as crisis, recovery and growth. We take the average of GDP of the selected period of each country as a cut-off point to make three economic scenarios. We use 40 stocks from the Pakistan stock exchange, 92 stocks from the Bombay stock exchange and 30 stocks from the Dhaka stock exchange. We compute optimal weights using global minimum variance portfolio (GMVP) for all stocks to construct optimal portfolios and analyze the data by using MV, SV, MaD and CVaR models for each subperiod. We find that CVaR (95%) gives better results in each scenario for all three countries and performance of portfolios is inconsistent in different scenarios. Full article
(This article belongs to the Special Issue Portfolio Optimization, Risk and Factor Analysis)
34 pages, 455 KiB  
Article
No-Arbitrage Principle in Conic Finance
by Mehdi Vazifedan and Qiji Jim Zhu
Risks 2020, 8(2), 66; https://doi.org/10.3390/risks8020066 - 19 Jun 2020
Cited by 1 | Viewed by 3453
Abstract
In a one price economy, the Fundamental Theorem of Asset Pricing (FTAP) establishes that no-arbitrage is equivalent to the existence of an equivalent martingale measure. Such an equivalent measure can be derived as the normal unit vector of the hyperplane that separates the [...] Read more.
In a one price economy, the Fundamental Theorem of Asset Pricing (FTAP) establishes that no-arbitrage is equivalent to the existence of an equivalent martingale measure. Such an equivalent measure can be derived as the normal unit vector of the hyperplane that separates the attainable gain subspace and the convex cone representing arbitrage opportunities. However, in two-price financial models (where there is a bid–ask price spread), the set of attainable gains is not a subspace anymore. We use convex optimization, and the conic property of this region to characterize the “no-arbitrage” principle in financial models with the bid–ask price spread present. This characterization will lead us to the generation of a set of price factor random variables. Under such a set, we can find the lower and upper bounds (supper-hedging and sub-hedging bounds) for the price of any future cash flow. We will show that for any given cash flow, for which the price is outside the above range, we can build a trading strategy that provides one with an arbitrage opportunity. We will generalize this structure to any two-price finite-period financial model. Full article
(This article belongs to the Special Issue Portfolio Optimization, Risk and Factor Analysis)
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