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Risks, Volume 12, Issue 9 (September 2024) – 6 articles

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20 pages, 4025 KiB

Open AccessArticle

A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction

by Farhat Iqbal, Dimitrios Koutmos, Eman A. Ahmed and Lulwah M. Al-Essa

Risks 2024, 12(9), 139; https://doi.org/10.3390/risks12090139 - 30 Aug 2024

Viewed by 143

Abstract

The global foreign exchange (FX) market represents a critical and sizeable component of our financial system. It is a market where firms and investors engage in both speculative trading and hedging. Over the years, there has been a growing interest in FX modeling and prediction. Recently, machine learning (ML) and deep learning (DL) techniques have shown promising results in enhancing predictive accuracy. Motivated by the growing size of the FX market, as well as advancements in ML, we propose a novel forecasting framework, the MVO-BiGRU model, which integrates variational mode decomposition (VMD), data augmentation, Optuna-optimized hyperparameters, and bidirectional GRU algorithms for monthly FX rate forecasting. The data augmentation in the Prevention module significantly increases the variety of data combinations, effectively reducing overfitting issues, while the Optuna optimization ensures optimal model configuration for enhanced performance. Our study’s contributions include the development of the MVO-BiGRU model, as well as the insights gained from its application in FX markets. Our findings demonstrate that the MVO-BiGRU model can successfully avoid overfitting and achieve the highest accuracy in out-of-sample forecasting, while outperforming benchmark models across multiple assessment criteria. These findings offer valuable insights for implementing ML and DL models on low-frequency time series data, where artificial data augmentation can be challenging. Full article

(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)

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16 pages, 391 KiB

Open AccessArticle

The Spatial Analysis of the Role of Green Finance in Carbon Emission Reduction

by Menghan Xiao, Xiaojing Guo, Gonghang Chen, Xiangfeng Ji and Wenqing Sun

Risks 2024, 12(9), 138; https://doi.org/10.3390/risks12090138 - 29 Aug 2024

Viewed by 222

Abstract

Under the “dual carbon” goal, the core issue at present is to improve the environment while ensuring economic development. As a result, green finance, that is a tool that integrates finance and environmental protection, has shown increasingly significant carbon reduction effects. With the panel data of 30 provinces in China from 2012 to 2021 being the research object, this study employs a spatial Durbin model to examine the impact of green finance on carbon emissions and further discusses its mechanism effects. The empirical results indicate the following: firstly, the development of green finance effectively suppresses carbon emissions; secondly, by decomposing the spatial effect of green finance on carbon emissions, it is found that green finance also reduces carbon emissions in neighboring regions due to the spillover effects; finally, green finance can suppress carbon emissions through technological innovation and industrial structure upgrading. Therefore, it is imperative to actively engage in practical work related to green finance, to establish a sound system for green finance, and simultaneously, to enhance cooperation among regions in terms of green finance, in order to fully leverage its role in suppressing carbon emissions. Full article

(This article belongs to the Special Issue Nexus between Green Finance and Green Total Factor Productivity: Principles, Techniques, and Noval Opportunities)

33 pages, 5094 KiB

Open AccessArticle

Claim Prediction and Premium Pricing for Telematics Auto Insurance Data Using Poisson Regression with Lasso Regularisation

by Farha Usman, Jennifer S. K. Chan, Udi E. Makov, Yang Wang and Alice X. D. Dong

Risks 2024, 12(9), 137; https://doi.org/10.3390/risks12090137 - 28 Aug 2024

Viewed by 243

Abstract

We leverage telematics data on driving behavior variables to assess driver risk and predict future insurance claims in a case study utilising a representative telematics sample. In the study, we aim to categorise drivers according to their driving habits and establish premiums that accurately reflect their driving risk. To accomplish our goal, we employ the two-stage Poisson model, the Poisson mixture model, and the Zero-Inflated Poisson model to analyse the telematics data. These models are further enhanced by incorporating regularisation techniques such as lasso, adaptive lasso, elastic net, and adaptive elastic net. Our empirical findings demonstrate that the Poisson mixture model with the adaptive lasso regularisation outperforms other models. Based on predicted claim frequencies and drivers’ risk groups, we introduce a novel usage-based experience rating premium pricing method. This method enables more frequent premium updates based on recent driving behaviour, providing instant rewards and incentivising responsible driving practices. Consequently, it helps to alleviate cross-subsidization among risky drivers and improves the accuracy of loss reserving for auto insurance companies. Full article

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23 pages, 516 KiB

Open AccessFeature PaperArticle

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 270

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 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)

22 pages, 1518 KiB

Open AccessArticle

Using the Fuzzy Version of the Pearl’s Algorithm for Environmental Risk Assessment Tasks

by Oleg Uzhga-Rebrov

Risks 2024, 12(9), 135; https://doi.org/10.3390/risks12090135 - 26 Aug 2024

Viewed by 298

Abstract

In risk assessment, numerous subfactors influence the probabilities of the main factors. These main factors reflect adverse outcomes, which are essential in risk assessment. A Bayesian network can model the entire set of subfactors and their interconnections. To assess the probabilities of all possible states of the main factors (adverse consequences), complete information about the probabilities of all relevant subfactor states in the network nodes must be utilized. This is a typical task of probabilistic inference. The algorithm proposed by J. Pearl is widely used for point estimates of relevant probabilities. However, in many practical problems, including environmental risk assessment, it is not possible to assign crisp probabilities for relevant events due to the lack of sufficient statistical data. In such situations, expert assignment of probabilities is widely used. Uncertainty in expert assessments can be successfully modeled using triangular fuzzy numbers. That is why this article proposes a fuzzy version of this algorithm, which can solve the problem of probabilistic inference on a Bayesian network when the initial probability values are given as triangular fuzzy numbers. Full article

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26 pages, 964 KiB

Open AccessArticle

Financial Risk Management in Healthcare in the Provision of High-Tech Medical Assistance for Sustainable Development: Evidence from Russia

by Abdula M. Chililov

Risks 2024, 12(9), 134; https://doi.org/10.3390/risks12090134 - 26 Aug 2024

Viewed by 256

Abstract

The research determines the level of financial risk in the Russian healthcare system and identifies prospects for improving the current Russian practice of financial risk management in healthcare when providing high-tech medical care for sustainable development (using Russia as an example). The author summarizes the advanced experience of the top 20 largest healthcare organizations in Russia by revenue in 2022. Based on this experience, the author developed an SEM model of the financial risks in healthcare during the provision of high-tech medical care in Russia from a sustainable development perspective. The theoretical significance of the developed model lies in uncovering the previously unknown causal relationships between the implementation of the ICT, sustainable development support, and financial risks in healthcare. The model reveals a new market dimension of financial risks for healthcare organizations in Russia. The main conclusion is that implementing the ICT and support for sustainable development helps to reduce the financial risks in healthcare. The identified potential for reducing financial risks in providing high-tech medical care in Russia until 2026 is practically significant. This prospect can be practically applied as a roadmap for the digital modernization and sustainable development of healthcare until 2026, enhancing the state healthcare policy in Russia. The established systemic relationship between ICT implementation, sustainable development support, and financial risks in healthcare is of managerial importance because it will increase the predictability of the financial risks in the market dimension of healthcare in Russia. The newly developed approach to risk management in healthcare during the provision of high-tech medical care in Russia has expanded the instrumental framework of risk management for healthcare organizations in Russia and revealed further opportunities for improving its efficiency. Full article

(This article belongs to the Special Issue Managing Financial Risks Based on Corporate Social Responsibility for Sustainable Development II)

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