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Financial Derivatives: Market Risk, Pricing, and Hedging
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Financial Derivatives: Market Risk, Pricing, and Hedging

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

Deadline for manuscript submissions: 31 January 2025 | Viewed by 2085

Special Issue Editor

Dr. Young Shin Aaron Kim

E-Mail Website
Guest Editor
College of Business, Stony Brook University, Stony Brook, NY 11794-3775, USA
Interests: financial risk management; derivative pricing and hedging; mathematical and statistical modeling with levy process; time varying volatility; asymmetric dependence; fattails and long range dependence

Special Issue Information

Dear Colleagues,

The Special Issue on Financial Derivatives: Market Risk, Pricing, and Hedging aims to explore and demonstrate various aspects of financial derivatives which play a crucial role in modern finance, offering tools for risk management, speculation, and portfolio optimization. This Special Issue collects cutting-edge research that enhances our understanding of financial models, market risk, and pricing and hedging strategies associated with financial derivatives.

Key objectives of this Special Issue include but are not limited to:

  • Examining the latest developments in pricing models for various types of derivatives, including options, futures, swaps, and forwards.
  • Investigating the dynamics of market risk inherent in derivative instruments.
  • Finding efficient methodologies for measuring and managing financial risks.
  • Research an innovative machine learning algorithm to find hedging strategies to reduce risk exposure in portfolios.
  • Analyzing the impact of regulatory changes and technological advancements on the pricing, risk management, and trading of financial derivatives.
  • Fostering interdisciplinary research that integrates insights from finance, mathematics, economics, and computational methods to address complex issues in derivative markets.

Contributions to this Special Issue may include theoretical studies, empirical analyses, methodological advancements, and case studies that shed light on the challenges and opportunities in the realm of financial derivatives. By synthesizing and disseminating state-of-the-art research, this Special Issue aims to provide valuable insights for academics, practitioners, policymakers, and stakeholders involved in derivative markets.

Dr. Young Shin Aaron Kim
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. 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 derivatives
  • derivative instruments
  • pricing of options, futures, swaps, forwards, etc.
  • pricing models
  • derivative markets
  • market risk
  • hedging

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Published Papers (3 papers)

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Research

22 pages, 546 KiB  
Article
A Sequential Importance Sampling for Estimating Multi-Period Tail Risk
by Ye-Ji Seo and Sunggon Kim
Risks 2024, 12(12), 201; https://doi.org/10.3390/risks12120201 - 13 Dec 2024
Viewed by 231
Abstract
Plain or crude Monte Carlo simulation (CMC) is commonly applied for estimating multi-period tail risk measures such as value-at-risk (VaR) and expected shortfall (ES). After fitting a volatility model to the past history of returns and estimating the conditional distribution of innovations, one [...] Read more.
Plain or crude Monte Carlo simulation (CMC) is commonly applied for estimating multi-period tail risk measures such as value-at-risk (VaR) and expected shortfall (ES). After fitting a volatility model to the past history of returns and estimating the conditional distribution of innovations, one can simulate the return process following the fitted volatility model with the estimated conditional distribution of innovations. Repeated generation of the return processes with the desired length gives a sufficient number of simulated multi-period returns. Then, the multi-period VaR and ES are directly estimated from the empirical distribution of them. CMC is easily applicable. However, it needs to generate a huge number of multi-period returns for the accurate estimation of a tail risk measure, especially when the confidence level of the measure is close to 1. To overcome this shortcoming, we propose a sequential importance sampling, which is a modification of CMC. In the proposed method. The sampling distribution of innovations is chosen differently from the estimated conditional distribution of innovations so that the simulated multi-period losses are more severe than in the case of CMC. In other words, the simulated losses over the VaR that is wanted to estimate are common in the proposed method, which reduces very much the estimation error of ES, and requires the less simulated samples. We propose how to find the near optimal sampling distribution. The multi-period VaR and ES are estimated from the weighted empirical distribution of the simulated multi-period returns. We propose how to compute the weight of a simulated multi-period return. An empirical study is given to backtest the estimated VaRs and ESs by the proposed method, and to compare the performance of the proposed sequential importance sampling with CMC. Full article
(This article belongs to the Special Issue Financial Derivatives: Market Risk, Pricing, and Hedging)
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13 pages, 1025 KiB  
Article
Dynamics of Foreign Exchange Futures Trading Volumes in Thailand
by Woradee Jongadsayakul
Risks 2024, 12(9), 147; https://doi.org/10.3390/risks12090147 - 14 Sep 2024
Viewed by 705
Abstract
Following the introduction of EUR/USD futures and USD/JPY futures on 31 October 2022, Thailand Futures Exchange first entered the top 11 list of derivatives exchanges based on foreign exchange derivative volumes in 2022. This paper investigates the dynamics of foreign exchange futures trading [...] Read more.
Following the introduction of EUR/USD futures and USD/JPY futures on 31 October 2022, Thailand Futures Exchange first entered the top 11 list of derivatives exchanges based on foreign exchange derivative volumes in 2022. This paper investigates the dynamics of foreign exchange futures trading volumes in Thailand through the VAR(2) model. Trading volumes of EUR/USD futures, USD/JPY futures, and USD/THB futures are considered over the sample period from 31 October 2022 to 12 January 2024. The empirical results provide no evidence that the trading volume of EUR/USD futures is dependent on the past trading volumes of USD/JPY futures and USD/THB futures. The Granger causality test results show the existence of bidirectional causality between the trading volumes of USD/JPY futures and USD/THB futures. The results of the impulse response function are consistent with the sign results of the VAR(2) model, showing that the USD/JPY futures trading volume has a negative impact on the USD/THB futures trading volume, and vice versa. The analysis of variance decomposition shows that the variability of the USD/JPY futures trading volume and USD/THB futures trading volume, apart from its own shock, is explained by other FX futures trading volume shocks. Therefore, traders should pay more attention to new FX futures trading activity due to its negative impact on the USD/THB futures trading volume and its contribution to the variance in the USD/THB futures trading volume. Understanding the futures trading volume relationship also helps Thailand Futures Exchange develop new products and services that can foster market liquidity and stability. Full article
(This article belongs to the Special Issue Financial Derivatives: Market Risk, Pricing, and Hedging)
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24 pages, 516 KiB  
Article
Dynamic Asset Pricing in a Unified Bachelier–Black–Scholes–Merton Model
by W. Brent Lindquist, Svetlozar T. Rachev, Jagdish Gnawali and Frank J. Fabozzi
Risks 2024, 12(9), 136; https://doi.org/10.3390/risks12090136 - 27 Aug 2024
Viewed by 626
Abstract
We present a unified, market-complete model that integrates both Bachelier and Black–Scholes–Merton frameworks for asset pricing. The model allows for the study, within a unified framework, of asset pricing in a natural world that experiences the possibility of negative security prices or riskless [...] Read more.
We present a unified, market-complete model that integrates both Bachelier and Black–Scholes–Merton frameworks for asset pricing. The model allows for the study, within a unified framework, of asset pricing in a natural world that experiences the possibility of negative security prices or riskless rates. Unlike the classical Black–Scholes–Merton, we show that option pricing in the unified model differs depending on whether the replicating, self-financing portfolio uses riskless bonds or a single riskless bank account. We derive option price formulas and extend our analysis to the term structure of interest rates by deriving the pricing of zero-coupon bonds, forward contracts, and futures contracts. We identify a necessary condition for the unified model to support a perpetual derivative. Discrete binomial pricing under the unified model is also developed. In every scenario analyzed, we show that the unified model simplifies to the standard Black–Scholes–Merton pricing under specific limits and provides pricing in the Bachelier model limit. We note that the Bachelier limit within the unified model allows for positive riskless rates. The unified model prompts us to speculate on the possibility of a mixed multiplicative and additive deflator model for risk-neutral option pricing. Full article
(This article belongs to the Special Issue Financial Derivatives: Market Risk, Pricing, and Hedging)
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