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Option pricing theory enables computation of the price of an option using different variables associated with the underlying security and options contract. The purpose of this study is to assess research trends that emerged in the field of option pricing. This study reviews existing literature of the option pricing domain, both qualitatively and quantitatively, and identifies potential themes for future research.

This study adopts bibliometric analysis method to explore literature published in the option pricing domain. As part of bibliometric analysis, this study considers both descriptive and network analysis to assess publication trends. For descriptive analysis, the “bibliometrix” package proposed by Aria and Cuccurullo (2017) is used and for network analysis, VOS viewer (Van Eck and Waltman, 2017) and Gephi (Bastian et al., 2009) are used.

This study identifies research trends, top researchers, articles, journals and contributions from institutions and countries in the option pricing domain. It identifies four clusters that show different directions and also focuses on past studies on the same subject. It explores research gaps by performing an in-depth analysis of existing literature on option pricing and suggests the way forward for research in this area.

To the best of the authors’ knowledge, no previous studies have attempted to analyze the literature published in the option pricing domain. This study fulfils this research gap by conducting a comprehensive analysis of studies in the option pricing area. This study identifies quality research work published in the domain, research trends, contribution by most relevant researchers, contributions across geographies and institutions and the connections among these aspects. This study also identifies important themes and provides directions for future research.

The purpose of this paper is to present a hybridized technique for analyzing the stochastic behavior of an industrial system. The feeding system of a paper mill situated in North India producing 200 tons of paper per day has been considered for analysis and efforts have been made to incorporate vague, ambiguous, imprecise and conflicting information quantified by fuzzy numbers.

In this paper, three important tools namely, fuzzy analysis, neural network and genetic algorithms (GAs), are used to built a hybridized and more realistic technique herein named as, neural network and GAs‐based Lambda‐Tau (NGABLT). The technique will facilitate the maintenance personnel in making a better decision. This technique has been demonstrated by computing some of the reliability indices of the considered system.

The results indicate that NGABLT technique reduces the gap between crisp and existing Lambda‐Tau results, i.e. it may be a more useful tool to assess the current system condition and suggests to improve the system reliability and availability.

The authors have suggested a hybridized technique for analyzing the stochastic behavior of the feeding system in a paper mill by computing fuzzy reliability indices.

Guar Seed crop is ruling the Indian International business mainly due to its application as a drilling fluid in shale energy industry concentrated in the USA. One of the allegations against futures market is its possible role in increasing the volatility of underlying physical market prices. Suspension of guar seed futures contract in 2012 at National Commodity Derivatives Exchange of India (NCDEX)-India, has reignited the controversy and raised an alarm bell to peek into obscure world of Indian commodity derivatives market. Against the backdrop of fiasco in guar futures trading, the purpose of this paper is to investigate whether sudden surge in futures trading volume leads to increase in the volatility of spot market prices.

Guar seed spot returns volatility is modeled as a GARCH (1, 1) process. Futures trading volume and open interest are segregated into expected and unexpected components. The data are analyzed from 2004 to 2011 using Augmented GARCH model to study the contemporaneous relationship between spot volatility and unexpected futures trading activity and Granger Causality test for examining the dynamic relationship between them and ascertaining causality.

Augmented GARCH model reports positive relationship between unexpected futures trading volume (UTV) and spot returns volatility, and, Granger Causality flows from UTV to spot volatility. Therefore, when the level of futures trading volume increases unexpectedly, the volatility of spot prices increases pointing toward the destabilizing impact of futures trading. However, hedger’s activity, represented by open interest is not seen to have any causal/destabilizing impact on spot price volatility of guar seed.

The study provides empirical evidence to support the concern of regulators, genuine hedgers and other traders about the presence of excessive speculation and market manipulations perpetrated through futures market that is disturbing the underlying physical market instead of strengthening it by aiding in price discovery and risk mitigation.

There are very few studies which have empirically investigated the temporal relation between volume and volatility in Indian agricultural commodity markets. With guar seed as a special case the present study investigates statistically the impact of futures trading on spot price volatility. In light of the findings of the study, the curb imposed on guar seed futures trading in 2012 was justified.

The purpose of this paper is to design a low-cost stress bimorph RF-MEMS switch which is the desired transmission area application.

The bimorph structure of the low-temperature plasma-enhanced chemical vapor deposition (PECVD) of thermal oxide and gold are utilized to create the vibrating membrane. The effects of process conditions of low-temperature oxide deposited using the PECVD technique enable stress-free deposition of the key structural layer.

Scanning electron microscope images of the RF micro-switch confirms negligible stress in the released structure. The RF performances of this device exhibit isolation around 43 dB of up to 50 GHz in the OFF-state position and an insertion loss of less than 0.18 dB in the ON-state.

The finite element method results show good isolation of 43 dB and less insertion loss of 0.18 dB.

This research aims to examine the impact of friction stir processing (FSP) treatment on an aluminum alloy, especially the AD31T alloy derived from the Al-Fe-Mg-Si system. The aim is to assess the influence of different processing techniques on the microstructure and physical and mechanical characteristics of the material, with a specific focus on structural and bulk imperfections inside the stir zone (SZ).

The study demonstrates that augmenting the linear velocity of the tool within the 25–100 mm/min range results in significant enhancements. The enhancements include a decrease in the heat-affected zone (HAZ), a reduction in the extent of volume defects inside the SZ and a more uniform deformation. The microstructural analysis results are corroborated by data acquired from microhardness and electrical conductivity studies, confirming the beneficial influence of modifying the tool’s linear velocity on the material parameters.

This study provides significant observations on the changes in microstructure and the generation of flaws throughout the process of FSP of AD31T alloy. These results have practical implications for improving the characteristics of the alloy and optimizing the production conditions.

All samples exhibit a distinct reduction in electrical conductivity within the initial third of the sample, aligning with the transitional region between the base metal (BM) and the HAZ. This underscores the importance of understanding the transitional zones during FSP.

The purpose of the present study is to simulate the industrial hot-dip process of Zn-2.5Wt.%Mg-3 Wt.%Al and Zn-2.5 Wt.%Mg-9 Wt.%Al-0.15 Wt.%Si coatings and to study the effect of low and high Al variation on their microstructure, microhardness, adhesion and corrosion behaviour.

The hot-dip Zn-2.5 Mg-xAl coating simulation on steel substrate was carried out using a hot-dip process simulator. The microstructure of the coatings was characterized using a scanning electron microscope, energy dispersive spectroscopy and X-ray diffraction. The corrosion behaviour of the coatings was studied using a salt spray test in 5% NaCl solution as well as dynamic polarization in 3.5% NaCl solution.

Microhardness of the developed Zn-2.5 Mg-xAl coatings has been found to be approximately two times higher than that of the conventional galvanized coating. Zn-2.5 Mg-3Al coating has exhibited two times higher corrosion resistance as compared to that of Zn-2.5 Mg-9Al-0.15Si coating because of formation of more homogeneous and defect-free microstructure of the former. The MgZn₂ phase has undergone preferential dissolution and provided Mg²⁺ ions to form a protective film.

The relative corrosion resistance of the two Zn–Al–Mg coatings with different Al content has been studied. The defect formed because of higher Al addition in the coating has been detected, and its effect on corrosion behaviour has been analysed.

The pharmaceutical industry faces multiple risks that adversely affect its performance. Within these risks, some dependencies have been observed, which help in streamlining the mitigation efforts. Therefore, the present work identifies and categorizes various risks/sub-risks in cause–effect groups, considering uncertainty in the decision-making process.

An extensive literature review and experts' opinions were utilized to identify and finalize the risks faced by the pharmaceutical industry. For further analysis, data collection was done using a questionnaire focusing on finalized risks. Based on the data, the causal relation under uncertainty between various risks/sub-risks was identified using a multi-criteria decision making (MCDM) technique, i.e. intuitionistic fuzzy DEMATEL, in a pairwise manner.

The results show that the three most prominent risk categories are operational, demand/customer/market and financial. Also, out of the seven main risks, only supplier and operational are categorized within the effect group and the rest, i.e. financial, demand, logistics, political and technology within the cause group. The sub-risks within each category have also been categorized into cause–effect groups. The mitigation of cause group risks will help in economize the financial resources and improve the performance and resilience of the industry.

There is insufficient research on identifying the causality among the pharmaceutical industry risks. Additionally, an extensive discussion on the identified cause–effect groups is also missing in the literature. Therefore, in this work, efforts have been made to determine the prominent risks for the Indian pharmaceutical industry that will be helpful for channelizing the resources to mitigate risks for a resilient industry.

The purpose of the present work is to improve the industry performance by identifying and quantifying the risks faced by the Indian pharmaceutical industry (IPI). The risk values for the prominent risks and overall industry are determined based on the four risk parameters, which would help determine the most contributive risks for mitigation.

An extensive literature survey was done to identify the risks, which were also validated by industry experts. The finalized risks were then evaluated using the fuzzy synthetic evaluation (FSE) method, which is the most suitable approach for the risk assessment with parameters having a set of different risk levels.

The three most contributive sub-risks are counterfeit drugs, demand fluctuations and loss of customers due to partners' poor service performance, while the main risks obtained are demand, financial and logistics. Also, the overall risk value indicates that the industry faces medium to high risk.

The study identifies the critical risks which need to be mitigated for an efficient industry. The industry is most vulnerable to the demand risk category. Therefore, the managers should minimize this risk by mitigating its sub-risks, like demand fluctuations, bullwhip effect, etc. Another critical sub-risk, the counterfeit risk, should be managed by adopting advanced technologies like blockchain, artificial intelligence, etc.

There is insufficient literature focusing on risk quantification. Therefore, this work addresses this gap and obtains the industry's most critical risks. It also discusses suitable mitigation strategies for better industry performance.