Special Issue "Frontiers in Quantitative Finance"

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editor

Dr. Tihana Škrinjarić
E-Mail Website
Guest Editor
Croatian National Bank, Trg hrvatskih velikana 3, 10000 Zagreb, Croatia
Interests: financial econometrics; portfolio analysis; stock market; developing markets; applied econometrics; performance measurement; quantitative techniques; risk analaysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Quantitative finance faces many challenges today. New models, methods, and techniques are being developed almost daily; data availability is enormous; interdisciplinary research is growing in size; uncertainty in economies and on financial markets is rising; etc. Thus, some classic approaches within financial modeling and portfolio management which are based on unrealistic assumptions are somewhat outdated. The idea of this Special Issue is to promote new models, methods, and other quantitative tools in financial economics, quantitative finance, portfolio management, and financial modeling as a whole. These new approaches extend the existing ones or present a new standpoint within quantitative analysis, but the goals are the same: facilitate the financial decision-making process.

Researchers and practitioners who are working on new ideas, models, and methods within quantitative finance are welcomed to submit papers which focus on the aforementioned topics. The new frontiers in quantitative finance include any quantitative contributions that can help in any aspects of the financial economics.

Dr. Tihana Škrinjarić
Guest Editor

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 1200 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

  • Parametric and nonparametric models 
  • Mathematical models 
  • Financial modeling 
  • Portfolio choice 
  • Volatility 
  • Quantitative tools in finance

Published Papers (9 papers)

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Research

Article
Debt Market Trends and Predictors of Specialization: An Analysis of Pakistani Corporate Sector
J. Risk Financial Manag. 2021, 14(5), 224; https://doi.org/10.3390/jrfm14050224 - 17 May 2021
Viewed by 364
Abstract
Recently, debt structure research has started focusing on the strategic perspective of financing choices, particularly to understand the reasons for debt specialization (DS). This paper examines trends of specialization over time and industry by using a comprehensive dataset on types of debt employed [...] Read more.
Recently, debt structure research has started focusing on the strategic perspective of financing choices, particularly to understand the reasons for debt specialization (DS). This paper examines trends of specialization over time and industry by using a comprehensive dataset on types of debt employed by the public limited companies during 2009–2018. The objective of the current study is to analyze the effect of debt market conditions by identifying significant predictors of DS. Time-series and cross-sectional results confirm the existence of DS, which is further validated by the findings of the cluster analysis. The empirical results indicate that overall, 61% of the companies solely rely on a single type of debt, mostly on short-term obligations accompanied by long-term secured and other debts. Moreover, small, mature, rated, group-affiliated, and low-leverage companies incline more towards this strategy. Credit rating, debt maturity, financial and interest coverage ratios serve as the primary determinants of the debt market that are significantly associated with the measures of DS. The results contribute to the capital structure literature by specifying that financing choice has an important implication in deciding the debt structure composition of the organizations. Full article
(This article belongs to the Special Issue Frontiers in Quantitative Finance)
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Article
Stress Testing and Systemic Risk Measures Using Elliptical Conditional Multivariate Probabilities
J. Risk Financial Manag. 2021, 14(5), 213; https://doi.org/10.3390/jrfm14050213 - 10 May 2021
Viewed by 964
Abstract
Systemic risk, in a complex system with several interrelated variables, such as a financial market, is quantifiable from the multivariate probability distribution describing the reciprocal influence between the system’s variables. The effect of stress on the system is reflected by the change in [...] Read more.
Systemic risk, in a complex system with several interrelated variables, such as a financial market, is quantifiable from the multivariate probability distribution describing the reciprocal influence between the system’s variables. The effect of stress on the system is reflected by the change in such a multivariate probability distribution, conditioned to some of the variables being at a given stress’ amplitude. Therefore, the knowledge of the conditional probability distribution function can provide a full quantification of risk and stress propagation in the system. However, multivariate probabilities are hard to estimate from observations. In this paper, I investigate the vast family of multivariate elliptical distributions, discussing their estimation from data and proposing novel measures for stress impact and systemic risk in systems with many interrelated variables. Specific examples are described for the multivariate Student-t and the multivariate normal distributions applied to financial stress testing. An example of the US equity market illustrates the practical potentials of this approach. Full article
(This article belongs to the Special Issue Frontiers in Quantitative Finance)
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Article
Portfolio Optimization Constrained by Performance Attribution
J. Risk Financial Manag. 2021, 14(5), 201; https://doi.org/10.3390/jrfm14050201 - 02 May 2021
Viewed by 338
Abstract
This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize conditional value-at-risk and investigate two performance attributes, asset allocation (AA) and the selection effect (SE), as constraints on asset weights. The test portfolio consists of [...] Read more.
This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize conditional value-at-risk and investigate two performance attributes, asset allocation (AA) and the selection effect (SE), as constraints on asset weights. The test portfolio consists of stocks from the Dow Jones Industrial Average index. Values for the performance attributes are established relative to two benchmarks, equi-weighted and price-weighted portfolios of the same stocks. Performance of the optimized portfolios is judged using comparisons of cumulative price and the risk-measures: maximum drawdown, Sharpe ratio, Sortino–Satchell ratio and Rachev ratio. The results suggest that achieving SE performance thresholds requires larger turnover values than that required for achieving comparable AA thresholds. The results also suggest a positive role in price and risk-measure performance for the imposition of constraints on AA and SE. Full article
(This article belongs to the Special Issue Frontiers in Quantitative Finance)
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Article
Exploring the Link of Real Options Theory with Dynamic Capabilities Framework in Open Innovation-Type Merger and Acquisition Deals
J. Risk Financial Manag. 2021, 14(4), 168; https://doi.org/10.3390/jrfm14040168 - 08 Apr 2021
Viewed by 526
Abstract
Although it is well established that acquisition-based dynamic capabilities have important consequences for merger and acquisition (M&A) processes, direct evidence on how real option applications can measure a dynamic capability-based synergy in open innovation-type M&A deals has been scarce. This study draws from [...] Read more.
Although it is well established that acquisition-based dynamic capabilities have important consequences for merger and acquisition (M&A) processes, direct evidence on how real option applications can measure a dynamic capability-based synergy in open innovation-type M&A deals has been scarce. This study draws from seminal research on real options theory to explore some of these benefits and limits to value a synergy in one recent highly strategic acquisition. To strengthen the identification of causal effects, the paper develops the proposition that justifies the role of dynamic capabilities as antecedents of the success of open innovation-type M&A deals in the ICT industry and demonstrates real options’ application to measure M&A synergies. To test the internal and external validity of the proposition, the explorative case study on Samsung’s acquisition of Harman International Industries was analyzed and interpreted. This study contributes important empirical evidence to bear on the literature on open innovation theory, dynamic capabilities framework, and real options theory. Full article
(This article belongs to the Special Issue Frontiers in Quantitative Finance)
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Article
Economic Policy Uncertainty and Stock Return Momentum
J. Risk Financial Manag. 2021, 14(4), 141; https://doi.org/10.3390/jrfm14040141 - 24 Mar 2021
Viewed by 522
Abstract
This paper investigates the relationship between economic policy uncertainty (EPU), an index capturing newspaper coverage of policy-related issues, and momentum profits. Momentum remains an unexplained anomaly. Our findings reveal a statistically negative association between EPU and hedge momentum portfolios. The short side portfolio [...] Read more.
This paper investigates the relationship between economic policy uncertainty (EPU), an index capturing newspaper coverage of policy-related issues, and momentum profits. Momentum remains an unexplained anomaly. Our findings reveal a statistically negative association between EPU and hedge momentum portfolios. The short side portfolio dominates this effect as compared to the long side. EPU is statistically significant after controlling for macroeconomic variables. Furthermore, the paper conducts a battery of time series analysis, which highlights that EPU has a causal relationship with the hedge portfolio in the short run. On the other hand, the hedge portfolio has a long-term relationship with EPU, not the other way around. Full article
(This article belongs to the Special Issue Frontiers in Quantitative Finance)
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Article
Impact of Financial Support on Textile Enterprises’ Development
J. Risk Financial Manag. 2021, 14(3), 135; https://doi.org/10.3390/jrfm14030135 - 22 Mar 2021
Viewed by 467
Abstract
The purpose of this study is to determine the mutual influence of financial security on the textile enterprises development level. The proposed methodological approach is based on the formation of an integrated financial security indicator and its regression model. The study is based [...] Read more.
The purpose of this study is to determine the mutual influence of financial security on the textile enterprises development level. The proposed methodological approach is based on the formation of an integrated financial security indicator and its regression model. The study is based on 16 textile enterprises in the European Union. Integral indicators on capital structure, current financing sufficiency and financial efficiency of the investigated enterprises have been defined according to the rapid diagnostics of financial provision of the textile enterprises. The state of financial support for the studied companies’ development has been evaluated. It has been established that the development of textile enterprises depends to a large extent on their financial support as a whole. The change in the development level of companies depends substantially on the change in the integrated indicator of their financial provision. In particular, textile enterprises’ development is significantly affected by the capital structure and the predominance of equity in it, as well as current financing. The financial efficiency factors taken into account do not have a significant impact on the development of textile enterprises. This study proposes a financial security model, developed by partial integrated indicators. It enables visual comparison, collation of the capital structure state, current financing and financial efficiency of the studied enterprises with optimal value. Full article
(This article belongs to the Special Issue Frontiers in Quantitative Finance)
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Article
Machine Learning in Futures Markets
J. Risk Financial Manag. 2021, 14(3), 119; https://doi.org/10.3390/jrfm14030119 - 13 Mar 2021
Viewed by 608
Abstract
In this paper, we demonstrate how a well-established machine learning-based statistical arbitrage strategy can be successfully transferred from equity to futures markets. First, we preprocess futures time series comprised of front months to render them suitable for our returns-based trading framework and compile [...] Read more.
In this paper, we demonstrate how a well-established machine learning-based statistical arbitrage strategy can be successfully transferred from equity to futures markets. First, we preprocess futures time series comprised of front months to render them suitable for our returns-based trading framework and compile a data set comprised of 60 futures covering nearly 10 trading years. Next, we train several machine learning models to predict whether the h-day-ahead return of each future out- or underperforms the corresponding cross-sectional median return. Finally, we enter long/short positions for the top/flop-k futures for a duration of h days and assess the financial performance of the resulting portfolio in an out-of-sample testing period. Thereby, we find the machine learning models to yield statistically significant out-of-sample break-even transaction costs of 6.3 bp—a clear challenge to the semi-strong form of market efficiency. Finally, we discuss sources of profitability and the robustness of our findings. Full article
(This article belongs to the Special Issue Frontiers in Quantitative Finance)
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Article
Cash Flows Discounted Using a Model-Free SDF Extracted under a Yield Curve Prior
J. Risk Financial Manag. 2021, 14(3), 100; https://doi.org/10.3390/jrfm14030100 - 04 Mar 2021
Viewed by 421
Abstract
We developed a model-free Bayesian extraction procedure for the stochastic discount factor under a yield curve prior. Previous methods in the literature directly or indirectly use some particular parametric asset-pricing models such as with long-run risks or habits as the prior. Here, in [...] Read more.
We developed a model-free Bayesian extraction procedure for the stochastic discount factor under a yield curve prior. Previous methods in the literature directly or indirectly use some particular parametric asset-pricing models such as with long-run risks or habits as the prior. Here, in contrast, we used no such model, but rather, we adopted a prior that enforces external information about the historically very low levels of U.S. short- and long-term interest rates. For clarity and simplicity, our data were annual time series. We used the extracted stochastic discount factor to determine the stripped cash flow risk premiums on a panel of industrial profits and consumption. Interestingly, the results align very closely with recent limited information (bounded rationality) models of the term structure of equity risk premiums, although nowhere did we use any theory on the discount factor other than its implied moment restrictions. Full article
(This article belongs to the Special Issue Frontiers in Quantitative Finance)
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Article
Maximum Entropy Evaluation of Asymptotic Hedging Error under a Generalised Jump-Diffusion Model
J. Risk Financial Manag. 2021, 14(3), 97; https://doi.org/10.3390/jrfm14030097 - 28 Feb 2021
Viewed by 399
Abstract
In this paper we propose a maximum entropy estimator for the asymptotic distribution of the hedging error for options. Perfect replication of financial derivatives is not possible, due to market incompleteness and discrete-time hedging. We derive the asymptotic hedging error for options under [...] Read more.
In this paper we propose a maximum entropy estimator for the asymptotic distribution of the hedging error for options. Perfect replication of financial derivatives is not possible, due to market incompleteness and discrete-time hedging. We derive the asymptotic hedging error for options under a generalised jump-diffusion model with kernel bias, which nests a number of very important processes in finance. We then obtain an estimation for the distribution of hedging error by maximising Shannon’s entropy subject to a set of moment constraints, which in turn yields the value-at-risk and expected shortfall of the hedging error. The significance of this approach lies in the fact that the maximum entropy estimator allows us to obtain a consistent estimate of the asymptotic distribution of hedging error, despite the non-normality of the underlying distribution of returns. Full article
(This article belongs to the Special Issue Frontiers in Quantitative Finance)
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