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Risks
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Risk Management in Financial and Commodity Markets
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Risk Management in Financial and Commodity Markets

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: 15 December 2026 | Viewed by 15212

Special Issue Editor

Dr. Alper Gormus

E-Mail Website
Guest Editor
Department of Finance and Economics, E. Craig Wall Sr. College of Business, Coastal Carolina University, Conway, SC 29528, USA
Interests: international financial markets; energy investments; mutual funds
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for papers for an upcoming Special Issue titled “Risk Management in Financial and Commodity Markets”. In the context of increasing volatility, interconnectedness, and complexity within today’s global economy, implications of risk transmission and its management strategies have become more critical than ever. Rapid market shifts driven by geopolitical tensions, technological advancements, regulatory transformations, climate change implications, and evolving ESG standards continuously challenge traditional financial and commodity market models. This Special Issue seeks rigorous theoretical and empirical contributions exploring innovative insights and methodologies regarding risk dynamics in domestic and international markets. Research highlighting practical implications, addressing various market dynamics, and providing actionable insights for academics, policymakers, investors, and practitioners is particularly welcome.

We warmly encourage you to submit your research and help advance our collective understanding of these vital mechanisms in navigating uncertainty.

Warm regards,

Dr. Alper Gormus
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 250 words) can be sent to the Editorial Office for assessment.

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. Risks 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 1800 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

  • forecasting
  • hedging
  • risk management
  • commodity markets
  • financial markets
  • market volatility

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

17 pages, 569 KB  
Article
The Paradox of Cyber Risk Controls: An Empirical Analysis of Readiness and Protection Inefficiencies in Thailand’s Financial Sector
by Artid Sringam and Pongpisit Wuttidittachotti
Risks 2026, 14(1), 20; https://doi.org/10.3390/risks14010020 - 19 Jan 2026
Viewed by 594
Abstract
As Thailand’s financial sector accelerates its digital transformation, cybersecurity has transitioned from a mere technical support function to a strategic imperative that governs operational risk and financial stability. This study empirically examines the efficacy of cyber risk controls and their correlation with perceived [...] Read more.
As Thailand’s financial sector accelerates its digital transformation, cybersecurity has transitioned from a mere technical support function to a strategic imperative that governs operational risk and financial stability. This study empirically examines the efficacy of cyber risk controls and their correlation with perceived organizational readiness. Utilizing a quantitative survey of 53 specialized practitioners (N = 53), we assessed maturity across the six dimensions of the Bank of Thailand’s Cyber Resilience Assessment regulatory framework: Governance, Identification, Protection, Detection, Response, and Third-Party Risk Management. While descriptive statistics indicate high overall maturity (x¯ = 4.19, S.D. = 0.37), multiple regression analysis uncovers a critical “Protection Paradox”. Specifically, the “Protection” dimension exhibits a statistically significant negative impact on readiness (β = −0.432, p = 0.01), suggesting that over-engineered technical controls induce operational friction. In contrast, “Identification” emerged as the primary positive driver of readiness (β = 0.627, p < 0.01), highlighting visibility as a superior strategic lever. Furthermore, a structural disconnect was identified between strategic “Governance” and “Third-Party Risk Management” (r = 0.46), highlighting a “Silo Effect” where board-level policy fails to effectively mitigate supply chain risks. These findings suggest that financial institutions must pivot from volume-based compliance to risk-optimized integration to bridge these strategic and operational gaps. Full article
(This article belongs to the Special Issue Risk Management in Financial and Commodity Markets)
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14 pages, 856 KB  
Article
ESG Risk and Agricultural Commodity Integration
by Alper Gormus, Yoav Wachsman and Elif Gormus
Risks 2026, 14(1), 7; https://doi.org/10.3390/risks14010007 - 4 Jan 2026
Viewed by 649
Abstract
This study investigates how major agricultural commodities interact with diversified U.S. equity funds, sorted by their environmental, social, and governance (ESG) risk exposure. Using daily Morningstar data on 880 U.S. equity mutual funds, we construct portfolios representing high- and low-ESG-risk equities and examine [...] Read more.
This study investigates how major agricultural commodities interact with diversified U.S. equity funds, sorted by their environmental, social, and governance (ESG) risk exposure. Using daily Morningstar data on 880 U.S. equity mutual funds, we construct portfolios representing high- and low-ESG-risk equities and examine their linkages with prices for eight agricultural commodities. Applying Fourier-augmented Toda–Yamamoto VAR and LM-GARCH models that accommodate both abrupt and gradual structural breaks, we document clear heterogeneity across ESG risk segments. Low-ESG-risk portfolios exhibit minimal price and volatility spillovers from agricultural commodities, whereas high-ESG-risk portfolios display strong and often bidirectional transmissions—particularly for coffee, corn, cotton, livestock, and soybeans. These findings highlight ESG risk exposure as a key dimension shaping commodity–equity integration and provide new evidence on how sustainability-related risks influence equity market vulnerability to commodity shocks. Full article
(This article belongs to the Special Issue Risk Management in Financial and Commodity Markets)
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20 pages, 5153 KB  
Article
Forecasting Commodity Prices Using Futures: The Case of Copper
by Gonzalo Cortazar, Mariavictoria Enberg and Hector Ortega
Risks 2026, 14(1), 2; https://doi.org/10.3390/risks14010002 - 24 Dec 2025
Viewed by 1963
Abstract
This paper analyzes three forecasting methods for commodity spot prices and applies them to copper prices. The first method uses futures prices from either LME or COMEX. The second method uses analysts’ consensus expectations, reported by Bloomberg. The third method jointly uses futures [...] Read more.
This paper analyzes three forecasting methods for commodity spot prices and applies them to copper prices. The first method uses futures prices from either LME or COMEX. The second method uses analysts’ consensus expectations, reported by Bloomberg. The third method jointly uses futures and analysts’ expectations as inputs to a multifactor stochastic pricing model, with time-varying risk premiums that smooth its data using the Kalman filter. All three alternatives are compared with the well-known no-change forecast benchmark and with each other. The main finding is that analysts’ expectations are a valuable source of data for forecasting copper prices. Also, when futures prices are relatively higher than spot prices, the model presented is the best alternative for forecasting copper prices at any horizon up to 24 months, and when prices are relatively lower than spot prices, the model is the best alternative for long-term forecasts and for LME futures prices for 1 to 12 months. Full article
(This article belongs to the Special Issue Risk Management in Financial and Commodity Markets)
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16 pages, 1252 KB  
Article
HAR-RV-CARMA: A Kalman Filter-Weighted Hybrid Model for Enhanced Volatility Forecasting
by Chigozie Andy Ngwaba
Risks 2025, 13(11), 223; https://doi.org/10.3390/risks13110223 - 6 Nov 2025
Cited by 1 | Viewed by 2382
Abstract
This paper introduces a new hybrid model, HAR-RV-CARMA, which combines the Heterogeneous Autoregressive model for Realized Volatility (HAR-RV) with the Continuous Autoregressive Moving Average (CARMA) model. The key innovation of this study lies in the use of a Kalman filter-based dynamic state weighting [...] Read more.
This paper introduces a new hybrid model, HAR-RV-CARMA, which combines the Heterogeneous Autoregressive model for Realized Volatility (HAR-RV) with the Continuous Autoregressive Moving Average (CARMA) model. The key innovation of this study lies in the use of a Kalman filter-based dynamic state weighting mechanism to optimally combine the predictive capabilities of both models while mitigating overfitting. The proposed model is applied to five major Covered Call Exchange-Traded Funds (ETFs), QYLD, XYLD, RYLD, JEPI, and JEPQ, utilizing daily realized volatility data from 2019 to 2024. Model performance is evaluated against standalone HAR-RV and CARMA models using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Quasi-Likelihood (QLIKE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Additionally, the study assesses directional accuracy and conducts a Diebold-Mariano test to compare forecast performance against the standalone models statistically. Empirical results suggest that the HAR-RV-CARMA hybrid model significantly outperforms both HAR-RV and CARMA in volatility forecasting across all evaluation criteria. It achieves lower forecast errors, superior goodness-of-fit, and higher directional accuracy, with Diebold-Mariano test outcomes rejecting the null hypothesis of equal predictive ability at significant levels. These findings highlight the effectiveness of dynamic model weighting in improving predictive accuracy and offer a strong framework for volatility modeling in financial markets. Full article
(This article belongs to the Special Issue Risk Management in Financial and Commodity Markets)
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16 pages, 1792 KB  
Article
The Russia–Ukraine Conflict and Stock Markets: Risk and Spillovers
by Maria Leone, Alberto Manelli and Roberta Pace
Risks 2025, 13(7), 130; https://doi.org/10.3390/risks13070130 - 4 Jul 2025
Viewed by 8908
Abstract
Globalization and the spread of technological innovations have made world markets and economies increasingly unified and conditioned by international trade, not only for sales markets but above all for the supply of raw materials necessary for the functioning of the production complex of [...] Read more.
Globalization and the spread of technological innovations have made world markets and economies increasingly unified and conditioned by international trade, not only for sales markets but above all for the supply of raw materials necessary for the functioning of the production complex of each country. Alongside oil and gold, the main commodities traded include industrial metals, such as aluminum and copper, mineral products such as gas, electrical and electronic components, agricultural products, and precious metals. The conflict between Russia and Ukraine tested the unification of markets, given that these are countries with notable raw materials and are strongly dedicated to exports. This suggests that commodity prices were able to influence the stock markets, especially in the countries most closely linked to the two belligerents in terms of import-export. Given the importance of industrial metals in this period of energy transition, the aim of our study is to analyze whether Industrial Metals volatility affects G7 stock markets. To this end, the BEKK-GARCH model is used. The sample period spans from 3 January 2018 to 17 September 2024. The results show that lagged shocks and volatility significantly and positively influence the current conditional volatility of commodity and stock returns during all periods. In fact, past shocks inversely influence the current volatility of stock indices in periods when external events disrupt financial markets. The results show a non-linear and positive impact of commodity volatility on the implied volatility of the stock markets. The findings suggest that the war significantly affected stock prices and exacerbated volatility, so investors should diversify their portfolios to maximize returns and reduce risk differently in times of crisis, and a lack of diversification of raw materials is a risky factor for investors. Full article
(This article belongs to the Special Issue Risk Management in Financial and Commodity Markets)
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