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Risk Management and Forecasting Methods in Finance
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Risk Management and Forecasting Methods in Finance

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

Deadline for manuscript submissions: closed (1 April 2023) | Viewed by 9058

Special Issue Editors

Prof. Dr. Alexandru M. Badescu

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: financial econometrics; mathematical finance; actuarial science
Prof. Dr. Maciej Augustyniak

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, University of Montreal, Montreal, QC H3C 3J7, Canada
Interests: econometrics and computational statistics; quantitative finance; actuarial science

Special Issue Information

Dear Colleagues,

The Special Issue on “Risk Management and Forecasting Methods in Finance” aims to bring together novel articles on the modeling, pricing, and hedging of financial risks.

Data science has revolutionized the financial industry in recent years. Although new methods have enabled better risk management practices and more accurate forecasts, financial theory is constantly evolving to address new challenges brought forward by the recent financial crises. This Special Issue welcomes both theoretical and applied research papers which focus on topics including asset pricing and forecasting, volatility modeling, high-frequency data, portfolio optimization, and derivative valuation. Papers on the application of machine learning techniques to forecasting and risk management are particularly welcome. We also encourage submissions of works that study the pricing and hedging of financial products in the presence of basis risk, transactions costs, or model uncertainty.

Prof. Dr. Alexandru M. Badescu
Prof. Dr. Maciej Augustyniak
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

  • derivative pricing
  • risk management
  • machine learning methods
  • volatility modeling and forecasting
  • high-frequency data
  • model uncertainty

Published Papers (4 papers)

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Research

22 pages, 691 KiB  
Article
Modeling Risk for CVaR-Based Decisions in Risk Aggregation
by Yuriy Zinchenko and Alexandru V. Asimit
J. Risk Financial Manag. 2023, 16(5), 266; https://doi.org/10.3390/jrfm16050266 - 9 May 2023
Cited by 1 | Viewed by 1502
Abstract
Measuring the risk aggregation is an important exercise for any risk bearing carrier. It is not restricted to evaluation of the known portfolio risk position only, and could include complying with regulatory requirements, diversification, etc. The main difficulty of risk aggregation is creating [...] Read more.
Measuring the risk aggregation is an important exercise for any risk bearing carrier. It is not restricted to evaluation of the known portfolio risk position only, and could include complying with regulatory requirements, diversification, etc. The main difficulty of risk aggregation is creating an underlying robust probabilistic model. It is an irrefutable fact that the uncertainty in the individual risks is much lower in its complexity, as compared to modeling the dependence amongst the risks. As a result, it is often reasonable to assume that individual risks are modeled in a robust fashion, while the exact dependence remains unknown, yet some of its traits may be made available due to empirical evidence or “good practice”. Our main contribution is to propose a numerical procedure that enables the identification of the worst possible dependence scenario, when the risk preferences are modeled by the conditional value-at-risk in the presence of dependence uncertainty. For portfolios with two risks, it is known that CVaR ordering coincides with the lower-orthant stochastic ordering of the underlying bivariate distributions. As a by-product of our analysis, we show that no such extensions are possible to higher dimensions. Full article
(This article belongs to the Special Issue Risk Management and Forecasting Methods in Finance)
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18 pages, 1156 KiB  
Article
On the Measurement of Hedging Effectiveness for Long-Term Investment Guarantees
by Maciej Augustyniak, Alexandru Badescu and Mathieu Boudreault
J. Risk Financial Manag. 2023, 16(2), 112; https://doi.org/10.3390/jrfm16020112 - 10 Feb 2023
Viewed by 2149
Abstract
Although the finance literature has devoted a lot of research into the development of advanced models for improving the pricing and hedging performance, there has been much less emphasis on approaches to measure dynamic hedging effectiveness. This article discusses a statistical framework based [...] Read more.
Although the finance literature has devoted a lot of research into the development of advanced models for improving the pricing and hedging performance, there has been much less emphasis on approaches to measure dynamic hedging effectiveness. This article discusses a statistical framework based on regression analysis to measure the effectiveness of dynamic hedges for long-term investment guarantees. The importance of taking model risk into account is emphasized. The difficulties in reducing hedging risk to an appropriately low level lead us to propose a new perspective on hedging, and recognize it as a tool to modify the risk–reward relationship of the unhedged position. Full article
(This article belongs to the Special Issue Risk Management and Forecasting Methods in Finance)
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32 pages, 1881 KiB  
Article
A Fourier Interpolation Method for Numerical Solution of FBSDEs: Global Convergence, Stability, and Higher Order Discretizations
by Polynice Oyono Ngou and Cody Hyndman
J. Risk Financial Manag. 2022, 15(9), 388; https://doi.org/10.3390/jrfm15090388 - 31 Aug 2022
Cited by 2 | Viewed by 1640
Abstract
The convolution method for the numerical solution of forward-backward stochastic differential equations (FBSDEs) was originally formulated using Euler time discretizations and a uniform space grid. In this paper, we utilize a tree-like spatial discretization that approximates the BSDE on the tree, so that [...] Read more.
The convolution method for the numerical solution of forward-backward stochastic differential equations (FBSDEs) was originally formulated using Euler time discretizations and a uniform space grid. In this paper, we utilize a tree-like spatial discretization that approximates the BSDE on the tree, so that no spatial interpolation procedure is necessary. In addition to suppressing extrapolation error, leading to a globally convergent numerical solution for the FBSDE, we provide explicit convergence rates. On this alternative grid the conditional expectations involved in the time discretization of the BSDE are computed using Fourier analysis and the fast Fourier transform (FFT) algorithm. The method is then extended to higher-order time discretizations of FBSDEs. Numerical results demonstrating convergence are presented using a commodity price model, incorporating seasonality, and forward prices. Full article
(This article belongs to the Special Issue Risk Management and Forecasting Methods in Finance)
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15 pages, 455 KiB  
Article
Best-Arm Identification Using Extreme Value Theory Estimates of the CVaR
by Dylan Troop, Frédéric Godin and Jia Yuan Yu
J. Risk Financial Manag. 2022, 15(4), 172; https://doi.org/10.3390/jrfm15040172 - 8 Apr 2022
Viewed by 2348
Abstract
We consider a risk-aware multi-armed bandit framework with the goal of avoiding catastrophic risk. Such a framework has multiple applications in financial risk management. We introduce a new conditional value-at-risk (CVaR) estimation procedure combining extreme value theory with automated threshold selection by ordered [...] Read more.
We consider a risk-aware multi-armed bandit framework with the goal of avoiding catastrophic risk. Such a framework has multiple applications in financial risk management. We introduce a new conditional value-at-risk (CVaR) estimation procedure combining extreme value theory with automated threshold selection by ordered goodness-of-fit tests, and we apply this procedure to a pure exploration best-arm identification problem under a fixed budget. We empirically compare our results with the commonly used sample average estimator of the CVaR, and we show a significant performance improvement when the underlying arm distributions are heavy-tailed. Full article
(This article belongs to the Special Issue Risk Management and Forecasting Methods in Finance)
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