Submit to JRFM Review for JRFM Propose a Special Issue

Journal Menu

Journal Browser

Frontiers in Quantitative Finance

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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: closed (31 December 2021) | Viewed by 34670

Share This Special Issue

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, Collections and Topics 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approaches within financial modeling and portfolio management which are based on unrealistic assumptions are somewhat outdated.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 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

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

Published Papers (12 papers)

Download All Papers

Research

13 pages, 932 KiB

Open AccessArticle

Supervision of Banking Networks Using the Multivariate Threshold-Minimum Dominating Set (mT-MDS)

by Periklis Gogas, Theophilos Papadimitriou and Maria-Artemis Matthaiou

J. Risk Financial Manag. 2022, 15(6), 253; https://doi.org/10.3390/jrfm15060253 - 06 Jun 2022

Cited by 1 | Viewed by 1816

Abstract

The global financial crisis of 2008, triggered by the collapse of Lehman Brothers, highlighted a banking system that was widely exposed to systemic risk. The minimization of the systemic risk via a close and detailed monitoring of the entire banking network became a priority. This is a complex and demanding task considering the size of the banking systems; in the US and the EU they include more than 10,000 institutions. In this paper, we introduce a methodology which identifies a subset of banks that can: (a) efficiently represent the behavior of the whole banking system, and (b), provide, in the case of a failure, a plausible range of the crisis dispersion. The proposed methodology can be used by the regulators as an auxiliary monitoring tool to identify groups of banks that are potentially in distress and try to swiftly remedy their problems and minimize the propagation of the crisis by restricting contagion. This methodology is based on graph theory, and more specifically, complex networks. We termed this setting a “multivariate Threshold–Minimum Dominating Set” (mT-MDS), and it is an extension of the Threshold–Minimum Dominating Set methodology. The method was tested on a dataset of 570 U.S. banks, including 429 solvent ones and 141 failed ones. The variables used to create the networks were as follows: the total interest expense; the total interest income; the tier 1 (core) risk-based capital; and the total assets. The empirical results reveal that the proposed methodology can be successfully employed as an auxiliary tool for the efficient supervision of a large banking network. Full article

(This article belongs to the Special Issue Frontiers in Quantitative Finance)

► Show Figures

Figure 1

15 pages, 1089 KiB

Open AccessArticle

How Fast Does the Clock of Finance Run?—A Time-Definition Enforcing Stationarity and Quantifying Overnight Duration

by Michele Caraglio, Fulvio Baldovin and Attilio L. Stella

J. Risk Financial Manag. 2021, 14(8), 384; https://doi.org/10.3390/jrfm14080384 - 17 Aug 2021

Cited by 1 | Viewed by 1580

Abstract

A definition of time based on the assumption of scale invariance may enhance and simplify the analysis of historical series with cyclically recurrent patterns and seasonalities. By enforcing simple-scaling and stationarity of the distributions of returns, we identify a successful protocol of time definition in finance, functional from tens of minutes to a few days. Within this time definition, the significant reduction of cyclostationary effects allows analyzing the structure of the stochastic process underlying the series on the basis of statistical sampling sliding along the whole time series. At the same time, the duration of periods in which markets remain inactive is properly quantified by the novel clock, and the corresponding returns (e.g., overnight or weekend) can be consistently taken into account for financial applications. The method is applied to the S&P500 index recorded at a 1 min frequency between September 1985 and June 2013. Full article

(This article belongs to the Special Issue Frontiers in Quantitative Finance)

► Show Figures

Figure 1

12 pages, 1851 KiB

Open AccessArticle

Transfer Entropy Approach for Portfolio Optimization: An Empirical Approach for CESEE Markets

by Tihana Škrinjarić, Derick Quintino and Paulo Ferreira

J. Risk Financial Manag. 2021, 14(8), 369; https://doi.org/10.3390/jrfm14080369 - 12 Aug 2021

Cited by 6 | Viewed by 2410

Abstract

In this paper, we deal with the possibility of using econophysics concepts in dynamic portfolio optimization. The main idea of the research is that combining different methodological aspects in portfolio selection can enhance portfolio performance over time. Using data on CESEE stock market indices, we model the dynamics of entropy transfers from one return series to others. In the second step, the results are utilized in simulating the portfolio strategies that take into account the previous results. Here, the main results indicate that using entropy transfers in portfolio construction and rebalancing has the potential to achieve better portfolio value over time when compared to benchmark strategies. Full article

(This article belongs to the Special Issue Frontiers in Quantitative Finance)

► Show Figures

Figure 1

16 pages, 464 KiB

Open AccessArticle

Debt Market Trends and Predictors of Specialization: An Analysis of Pakistani Corporate Sector

by Kanwal Iqbal Khan, Faisal Qadeer, Mário Nuno Mata, Rui Miguel Dantas, João Xavier Rita and Jéssica Nunes Martins

J. Risk Financial Manag. 2021, 14(5), 224; https://doi.org/10.3390/jrfm14050224 - 17 May 2021

Cited by 5 | Viewed by 2856

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 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)

► Show Figures

Figure 1

17 pages, 663 KiB

Open AccessArticle

Stress Testing and Systemic Risk Measures Using Elliptical Conditional Multivariate Probabilities

by Tomaso Aste

J. Risk Financial Manag. 2021, 14(5), 213; https://doi.org/10.3390/jrfm14050213 - 10 May 2021

Cited by 1 | Viewed by 2656

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 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)

► Show Figures

Figure 1

12 pages, 1082 KiB

Open AccessArticle

Portfolio Optimization Constrained by Performance Attribution

by Yuan Hu, W. Brent Lindquist and Svetlozar T. Rachev

J. Risk Financial Manag. 2021, 14(5), 201; https://doi.org/10.3390/jrfm14050201 - 02 May 2021

Cited by 4 | Viewed by 2196

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 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)

► Show Figures

Figure 1

16 pages, 639 KiB

Open AccessArticle

Exploring the Link of Real Options Theory with Dynamic Capabilities Framework in Open Innovation-Type Merger and Acquisition Deals

by Andrejs Čirjevskis

J. Risk Financial Manag. 2021, 14(4), 168; https://doi.org/10.3390/jrfm14040168 - 08 Apr 2021

Cited by 10 | Viewed by 3891

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 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)

► Show Figures

Figure 1

17 pages, 1196 KiB

Open AccessArticle

Economic Policy Uncertainty and Stock Return Momentum

by Garima Goel, Saumya Ranjan Dash, Mário Nuno Mata, António Bento Caleiro, João Xavier Rita and José António Filipe

J. Risk Financial Manag. 2021, 14(4), 141; https://doi.org/10.3390/jrfm14040141 - 24 Mar 2021

Cited by 1 | Viewed by 2569

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 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)

► Show Figures

Figure 1

19 pages, 1331 KiB

Open AccessArticle

Impact of Financial Support on Textile Enterprises’ Development

by Kateryna Boichenko, Mário Nuno Mata, Pedro Neves Mata and Jéssica Nunes Martins

J. Risk Financial Manag. 2021, 14(3), 135; https://doi.org/10.3390/jrfm14030135 - 22 Mar 2021

Cited by 5 | Viewed by 3226

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 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)

► Show Figures

Figure 1

14 pages, 650 KiB

Open AccessArticle

Machine Learning in Futures Markets

by Fabian Waldow, Matthias Schnaubelt, Christopher Krauss and Thomas Günter Fischer

J. Risk Financial Manag. 2021, 14(3), 119; https://doi.org/10.3390/jrfm14030119 - 13 Mar 2021

Cited by 4 | Viewed by 5515

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 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)

► Show Figures

Figure 1

15 pages, 491 KiB

Open AccessArticle

Cash Flows Discounted Using a Model-Free SDF Extracted under a Yield Curve Prior

by A. Ronald Gallant and George Tauchen

J. Risk Financial Manag. 2021, 14(3), 100; https://doi.org/10.3390/jrfm14030100 - 04 Mar 2021

Viewed by 1836

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 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)

► Show Figures

Figure 1

19 pages, 1352 KiB

Open AccessArticle

Maximum Entropy Evaluation of Asymptotic Hedging Error under a Generalised Jump-Diffusion Model

by Farzad Alavi Fard, Firmin Doko Tchatoka and Sivagowry Sriananthakumar

J. Risk Financial Manag. 2021, 14(3), 97; https://doi.org/10.3390/jrfm14030097 - 28 Feb 2021

Cited by 1 | Viewed by 1806

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 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)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Frontiers in Quantitative Finance

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (12 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI