Fuzzy Approaches to Option Price Modeling

Muzzioli, Silvia; Baets, Bernard De

doi:10.1109/tfuzz.2016.2574906

Cited by 44 publications

(29 citation statements)

References 60 publications

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“…The model of fuzzy programming is used to specify the portfolio, which meets the investors´ requirements. Incorporation of fuzzy approaches to option price modelling is explained in the studies of Munoz et al (2013) and Muzzioli and De Beats (2017). The analysis of investors´ expectations of financial development is depicted in the study of Barbera et al (2008).…”

Section: Literature Reviewmentioning

confidence: 99%

Behavioural Aspects of the Financial Decision-Making

2019

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Background and Purpose: Behavioural finance is a relatively new, but rapidly evolving field that provides explanations of an economic decision-making by cognitive psychology, conventional economic and financial theory. Behavioural finance searches the influence of psychology on the behaviour of financial practitioners and the subsequent effects on the financial markets. The purpose of the paper is the research on behavioural aspects of financial decision-making as they help explain why and how markets might be inefficient. Design/Methodology/Approach: Fuzzy logic is an excellent tool for working with linguistic variables that are often found when working with behavioural data. Thus, we analyse the financial decision-making process from the perspective of behavioural finance aimed at better understanding of the decision-making process of investors applying the principles of fuzzy logic to solve various financial problems. Results: The results of the study indicate that fuzzy logic is applicable when solving problems of financial management and financial decision-making problems. The urgency of the fuzzy logic application for managerial and financial decisions should be emphasized. Research in this area indicates that in some cases, as in the case of behavioural financing, the use of fuzzy logic is far more suitable than the use of other methods (Peters, Aguiar and Sales). Conclusion: The novelty of the paper is to extend the application of fuzzy sets in the area of financial decision-making. The paper demonstrates that despite the fact, that fuzzy logic is currently used mainly in technical directions, it is applicable also in financial management, especially, in cases where it is necessary to consider the influence of human and the occurrence of linguistic variables.

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Section: Literature Reviewmentioning

confidence: 99%

Behavioural Aspects of the Financial Decision-Making

2019

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“…Under long-term uncertainty, the ability to defer reinforcement is more valuable than that under deterministic scenarios [40]. Because the defer option can provide planning flexibility in response to various future conditions [41]. Since bus 13 advances the reinforcement of L19, L20 and L28 significantly, it kills the option to wait for new information and productively invest in the future.…”

Section: Performance Comparisonmentioning

confidence: 99%

Network Pricing for Multienergy Systems Under Long-Term Load Growth Uncertainty

Cheng

Yang

et al. 2022

IEEE Trans. Smart Grid

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The long-term uncertainty of multi-energy demand poses significant challenges to the coordinated pricing of multiple energy systems (MES). This paper proposes an integrated network pricing methodology for MES based on the long-run-incremental cost (LRIC) to recover network investment costs, affecting the siting and sizing of future distributed energy resources (DERs) and incentivizing the efficient utilization of MES. The stochasticity of multi-energy demand growth is captured by the Geometric Brownian Motion (GBM)-based model. Then, it is integrated with a system operation model to minimize operation costs, considering low-carbon targets and flexible demand. Thereafter, the kernel density estimation (KDE) method is used to perform the probabilistic optimal energy flow (POEF) to obtain energy flows under uncertain load conditions. Based on the probability density functions (PDFs) of energy flows, an LRIC-based network pricing model is designed, where Tail Value at Risk (TVaR) is used to model the risks of loading levels of branches and pipelines. The performance of the proposed methodology is validated on a typical MES. The proposed pricing method can stimulate cost-effective planning and utilization of MES infrastructures under long-term uncertainty, thus helping reduce low-carbon transition costs.

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“…Moving to the field of financial asset valuation, one of the areas in which fuzzy logic has been widely used is in options pricing. Muzzioli and De Baets [50], in a comprehensive literature review on the topic, identified that the majority of papers using fuzzy logic for option pricing have addressed the direct problem of pricing in both discrete and continuous time settings. Several articles have shown that fuzzy logic is suitable for identifying the market value of complex instruments, such as derivatives [51][52][53].…”

Section: Systematic Analysismentioning

confidence: 99%

Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

2019

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The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques.

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Fuzzy Approaches to Option Price Modeling

Cited by 44 publications

References 60 publications

Behavioural Aspects of the Financial Decision-Making

Behavioural Aspects of the Financial Decision-Making

Network Pricing for Multienergy Systems Under Long-Term Load Growth Uncertainty

Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

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