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Risks
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Volatility Modeling in Financial Market
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Volatility Modeling in Financial Market

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 6154

Special Issue Editors

Dr. Katarzyna Czech

E-Mail Website
Guest Editor
Institute of Economics and Finance, Warsaw University of Life Sciences, 02-787 Warsaw, Poland
Interests: financial economics; econometrics; international economics; applied mathematics; qualitative and multi-method research
Dr. Michal Wielechowski

E-Mail Website
Guest Editor
Department of Economics and Economic Policy, Institute of Economics and Finance, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warsaw, Poland
Interests: financial markets; stock market

Special Issue Information

Dear Colleagues,

In the dynamic turbulent world of financial markets, particularly in crisis situations, understanding and predicting volatility is crucial for investors, risk managers, and policymakers. This Special Issue, entitled “Volatility Modeling in Financial Markets”, aims to explore the latest advancements and methodologies in this vital field.

This Special Issue invites original research and comprehensive studies that delve into new models and approaches for forecasting market volatility, assessing risks, and understanding the implications of volatility in various financial instruments as well as markets. We encourage submissions that focus on, but are not limited to, dynamic models, financial econometrics, and the impact of macroeconomic factors on market volatility.

Contributions may also include empirical studies on the effectiveness of volatility models in real-world scenarios, advancements in computational techniques for volatility forecasting, and insights into how market volatility affects financial decision making and risk management strategies. This Special Issue seeks to provide a platform for researchers to share their insights and findings, fostering a deeper understanding of financial market dynamics in this turbulent time.

Dr. Katarzyna Czech
Dr. Michal Wielechowski
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. 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

  • financial market dynamics
  • market volatility forecasting
  • financial econometrics
  • turbulent times in financial markets
  • risk management strategies

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Published Papers (3 papers)

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Research

33 pages, 9119 KiB  
Article
Credit Risk Prediction Using Machine Learning and Deep Learning: A Study on Credit Card Customers
by Victor Chang, Sharuga Sivakulasingam, Hai Wang, Siu Tung Wong, Meghana Ashok Ganatra and Jiabin Luo
Risks 2024, 12(11), 174; https://doi.org/10.3390/risks12110174 - 4 Nov 2024
Viewed by 3018
Abstract
The increasing population and emerging business opportunities have led to a rise in consumer spending. Consequently, global credit card companies, including banks and financial institutions, face the challenge of managing the associated credit risks. It is crucial for these institutions to accurately classify [...] Read more.
The increasing population and emerging business opportunities have led to a rise in consumer spending. Consequently, global credit card companies, including banks and financial institutions, face the challenge of managing the associated credit risks. It is crucial for these institutions to accurately classify credit card customers as “good” or “bad” to minimize capital loss. This research investigates the approaches for predicting the default status of credit card customer via the application of various machine-learning models, including neural networks, logistic regression, AdaBoost, XGBoost, and LightGBM. Performance metrics such as accuracy, precision, recall, F1 score, ROC, and MCC for all these models are employed to compare the efficiency of the algorithms. The results indicate that XGBoost outperforms other models, achieving an accuracy of 99.4%. The outcomes from this study suggest that effective credit risk analysis would aid in informed lending decisions, and the application of machine-learning and deep-learning algorithms has significantly improved predictive accuracy in this domain. Full article
(This article belongs to the Special Issue Volatility Modeling in Financial Market)
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15 pages, 468 KiB  
Article
Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction
by Johanna M. Orozco-Castañeda, Sebastián Alzate-Vargas and Danilo Bedoya-Valencia
Risks 2024, 12(10), 156; https://doi.org/10.3390/risks12100156 - 30 Sep 2024
Cited by 1 | Viewed by 938
Abstract
This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the [...] Read more.
This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns. Full article
(This article belongs to the Special Issue Volatility Modeling in Financial Market)
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21 pages, 2172 KiB  
Article
Foreign Exchange Futures Trading and Spot Market Volatility in Thailand
by Woradee Jongadsayakul
Risks 2024, 12(7), 107; https://doi.org/10.3390/risks12070107 - 26 Jun 2024
Viewed by 1514
Abstract
This paper investigates how the introduction of foreign exchange futures has an impact on spot volatility and considers the contemporaneous and dynamic relationship between spot volatility and foreign exchange futures trading activity, including trading volume and open interest in the Thailand Futures Exchange [...] Read more.
This paper investigates how the introduction of foreign exchange futures has an impact on spot volatility and considers the contemporaneous and dynamic relationship between spot volatility and foreign exchange futures trading activity, including trading volume and open interest in the Thailand Futures Exchange context, with the examples of the EUR/USD futures and USD/JPY futures. The results of the EGARCH (1,1) model show that the introduction of foreign exchange futures decreases spot volatility. It also increases the rate at which new information is impounded into spot prices but decreases the persistency of volatility shocks. A positive effect of unexpected trading volume and a negative effect of unexpected open interest on contemporaneous spot volatility are in line with the VAR(1) model results of the dynamic relationship between spot volatility and foreign exchange futures trading activity. With the impact on spot volatility caused by unexpected open interest rate being stronger than by unexpected trading volume, foreign exchange futures trading stabilizes spot volatility. Full article
(This article belongs to the Special Issue Volatility Modeling in Financial Market)
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