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Financial Innovations and Derivatives
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Financial Innovations and Derivatives

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1958

Special Issue Editor

Dr. Leo H. Chan

E-Mail Website
Guest Editor
Department of Finance and Economics, Woodbury School of Business, Utah Valley University, Orem, UT 84058, USA
Interests: investments; financial markets and institutions; options and futures; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There has been an explosion of new financial products over the last two and half decades. Some of these products were created to improve access to different investment choices, while many others were created for the purpose of speculation disguised in the form of risk transfer tools. Much research has been conducted on credit default swaps (CDSs) and collateralized debt obligations (CDOs), as they were responsible for the 2008 global financial crisis. Two classes of financial products that are more widely accessible have garnered far less attention: target date funds (TDFs) and leveraged exchange traded notes (ETNs). While TDFs are not derivatives in the strictest sense, they are mostly composed of other mutual funds. The buying and selling of TDFs could have major impacts on underlying mutual funds. Leveraged ETNs are short-term speculation vehicles that allow investors access to derivatives (futures, options, and swaps) without having to directly own the underlying derivatives. Most investors, however, are unaware of the risks associated with leveraged ETNs. Therefore, the purpose of this Special Issue is to collect empirical works related to the risk and return of TDFs, leveraged ETNs, and their associated derivative products. We welcome the submission of empirical work related to the investment and risk management performance of the following:

  • Specialty ETFs (any ETFs not tied to commonly known indices);
  • Exchange trade notes (ETNs);
  • Leveraged ETNs;
  • Target date funds;
  • Robot advisor/robot-managed funds.

We will also consider high-quality papers related to the utilization of AI/ML in investment and risk management.

Dr. Leo H. Chan
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

  • specialty ETFs (any ETFs not tied to commonly known indices)
  • exchange trade notes (ETNs)
  • leveraged ETNs
  • target date funds
  • robot advisor/robot-managed funds

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Published Papers (3 papers)

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Research

32 pages, 12006 KiB  
Article
Hedging via Perpetual Derivatives: Trinomial Option Pricing and Implied Parameter Surface Analysis
by Jagdish Gnawali, W. Brent Lindquist and Svetlozar T. Rachev
J. Risk Financial Manag. 2025, 18(4), 192; https://doi.org/10.3390/jrfm18040192 - 2 Apr 2025
Viewed by 268
Abstract
We introduce a fairly general, recombining trinomial tree model in the natural world. Market completeness is ensured by considering a market consisting of two risky assets, a riskless asset and a European option. The two risky assets consist of a stock and a [...] Read more.
We introduce a fairly general, recombining trinomial tree model in the natural world. Market completeness is ensured by considering a market consisting of two risky assets, a riskless asset and a European option. The two risky assets consist of a stock and a perpetual derivative of that stock. The option has the stock and its derivative as its underlying. Using a replicating portfolio, we develop prices for European options and generate the unique relationships between the risk-neutral and real-world parameters of the model. We discuss calibration of the model to empirical data in the cases in which the risky asset returns are treated as either arithmetic or logarithmic. From historical price and call option data for select large cap stocks, we develop implied parameter surfaces for the real-world parameters in the model. Full article
(This article belongs to the Special Issue Financial Innovations and Derivatives)
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25 pages, 2138 KiB  
Article
Optimizing Portfolios with Pakistan-Exposed Exchange-Traded Funds: Risk and Performance Insight
by Ali Jaffri, Abootaleb Shirvani, Ayush Jha, Svetlozar T. Rachev and Frank J. Fabozzi
J. Risk Financial Manag. 2025, 18(3), 158; https://doi.org/10.3390/jrfm18030158 - 17 Mar 2025
Viewed by 328
Abstract
This study examines the investment landscape of Pakistan as an emerging and frontier market, focusing on implications for international investors, particularly those in the United States, through exchange-traded funds (ETFs) with exposure to Pakistan. The analysis encompasses 30 ETFs with varying degrees of [...] Read more.
This study examines the investment landscape of Pakistan as an emerging and frontier market, focusing on implications for international investors, particularly those in the United States, through exchange-traded funds (ETFs) with exposure to Pakistan. The analysis encompasses 30 ETFs with varying degrees of exposure to Pakistan, covering the period from 1 January 2016 to February 2024. This research highlights the potential benefits and risks associated with investing in these ETFs, emphasizing the importance of thorough risk assessments and portfolio performance comparisons. By providing descriptive statistics and performance metrics based on historical optimization, this paper aims to equip investors with the necessary insights to make informed decisions when optimizing their portfolios with Pakistan-exposed ETFs. The second part of the paper introduces and assesses dynamic optimization methodologies. This section is designed to explore the adaptability and performance metrics of dynamic optimization techniques in comparison with conventional historical optimization methods. By integrating dynamic optimization into the investigation, this research aims to offer insights into the efficacy of these contrasting methodologies in the context of Pakistan-exposed ETFs. The findings underscore the significance of Pakistan’s market dynamics within the broader context of emerging markets, offering a pathway for diversification and potential growth in investment strategies. Full article
(This article belongs to the Special Issue Financial Innovations and Derivatives)
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19 pages, 2183 KiB  
Article
In-Season Price Forecasting in Cotton Futures Markets Using ARIMA, Neural Network, and LSTM Machine Learning Models
by Jeffrey Vitale and John Robinson
J. Risk Financial Manag. 2025, 18(2), 93; https://doi.org/10.3390/jrfm18020093 - 10 Feb 2025
Viewed by 1000
Abstract
This study explores the efficacy of advanced machine learning models, including various Long Short-Term Memory (LSTM) architectures and traditional time series approaches, for forecasting cotton futures prices. This analysis is motivated by the importance of accurate price forecasting to aid U.S. cotton producers [...] Read more.
This study explores the efficacy of advanced machine learning models, including various Long Short-Term Memory (LSTM) architectures and traditional time series approaches, for forecasting cotton futures prices. This analysis is motivated by the importance of accurate price forecasting to aid U.S. cotton producers in hedging and marketing decisions, particularly in the Texas Gulf region. The models evaluated included ARIMA, basic feedforward neural networks, basic LSTM, bidirectional LSTM, stacked LSTM, CNN LSTM, and 2D convolutional LSTM models. The forecasts were generated for five-, ten-, and fifteen-day periods using historical data spanning 2009 to 2023. The results demonstrated that advanced LSTM architectures outperformed other models across all forecast horizons, particularly during periods of significant price volatility, due to their enhanced ability to capture complex temporal and spatial dependencies. The findings suggest that incorporating advanced LSTM architectures can significantly improve forecasting accuracy, providing a robust tool for producers and market analysts to better navigate price risks. Future research could explore integrating additional contextual variables to enhance model performance further. Full article
(This article belongs to the Special Issue Financial Innovations and Derivatives)
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