Special Issue "Risk Analysis and Portfolio Modelling"

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

Deadline for manuscript submissions: closed (31 March 2019).

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. David Allen
Website
Guest Editor
1 Honorary Professor, School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia
2 Honorary Professor, School of Business and Law, Edith Cowan University, Joondalup, WA 6027, Australia
3 Honorary Chair Professor, Department of Finance, Asia University, Wufeng 41354, Taiwan
Interests: investments; market microstructure; risk modelling; financial econometrics
Prof. Dr. Elisa Luciano
Website
Co-Guest Editor
Department of Economics and Statistics, University of Torino, Corso Unione Sovietica 218 bis, I-10134, Torino, Italy
Interests: risk management; credit risk; dependence in financial markets; markets with frictions; insurance applications

Special Issue Information

Dear Colleagues,

This Special Issue is concerned with the broad topic of Portfolio Analysis, and includes any novel theoretical or empirical research associated with the theoretical and empirical applications in this area.

Theoretical contributions relating to Portfolio Analysis should be associated with an empirical example, or directions in which the novel ideas might be applied in the context of portfolio modelling and assessment.

The Special Issue may be associated with any contributions in: Advances in portfolio theory, risk modelling, risk assessment and management, objective modelling criteria, loss function and risk measures, conditional and unconditional modelling of risk; applications of extreme value theory, volatility modelling and methods for capturing dependencies. New methods in time series analysis or methods for capturing dependencies in a portfolio context, such as applications of copula analysis, innovations in performance testing and measurement, or empirical comparisons of the efficacy of different approaches to portfolio modelling and management. 

Prof. Dr. David Allen
Prof. Dr. Elisa Luciano
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 papers will be 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 1000 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

  • Innovations in Portfolio Analysis
  • Analysis of dependencies
  • Portfolio Performance Analysis
  • Objective criteria
  • Applications of time-series techniques

Published Papers (12 papers)

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Editorial

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Open AccessEditorial
Risk Analysis and Portfolio Modelling
J. Risk Financial Manag. 2019, 12(4), 154; https://doi.org/10.3390/jrfm12040154 - 21 Sep 2019
Cited by 1
Abstract
Financial risk measurement is a challenging task because both the types of risk and their measurement techniques evolve quickly. This book collects a number of novel contributions for the measurement of financial risk, which addresses partially explored risks or risk takers in a [...] Read more.
Financial risk measurement is a challenging task because both the types of risk and their measurement techniques evolve quickly. This book collects a number of novel contributions for the measurement of financial risk, which addresses partially explored risks or risk takers in a wide variety of empirical contexts. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling) Printed Edition available

Research

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Open AccessArticle
The Outperformance Probability of Mutual Funds
J. Risk Financial Manag. 2019, 12(3), 108; https://doi.org/10.3390/jrfm12030108 - 26 Jun 2019
Cited by 1
Abstract
We propose the outperformance probability as a new performance measure, which can be used in order to compare a strategy with a specified benchmark, and develop the basic statistical properties of its maximum-likelihood estimator in a Brownian-motion framework. The given results are used [...] Read more.
We propose the outperformance probability as a new performance measure, which can be used in order to compare a strategy with a specified benchmark, and develop the basic statistical properties of its maximum-likelihood estimator in a Brownian-motion framework. The given results are used to investigate the question of whether mutual funds are able to beat the S&P 500 or the Russell 1000. Most mutual funds that are taken into consideration are, in fact, able to beat the market. We argue that one should refer to differential returns when comparing a strategy with a given benchmark and not compare both the strategy and the benchmark with the money-market account. This explains why mutual funds often appear to underperform the market, but this conclusion is fallacious. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling) Printed Edition available
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Open AccessArticle
Credit Scoring in SME Asset-Backed Securities: An Italian Case Study
J. Risk Financial Manag. 2019, 12(2), 89; https://doi.org/10.3390/jrfm12020089 - 17 May 2019
Cited by 1
Abstract
We investigate the default probability, recovery rates and loss distribution of a portfolio of securitised loans granted to Italian small and medium enterprises (SMEs). To this end, we use loan level data information provided by the European DataWarehouse platform and employ a logistic [...] Read more.
We investigate the default probability, recovery rates and loss distribution of a portfolio of securitised loans granted to Italian small and medium enterprises (SMEs). To this end, we use loan level data information provided by the European DataWarehouse platform and employ a logistic regression to estimate the company default probability. We include loan-level default probabilities and recovery rates to estimate the loss distribution of the underlying assets. We find that bank securitised loans are less risky, compared to the average bank lending to small and medium enterprises. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling) Printed Edition available
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Open AccessArticle
Value-at-Risk and Models of Dependence in the U.S. Federal Crop Insurance Program
J. Risk Financial Manag. 2019, 12(2), 65; https://doi.org/10.3390/jrfm12020065 - 16 Apr 2019
Cited by 2
Abstract
The federal crop insurance program covered more than 110 billion dollars in total liability in 2018. The program consists of policies across a wide range of crops, plans, and locations. Weather and other latent variables induce dependence among components of the portfolio. Computing [...] Read more.
The federal crop insurance program covered more than 110 billion dollars in total liability in 2018. The program consists of policies across a wide range of crops, plans, and locations. Weather and other latent variables induce dependence among components of the portfolio. Computing value-at-risk (VaR) is important because the Standard Reinsurance Agreement (SRA) allows for a portion of the risk to be transferred to the federal government. Further, the international reinsurance industry is extensively involved in risk sharing arrangements with U.S. crop insurers. VaR is an important measure of the risk of an insurance portfolio. In this context, VaR is typically expressed in terms of probable maximum loss (PML) or as a return period, whereby a loss of certain magnitude is expected to return within a given period of time. Determining bounds on VaR is complicated by the non-homogeneous nature of crop insurance portfolios. We consider several different scenarios for the marginal distributions of losses and provide sharp bounds on VaR using a rearrangement algorithm. Our results are related to alternative measures of portfolio risks based on multivariate distribution functions and alternative copula specifications. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling) Printed Edition available
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Open AccessArticle
Herding in Smart-Beta Investment Products
J. Risk Financial Manag. 2019, 12(1), 47; https://doi.org/10.3390/jrfm12010047 - 21 Mar 2019
Cited by 3
Abstract
We highlight herding of investors as one major risk factor that is typically ignored in statistical approaches to portfolio modelling and risk management. Our survey focuses on smart-beta investing where such methods and investor herding seem particularly relevant but its negative effects have [...] Read more.
We highlight herding of investors as one major risk factor that is typically ignored in statistical approaches to portfolio modelling and risk management. Our survey focuses on smart-beta investing where such methods and investor herding seem particularly relevant but its negative effects have not yet come to the fore. We point out promising and novel approaches of modelling herding risk which merit empirical analysis. This financial economists’ perspective supplements the vast statistical exploration of implementing factor strategies. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling) Printed Edition available
Open AccessArticle
The Determinants of Sovereign Risk Premium in African Countries
J. Risk Financial Manag. 2019, 12(1), 29; https://doi.org/10.3390/jrfm12010029 - 09 Feb 2019
Cited by 1
Abstract
This paper investigates the determinants of the sovereign risk premium in African countries. We employ the dynamic fixed effects model to determine the key drivers of sovereign bond spreads. Country-specific effects are fixed and the inclusion of dummy variables using the Bai–Perron multiple [...] Read more.
This paper investigates the determinants of the sovereign risk premium in African countries. We employ the dynamic fixed effects model to determine the key drivers of sovereign bond spreads. Country-specific effects are fixed and the inclusion of dummy variables using the Bai–Perron multiple structural break test is significant at a 5% level. For robustness, the time-series generalized method of moments (GMM) is used where the null hypothesis of the Sargan Test of over-identifying restrictions (OIR) and the Arellano–Bond Test of no autocorrelation are not rejected. This implies that the instruments used are valid and relevant. In addition, there is no autocorrelation in the error terms. Our results show that the exchange rate, Money supply/GDP (M2/GDP) ratio, and trade are insignificant. Furthermore, our findings indicate that public debt/GDP ratio, GDP growth, inflation rate, foreign exchange reserves, commodity price, and market sentiment are significant at a 5% and 10% level. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling) Printed Edition available
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Open AccessArticle
Time–Scale Relationship between Securitized Real Estate and Local Stock Markets: Some Wavelet Evidence
J. Risk Financial Manag. 2019, 12(1), 16; https://doi.org/10.3390/jrfm12010016 - 20 Jan 2019
Cited by 1
Abstract
This study revisits the relationship between securitized real estate and local stock markets by focusing on their time-scale co-movement and contagion dynamics across five developed countries. Since securitized real estate market is an important capital component of the domestic stock market in the [...] Read more.
This study revisits the relationship between securitized real estate and local stock markets by focusing on their time-scale co-movement and contagion dynamics across five developed countries. Since securitized real estate market is an important capital component of the domestic stock market in the respective economies, it is linked to the stock market. Earlier research does not have satisfactory results, because traditional methods average different relationships over various time and frequency domains between securitized real estate and local stock markets. According to our novel wavelet analysis, the relationship between the two asset markets is time–frequency varying. The average long run real estate–stock correlation fails to outweigh the average short run correlation, indicating the real estate markets examined may have become increasingly less sensitive to the domestic stock markets in the long-run in recent years. Moreover, securitized real estate markets appear to lead stock markets in the short run, whereas stock markets tend to lead securitized real estate markets in the long run, and to a lesser degree medium-term. Finally, we find incomplete real estate and local stock market integration among the five developed economies, given only weaker long-run integration beyond crisis periods. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling) Printed Edition available
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Open AccessArticle
A Communication Theoretic Interpretation of Modern Portfolio Theory Including Short Sales, Leverage and Transaction Costs
J. Risk Financial Manag. 2019, 12(1), 4; https://doi.org/10.3390/jrfm12010004 - 29 Dec 2018
Cited by 1
Abstract
Modern Portfolio Theory is the ground upon which most works in portfolio optimization context find their foundations. Many studies attempt to extend the Modern Portfolio Theory to include short sale, leverage and transaction costs, features not considered in Markowitz’s seminal work from 1952. [...] Read more.
Modern Portfolio Theory is the ground upon which most works in portfolio optimization context find their foundations. Many studies attempt to extend the Modern Portfolio Theory to include short sale, leverage and transaction costs, features not considered in Markowitz’s seminal work from 1952. The drawback of such theories is that they complicate considerably the simplicity of the original technique. Here, we propose a simple and unified method, which takes inspiration from, and shows connections with the matched filter theory in communications, to evaluate the best portfolio allocation with the possibility of including a leverage factor and short sales. Finally, we extend the presented method to also consider the transaction costs. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling) Printed Edition available
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Open AccessFeature PaperArticle
Capital Allocation in Decentralized Businesses
J. Risk Financial Manag. 2018, 11(4), 82; https://doi.org/10.3390/jrfm11040082 - 26 Nov 2018
Cited by 3
Abstract
This paper described a theory of capital allocation for decentralized businesses, taking into account the costs associated with risk capital. We derive an adjusted present value expression for making investment decisions, that incorporates the time varying profile of risk capital. We discuss the [...] Read more.
This paper described a theory of capital allocation for decentralized businesses, taking into account the costs associated with risk capital. We derive an adjusted present value expression for making investment decisions, that incorporates the time varying profile of risk capital. We discuss the implications for business performance measurement. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling) Printed Edition available
Open AccessArticle
Risk Assessment of Housing Market Segments: The Lender’s Perspective
J. Risk Financial Manag. 2018, 11(4), 69; https://doi.org/10.3390/jrfm11040069 - 26 Oct 2018
Cited by 2
Abstract
It is well known that risk factors influence how investment portfolios perform from a lender’s perspective; therefore, a thorough risk assessment of the housing market is vital. The aim of this paper was to analyze the risks from housing apartments in different housing [...] Read more.
It is well known that risk factors influence how investment portfolios perform from a lender’s perspective; therefore, a thorough risk assessment of the housing market is vital. The aim of this paper was to analyze the risks from housing apartments in different housing market segments by using the Stockholm, Sweden, owner-occupied apartment market as a case study. By applying quantitative and systems engineering methods, we (1) established the relationship between the overall housing market and several housing market segments, (2) analyzed the results from the quantitative model, and (3) finally provided a feasible portfolio regarding risk control based on the given data. The goal was to determine how different housing segment factors could reveal risk towards the overall market and offer better outlooks for risk management when it comes to housing apartments. The results indicated that the risk could be reduced at the same time as the return increased. From a lender’s perspective, this could reduce the overall risk. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling) Printed Edition available
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Open AccessFeature PaperArticle
Insider Trading and Institutional Holdings in Seasoned Equity Offerings
J. Risk Financial Manag. 2018, 11(3), 53; https://doi.org/10.3390/jrfm11030053 - 10 Sep 2018
Cited by 2
Abstract
We investigate three issues about the impact of insider trades and institutional holdings on seasoned equity offerings (SEOs). First, we test how insider trades affect the trading behavior of institutional investors in SEOs. Second, we test whose trading behavior, either insiders or institutional [...] Read more.
We investigate three issues about the impact of insider trades and institutional holdings on seasoned equity offerings (SEOs). First, we test how insider trades affect the trading behavior of institutional investors in SEOs. Second, we test whose trading behavior, either insiders or institutional investors, has greater explanatory power for the performance of SEO firms after issuing new stocks. Third, we analyze the industry-wide spillover effects of insider trades and institutional holdings. Empirically, we find that insiders and institutional investors of SEO firms may utilize similar information in their transactions because insider trades induce similar trading behavior for institutional investors. In addition, insider trades, relative to institutional holdings, have greater explanatory power for SEO firm’s long-term performance. Finally, compared with insider trades, institutional holdings have a more significant spillover effect in the industry of SEO firms. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling) Printed Edition available
Open AccessArticle
Credit Ratings and Liquidity Risk for the Optimization of Debt Maturity Structure
J. Risk Financial Manag. 2018, 11(2), 24; https://doi.org/10.3390/jrfm11020024 - 11 May 2018
Cited by 1
Abstract
The purpose of this study is to examine the relationship between credit rating scales and debt maturity choices. A liquidity hypothesis is used to formulate the testable proposition and conceptual framework. Generalized linear model (GLM) and pooled ordinary least square (OLS) are utilized [...] Read more.
The purpose of this study is to examine the relationship between credit rating scales and debt maturity choices. A liquidity hypothesis is used to formulate the testable proposition and conceptual framework. Generalized linear model (GLM) and pooled ordinary least square (OLS) are utilized by SAS programming to test the proposed hypothesis. Other different estimation techniques are also used for robust evidence. Results suggest that companies with high and low ratings have a shorter debt maturity. Companies with medium ratings have longer debt maturity structure. Liquidity shows a negative association with longer debt maturity structure. It is evident that at high rating scale with high liquidity, and at lower rating scales with lower liquidity firms have a shorter debt maturity. Mid rated firms with a low probability of refinancing risk show longer debt maturity structure. Considering refinancing risk by Asian companies make the nonlinear relationship between credit ratings and debt maturity choices. Results suggest the importance of credit ratings for the optimization of debt maturity structure of Asian firms, which was totally overlooked by the past studies. The findings of this study are consistent with the liquidity hypothesis. The findings also motivating financial managers and investors to consider credit ratings as a measure of financial constraints. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling) Printed Edition available
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