A Commemorative Issue in Honor of Professor Michael McAleer, 1952–2021

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074).

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 32869

Special Issue Editors


E-Mail Website
Guest Editor
1. Department of Finance, Fintech & Blockchain Research Center, Big Data Research Center, Asia University, Taichung City 41354, Taiwan
2. Department of Medical Research, China Medical University Hospital, Taichung City 40447, Taiwan
3. Department of Economics and Finance, The Hang Seng University of Hong Kong, Hong Kong, China
Interests: behavioral models; mathematical modeling; econometrics; energy economics; equity analysis; investment theory; risk management; behavioral economics; operational research; decision theory; environmental economics; public health; time series analysis; forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is a Special Issue to honor Professor Michael McAleer who passed away peacefully on 8 July 2021 after a long fight with cancer. Michael held a PhD on Economics from Queen’s University, Canada. He held positions as University Research Chair Professor, Department of Finance, College of Management, and Department of Bioinformatics and Medical Engineering, College of Information and Electrical Engineering, Asia University, Taiwan; Erasmus Visiting Professor of Quantitative Finance, Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, the Netherlands; Adjunct Professor, Department of Economic Analysis and ICAE, Complutense University of Madrid (founded 1293), Spain; and Adjunct Professor, Department of Mathematics and Statistics, University of Canterbury, New Zealand. On numerous occasions, he served as a distinguished visiting professor at the University of Tokyo, Kyoto University, and Osaka University, Japan; University of Padova (founded 1222), Italy, Complutense University of Madrid (founded 1293), Spain; Ca' Foscari University of Venice, Italy; University of Zurich, Switzerland; University of Hong Kong, Chinese University of Hong Kong; and Hong Kong University of Science and Technology. He was an elected Distinguished Fellow of the International Engineering and Technology Institute (DFIETI), and an elected Fellow of the Academy of the Social Sciences in Australia (FASSA), International Environmental Modelling and Software Society (FIEMSS), Modelling and Simulation Society of Australia and New Zealand (FMSSANZ), Tinbergen Institute, the Netherlands, Journal of Econometrics, and Econometric Reviews. He was the Editor-in-Chief of the Journal of Risk and Financial Management (MDPI) and had guest-edited numerous Special Issues in JRFM. In terms of academic publications, he published 1000+ journal articles and books in economics, financial econometrics, quantitative finance, risk and financial management, econometrics, statistics, time series analysis, energy economics and finance, sustainability, carbon emissions, climate change econometrics, forecasting, informatics, data mining, bibliometrics, and international rankings of journals and academics.

We expect to collect papers in all the areas that Michael had an interest in and contributed to, and we urge all of his colleagues and collaborators over the years to contribute to this Special Issue and thus honor his legacy to the profession.

Prof. Dr. Thanasis Stengos
Prof. Dr. Wing-Keung Wong
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

  • econometric theory
  • financial econometrics
  • risk and financial management
  • time series analysis
  • simulations and data mining
  • forecasting

Published Papers (12 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

20 pages, 7535 KiB  
Article
Fractile Graphical Analysis in Finance: A New Perspective with Applications
by Anil K. Bera and Aurobindo Ghosh
J. Risk Financial Manag. 2022, 15(9), 412; https://doi.org/10.3390/jrfm15090412 - 19 Sep 2022
Viewed by 1495
Abstract
Fractile Graphical Analysis (FGA) was proposed by Prasanta Chandra Mahalanobis in 1961 as a method for comparing two distributions at two different points (of time or space) controlling for the rank of a covariate through fractile groups. We use bootstrap techniques to formalize [...] Read more.
Fractile Graphical Analysis (FGA) was proposed by Prasanta Chandra Mahalanobis in 1961 as a method for comparing two distributions at two different points (of time or space) controlling for the rank of a covariate through fractile groups. We use bootstrap techniques to formalize the heuristic method used by Mahalanobis for approximating the standard error of the dependent variable using fractile graphs from two independently selected “interpenetrating network of subsamples.” We highlight the potential and revisit this underutilized technique of FGA with a historical perspective. We explore a new non-parametric regression method called Fractile Regression where we condition on the ranks of the covariate and compare it with existing regression techniques. We apply this method to compare mutual fund inflow distributions after conditioning on ranks or fractiles of pre-tax and post-tax returns and compare distributions of private and public equity returns after controlling for fractiles of assets under management size using the two sample smooth test. Full article
Show Figures

Figure 1

9 pages, 297 KiB  
Article
Saddlepoint Method for Pricing European Options under Markov-Switching Heston’s Stochastic Volatility Model
by Mengzhe Zhang and Leunglung Chan
J. Risk Financial Manag. 2022, 15(9), 396; https://doi.org/10.3390/jrfm15090396 - 06 Sep 2022
Viewed by 1345
Abstract
This paper evaluates the prices of European-style options when dynamics of the underlying asset is assumed to follow a Markov-switching Heston’s stochastic volatility model. Under this framework, the expected return and the long-term mean of the variance of the underlying asset rely on [...] Read more.
This paper evaluates the prices of European-style options when dynamics of the underlying asset is assumed to follow a Markov-switching Heston’s stochastic volatility model. Under this framework, the expected return and the long-term mean of the variance of the underlying asset rely on states of the economy modeled by a continuous-time Markov chain. There is evidence that the Markov-switching Heston’s stochastic volatility model performs well in capturing major events affecting price dynamics. However, due to the nature of the model, analytic solutions for the prices of options or other financial derivatives do not exist. By means of the saddlepoint method, an analytic approximation for European-style option price is presented. The saddlepoint method gives an effective approximation to option prices under the Markov-switching Heston’s stochastic volatility model. Full article
Show Figures

Figure 1

15 pages, 334 KiB  
Article
Estimation and Inference for the Threshold Model with Hybrid Stochastic Local Unit Root Regressors
by Chaoyi Chen and Thanasis Stengos
J. Risk Financial Manag. 2022, 15(6), 242; https://doi.org/10.3390/jrfm15060242 - 28 May 2022
Cited by 2 | Viewed by 2270
Abstract
In this paper, we study the estimation and inference of the threshold model with hybrid local stochastic unit root regressors. Our main contribution is to propose an estimator that generalizes the threshold model with various forms of nonstationary regressors and to obtain its [...] Read more.
In this paper, we study the estimation and inference of the threshold model with hybrid local stochastic unit root regressors. Our main contribution is to propose an estimator that generalizes the threshold model with various forms of nonstationary regressors and to obtain its limiting distribution theory. In particular, our proposed model generalizes the threshold model with unit root, local-to-unity, and stochastic unit root regressors. We provide the estimation strategy for the least squares estimator and derive the asymptotic results for the proposed estimator. Depending on the diminishing rate of the threshold effect, we find that the limiting distribution of the threshold estimator takes different forms. Monte Carlo simulations are used to assess our proposed estimator’s finite sample performance, which is found to perform well. Full article
25 pages, 642 KiB  
Article
Cryptocurrencies, Diversification and the COVID-19 Pandemic
by David E. Allen
J. Risk Financial Manag. 2022, 15(3), 103; https://doi.org/10.3390/jrfm15030103 - 24 Feb 2022
Cited by 11 | Viewed by 3465
Abstract
This paper features an analysis of cryptocurrencies and the impact of the COVID-19 pandemic on their effectiveness as a portfolio diversification tool and explores the correlations between the continuously compounded returns on Bitcoin, Ethereum and the S&P500 Index using a variety of parametric [...] Read more.
This paper features an analysis of cryptocurrencies and the impact of the COVID-19 pandemic on their effectiveness as a portfolio diversification tool and explores the correlations between the continuously compounded returns on Bitcoin, Ethereum and the S&P500 Index using a variety of parametric and non-parametric techniques. These methods include linear standard metrics such as the application of ordinary least squares regression (OLS) and the Pearson, Spearman and Kendall’s tau measures of association. In addition, non-linear, non-parametric measures such as the Generalised Measure of Correlation (GMC) and non-parametric copula estimates are applied. The results across this range of measures are consistent. The metrics suggest that, whilst the shock of the COVID-19 pandemic does not appear to have increased the correlations between the cryptocurrency series, it appears to have increased the correlations between the returns on cryptocurrencies and those on the S&P500 Index. This suggests that investments in cryptocurrencies are not likely to offer key diversification strategies in times of crisis, on the basis of evidence provided by this crisis. Full article
Show Figures

Figure 1

17 pages, 446 KiB  
Article
Self-Weighted LSE and Residual-Based QMLE of ARMA-GARCH Models
by Shiqing Ling and Ke Zhu
J. Risk Financial Manag. 2022, 15(2), 90; https://doi.org/10.3390/jrfm15020090 - 19 Feb 2022
Cited by 1 | Viewed by 1861
Abstract
This paper studies the self-weighted least squares estimator (SWLSE) of the ARMA model with GARCH noises. It is shown that the SWLSE is consistent and asymptotically normal when the GARCH noise does not have a finite fourth moment. Using the residuals from the [...] Read more.
This paper studies the self-weighted least squares estimator (SWLSE) of the ARMA model with GARCH noises. It is shown that the SWLSE is consistent and asymptotically normal when the GARCH noise does not have a finite fourth moment. Using the residuals from the estimated ARMA model, it is shown that the residual-based quasi-maximum likelihood estimator (QMLE) for the GARCH model is consistent and asymptotically normal, but if the innovations are asymmetric, it is not as efficient as that when the GARCH process is observed. Using the SWLSE and residual-based QMLE as the initial estimators, the local QMLE for ARMA-GARCH model is asymptotically normal via an one-step iteration. The importance of the proposed estimators is illustrated by simulated data and five real examples in financial markets. Full article
Show Figures

Figure 1

23 pages, 298 KiB  
Article
Testing Stock Market Efficiency from Spillover Effect of Panama Leaks
by Adeel Nasir, Ștefan Cristian Gherghina, Mário Nuno Mata, Kanwal Iqbal Khan, Pedro Neves Mata and Joaquim António Ferrão
J. Risk Financial Manag. 2022, 15(2), 79; https://doi.org/10.3390/jrfm15020079 - 14 Feb 2022
Viewed by 2452
Abstract
On 3 April 2016, Mossack Fonseca provided the historically most significant leak of its shareholder’s data for owning offshore companies. Shareholders include many political and influential figures around the globe, which causes a moral hazard. The study analyses the effects of Panama leak [...] Read more.
On 3 April 2016, Mossack Fonseca provided the historically most significant leak of its shareholder’s data for owning offshore companies. Shareholders include many political and influential figures around the globe, which causes a moral hazard. The study analyses the effects of Panama leak events on five stock exchanges to ensure the market efficiency and investor perception related to the Panama leaks. Event study methodology is used on five occasions associated with Panama papers, i.e., the resignation of the Prime Minister of Iceland on 5 April 2016, Jurgen Mossack’s resignation on 7 April 2016, the resignation of the Spanish Minister of Industry on 15 April 2016, the 450 personalities of Pakistan that were nominated in Panama papers on 15 April 2016, and the formation of an inquiry commission to inquire into the matter. The market efficiency of five stock exchanges was checked, i.e., the KSE 100 of Pakistan, the OMXIPI exchange of Iceland, the IBEX 35 of Spain, the New York stock exchange (NYSE), and S&P 500. The market remains efficient for most events and investor behaviour changes for one or two days around the event day (this event has concise term significant abnormal returns in all stock exchanges or concise term significant abnormal macroeconomic effects are observed in all stock exchanges). Full article
26 pages, 1297 KiB  
Article
Hierarchical Time-Varying Estimation of Asset Pricing Models
by Richard T. Baillie, Fabio Calonaci and George Kapetanios
J. Risk Financial Manag. 2022, 15(1), 14; https://doi.org/10.3390/jrfm15010014 - 04 Jan 2022
Cited by 1 | Viewed by 1671
Abstract
This paper presents a new hierarchical methodology for estimating multi factor dynamic asset pricing models. The approach is loosely based on the sequential Fama–MacBeth approach and developed in a kernel regression framework. However, the methodology uses a very flexible bandwidth selection method which [...] Read more.
This paper presents a new hierarchical methodology for estimating multi factor dynamic asset pricing models. The approach is loosely based on the sequential Fama–MacBeth approach and developed in a kernel regression framework. However, the methodology uses a very flexible bandwidth selection method which is able to emphasize recent data and information to derive the most appropriate estimates of risk premia and factor loadings at each point in time. The choice of bandwidths and weighting schemes are achieved by a cross-validation procedure; this leads to consistent estimators of the risk premia and factor loadings. Additionally, an out-of-sample forecasting exercise indicates that the hierarchical method leads to a statistically significant improvement in forecast loss function measures, independently of the type of factor considered. Full article
Show Figures

Figure 1

26 pages, 4613 KiB  
Article
Dimension Reduction via Penalized GLMs for Non-Gaussian Response: Application to Stock Market Volatility
by Tao Li, Anthony F. Desmond and Thanasis Stengos
J. Risk Financial Manag. 2021, 14(12), 583; https://doi.org/10.3390/jrfm14120583 - 04 Dec 2021
Viewed by 1877
Abstract
We fit U.S. stock market volatilities on macroeconomic and financial market indicators and some industry level financial ratios. Stock market volatility is non-Gaussian distributed. It can be approximated by an inverse Gaussian (IG) distribution or it can be transformed by Box–Cox transformation to [...] Read more.
We fit U.S. stock market volatilities on macroeconomic and financial market indicators and some industry level financial ratios. Stock market volatility is non-Gaussian distributed. It can be approximated by an inverse Gaussian (IG) distribution or it can be transformed by Box–Cox transformation to a Gaussian distribution. Hence, we used a Box–Cox transformed Gaussian LASSO model and an IG GLM LASSO model as dimension reduction techniques and we attempted to identify some common indicators to help us forecast stock market volatility. Via simulation, we validated the use of four models, i.e., a univariate Box–Cox transformation Gaussian LASSO model, a three-phase iterative grid search Box–Cox transformation Gaussian LASSO model, and both canonical link and optimal link IG GLM LASSO models. The latter two models assume an approximately IG distributed response. Using these four models in an empirical study, we identified three macroeconomic indicators that could help us forecast stock market volatility. These are the credit spread between the U.S. Aaa corporate bond yield and the 10-year treasury yield, the total outstanding non-revolving consumer credit, and the total outstanding non-financial corporate bonds. Full article
Show Figures

Figure 1

24 pages, 373 KiB  
Article
Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research
by Li Xian Liu, Shuangzhe Liu and Milind Sathye
J. Risk Financial Manag. 2021, 14(10), 474; https://doi.org/10.3390/jrfm14100474 - 08 Oct 2021
Cited by 7 | Viewed by 4148
Abstract
Risk management has been a topic of great interest to Michael McAleer. Even as recent as 2020, his paper on risk management for COVID-19 was published. In his memory, this article is focused on bankruptcy risk in financial firms. For financial institutions in [...] Read more.
Risk management has been a topic of great interest to Michael McAleer. Even as recent as 2020, his paper on risk management for COVID-19 was published. In his memory, this article is focused on bankruptcy risk in financial firms. For financial institutions in particular, banks are considered special, given that they perform risk management functions that are unique. Risks in banking arise from both internal and external factors. The GFC underlined the need for comprehensive risk management, and researchers since then have been working towards fulfilling that need. Similarly, the central banks across the world have begun periodic stress-testing of banks’ ability to withstand shocks. This paper investigates the machine-learning and statistical techniques used in the literature on bank failure prediction. The study finds that though considerable progress has been made using advanced statistical and computational techniques, given the complex nature of banking risk, the ability of statistical techniques to predict bank failures is limited. Machine-learning-based models are increasingly becoming popular due to their significant predictive ability. The paper also suggests the directions for future research. Full article
15 pages, 3055 KiB  
Article
Contrasting Cryptocurrencies with Other Assets: Full Distributions and the COVID Impact
by Esfandiar Maasoumi and Xi Wu
J. Risk Financial Manag. 2021, 14(9), 440; https://doi.org/10.3390/jrfm14090440 - 14 Sep 2021
Cited by 6 | Viewed by 1967
Abstract
We investigate any similarity and dependence based on the full distributions of cryptocurrency assets, stock indices and industry groups. We characterize full distributions with entropies to account for higher moments and non-Gaussianity of returns. Divergence and distance between distributions are measured by metric [...] Read more.
We investigate any similarity and dependence based on the full distributions of cryptocurrency assets, stock indices and industry groups. We characterize full distributions with entropies to account for higher moments and non-Gaussianity of returns. Divergence and distance between distributions are measured by metric entropies, and are rigorously tested for statistical significance. We assess the stationarity and normality of assets, as well as the basic statistics of cryptocurrencies and traditional asset indices, before and after the COVID-19 pandemic outbreak. These assessments are not subjected to possible misspecifications of conditional time series models which are also examined for their own interests. We find that the NASDAQ daily return has the most similar density and co-dependence with Bitcoin daily return, generally, but after the COVID-19 outbreak in early 2020, even S&P500 daily return distribution is statistically closely dependent on, and indifferent from Bitcoin daily return. All asset distances have declined by 75% or more after the COVID-19 outbreak. We also find that the highest similarity before the COVID-19 outbreak is between Bitcoin and Coal, Steel and Mining industries, and after the COVID-19 outbreak is between Bitcoin and Business Supplies, Utilities, Tobacco Products and Restaurants, Hotels, Motels industries, compared to several others. This study shed light on examining distribution similarity and co-dependence between cryptocurrencies and other asset classes. Full article
Show Figures

Figure 1

24 pages, 342 KiB  
Article
Spurious Relationships for Nearly Non-Stationary Series
by Yushan Cheng, Yongchang Hui, Michael McAleer and Wing-Keung Wong
J. Risk Financial Manag. 2021, 14(8), 366; https://doi.org/10.3390/jrfm14080366 - 09 Aug 2021
Cited by 6 | Viewed by 2249
Abstract
Literature shows that the regression of independent and (nearly) nonstationary time series could result in spurious outcomes. In this paper, we conjecture that under some situations, the regression of two independent and nearly non-stationary series does not have any spurious problem at all. [...] Read more.
Literature shows that the regression of independent and (nearly) nonstationary time series could result in spurious outcomes. In this paper, we conjecture that under some situations, the regression of two independent and nearly non-stationary series does not have any spurious problem at all. To check whether our conjecture holds, we set up several situations and conduct simulations to justify our conjecture. Our simulations show that under some situations, the chance that the regressions being spurious is very high for all the cases simulated in our paper. Nonetheless, under some other situations, our simulation shows that the rejection rates are much smaller than the 5% level of significance for all the cases simulated in our paper, implying that our conjecture could hold under some situations that regression of two independent and nearly non-stationary series does not have any spurious problem at all. Full article

Review

Jump to: Research

32 pages, 1591 KiB  
Review
Order Routing Decisions for a Fragmented Market: A Review
by Suchismita Mishra and Le Zhao
J. Risk Financial Manag. 2021, 14(11), 556; https://doi.org/10.3390/jrfm14110556 - 17 Nov 2021
Cited by 1 | Viewed by 3467
Abstract
This paper reviews the up-to-date theoretical, empirical, and experimental literature related to the trading venue choice in the context of the fragmented equity markets. We provide a brief background on the history of trading fragmentation in the equity market and its determinants. We [...] Read more.
This paper reviews the up-to-date theoretical, empirical, and experimental literature related to the trading venue choice in the context of the fragmented equity markets. We provide a brief background on the history of trading fragmentation in the equity market and its determinants. We discuss the direct and indirect impacts of the market fragmentation on market quality in various dimensions, including liquidity, volatility, and price efficiency. Next, we identify possible determinants and channels from theoretical and empirical studies that could explain order routing decisions and present the possible directions for future research. Finally, we discuss the major regulatory reforms in the U.S. equity market on routing venue decisions. This topic is relevant in current times when phenomena such as “GameStop Frenzy” have drawn significant attention to commission-free trading venues. Full article
Show Figures

Figure 1

Back to TopTop