Search results

It intends to help the learners assess the scenarios of volatility in the Indian capital market which was caused by unpredictable market forces. It also helps in understanding how analysts struggle to predict the direction of the market and what options strategies can be recommended to be deployed by the investors to maximize returns in such compelling scenarios.

This case study presents snapshots of high volatilities caused by the market and economic forces in the Indian capital market. It depicts how analysts struggled to predict the direction of the market; and how high volatility can put them in trouble. It also exemplifies as to how by selecting the apt strategies, investors maximize their immediate returns in a volatile period and can produce large returns in a short time.

The best time to discuss the case is during the completion of options strategies in the course of Derivatives or Portfolio/Investment Management.

Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

CSS 1: Accounting and Finance.

The purpose of this study is to examine the evidences of leverage effects on the conditional volatility of exchange rates because of asymmetric innovations and its spillover effects among the exchange rates of selected emerging and growth-leading economies.

The empirical analysis uses the sign bias test and asymmetric generalized autoregressive conditional heteroskedasticity (GARCH) models to capture the leverage effects on conditional volatility of exchange rates and also uses multivariate GARCH (MGARCH) model to address volatility spillovers among the studied exchange rates.

The study finds substantial impact of asymmetric innovations (news) on the conditional volatility of exchange rates, where Russian Ruble is showing significant leverage effect followed by Indian Rupee. The exchange rates depict significant mean spillover effects, where Rupee, Peso and Ruble are strongly connected; Real, Rupiah and Lira are moderately connected; and Yuan is the least connected exchange rate within the sample. The study also finds the assimilation of information in foreign exchanges and increased spillover effects in the post 2008 periods.

The results probably have the implications for international investment and asset management. Portfolio managers could use this research to optimize their international portfolio. Policymakers such as central banks may find the study useful to monitor and design interventions strategies in foreign exchange markets keeping an eye on the nature of movements among these exchange rates.

This is one of the few empirical research studies that aim to explore the leverage effects on exchange rates and their volatility spillovers among seven emerging and growth-leading economies using advanced econometric methodologies.

The purpose of this paper is to econometrically investigate the level of financial co-integration of the least developed countries (LDCs) of Asia and Pacific region. In addition, the paper also tested the co-integration of LDCs with the world’s second largest economy “China.” For this, the paper employed the foreign exchange data sets of respective LDCs. It also aimed to assess the dynamic conditional correlation (DCC) between the foreign exchange rates of LDCs and China, and further, examined the past and current level of their co-relational dependence.

The authors created data sets namely LDCs of Asia and Pacific, LDCs of SAARC, LDCs of ASEAN, LDCs of Pacific, LDCs of SAARC and ASEAN, LDCs of ASEAN and Pacific, and LDCs of SAARC and Pacific. In addition, the authors tested the co-integration of these seven groups with China, and thus, making a total of 14 data sets. The analysis was carried out using the Johansen and Gregory-Hansen multivariate co-integration econometric techniques. To assess the DCC, multivariate DCC GARCH model was employed.

It was found that at the intra-regional level, exchange rates of LDCs of SAARC, ASEAN and Pacific were co-integrated and showed the existence of 1-3 co-integrating equations. At inter-regional level SAARC-ASEAN, ASEAN-Pacific and SAARC-Pacific were also co-integrated and showed 1-3 co-integrated equations. However, on the inclusion of China in the study, the degree of co-integration of exchange rate of China with LDCs of SAARC and ASEAN increased, while with Pacific, the result was mixed. Conditional correlation estimated of multivariate DCC GARCH model suggested that except for Afghanistan, there was an upward shift in the correlation dynamics of exchange rates of LDCs with China, post global financial crisis.

Asia and Pacific region constituted of 53 countries, of which 13 were LDCs. Enhanced financial integration among LDCs of Asia-Pacific region and also between LDCs and major economies of the region like China will strengthen economic and financial integration efforts in the region.

The present paper attempted a comparative assessment of the co-movements of the foreign exchange markets of LDCs, the countries which have remained largely neglected in academic discourses on financial integration.

The purpose of this paper is to investigate empirically the forecasting performance of jump-diffusion option pricing models of (Merton and Bates) with the benchmark Black–Scholes (BS) model relative to market, for pricing Nifty index options of India. The specific period chosen for this study canvasses the extreme up and down limits (jumps) of the Indian capital market. In addition, equity markets keep on facing high and low tides of financial flux amid new economic and financial considerations. With this backdrop, the paper focuses on finding an impeccable option-pricing model which can meet the requirements of option traders and practitioners during tumultuous periods in the future.

Envisioning the fact, the all option-pricing models normally does wrong valuation relative to market. For estimating the structural parameters that governs the underlying asset distribution purely from the underlying asset return data, we have used the nonlinear least-square method. As an approach, we analyzed model prices by dividing the option data into 15 moneyness-maturity groups – depending on the time to maturity and strike price. The prices are compared analytically by continuously updating the parameters of two models using cross-sectional option data on daily basis. Estimated parameters then used to figure out the forecasting performance of models with corresponding BS and market – for pricing day-ahead option prices and implied volatility.

The outcomes of the paper reveal that the jump-diffusion models are a better substitute of classical BS, thus improving the pricing bias significantly. But compared to jump-diffusion model of Merton’s, the model of Bates’ can be applied more uniquely to find out the pricing of three popularly traded categories: deep-out-of-the-money, out-of-the-money and at-the-money of Nifty index options.

The outcome of this research work reveals that the jumps are important components of pricing dynamics of Nifty index options. Incorporation of jump-diffusion process into option pricing of Nifty index options leads to a higher pricing effectiveness, reduces the pricing bias and gives values closer to the market. As the models have been tested in extreme conditions to determine the dominant effectuality, the outcome of this paper helps traders in keeping the investment protected under normal conditions.

The specific period chosen for this study is very unique; it canvasses the extreme up and down limits (jumps) of the Indian capital market and provides the most apt situation for testifying the pricing competitiveness of the models in question. To testify the robustness of models, they have been put into a practical implication of complete cycle of financial frame.

The purpose of this paper is to explore the forecasting effectiveness of Black-Scholes (BS) focussing parity analysis of time series econometric and implied volatility (IV) numerical techniques.

To analyze the comparative competitiveness of econometric time series and IV models this paper consolidated the study with their inter-relations leading toward multilayered moneyness-maturity correlation of model and market option prices, thoroughly determined the moneyness-maturity combinations of error metrics of Nifty index options.

Out of six models tested and critically examined here, the paper procures only a single model, IV, which best caters to the requirements of option traders and as a result the paper ended up that only IV supports to multifarious moneyness-maturity dimension of option pricing of Nifty index options. The analysis also confirms that the standard VIX is not a reliable tool for determining the base price of Nifty index options (via BS). As the IV landmarks during the most dynamic phase of Indian capital market which is a touchstone to justify the quality of any model, the paper can deduce that IV could continue to perform in hardships of financial contraction par smoothly and effectively.

The final outcome of this research which ended successfully in exploring a dominant model, guided successfully through the most volatile period of Indian economy can be used to safe guard investor's faith and to figure a design which could compete on the canvass of option pricing.

As equity market is always subject to highly unpredictable conditions and may keep on experiencing it through all times to come, the unified objective of research is to find out the most impeccable volatility model to meet out the requirements of option practitioners, specifically contributing upto the satisfaction and expected results during tumultuous period.

The Indian marble and stone industry has got the potential to contribute well to the development of the emerging economy. However, unlike the other Indian industries, stone and marble industries are highly underrated sectors, which may become a critical factor for development. This paper analyses the sustainability factors in supply chain management practices.

A literature review is used to identify the barriers and drivers in sustainable supply chain management practices. Interpretive structural modeling has been used to obtain a hierarchy of barriers and drivers along with driving power and dependence power analysis. Further, MICMAC analysis is used for segregating the barriers and drivers in terms of their impact on sustainability.

The findings of the work of this research are that the attention of society, government, and commercial banks should be more toward the unorganized condition of stone and marble sector. There should be an increase in the commitment of stakeholders to reduce pollution and install safety, by enforcing more relevant laws and regulations and creating the importance of environmental awareness.

The main contribution of this research is to identify the barriers and drivers of sustainable supply chain management in a stone and marble industry. The paper proposes a sound mathematical model to prioritize the critical factors for responsible production and consumption of resources from sustainability perspectives of stone industry.

Lean manufacturing (LM) is prevalent in the manufacturing industry; thus, focusing on fast and accurate lean tool implementation is the new paradigm in manufacturing. Value stream mapping (VSM) is one of the many LM tools. It is understood that combining LM implementation with VSM tools can generate better outcomes. This paper aims to develop an expert system for optimal sequencing of VSM tools for lean implementation.

A proposed artificial neural network (ANN) model is based on the analytic network process (ANP) devised for this study. It will facilitate the selection of VSM tools in an optimal sequence.

Considering different types of wastes and their level of occurrence, organizations need a set of specific tools that will be effective in the elimination of these wastes. The developed ANP model computes a level of interrelation between wastes and VSM tools. The ANN is designed and trained by data obtained from numerous case studies, so it can predict the accurate sequence of VSM tools for any new case data set.

The design and use of the ANN model provide an integrated result of both empirical and practical cases, which is more accurate because all viable aspects are then considered. The proposed modeling approach is validated through implementation in an automobile manufacturing company. It has resulted in benefits, namely, reduction in bias, time required, effort required and complexity of the decision process. More importantly, according to all performance criteria and subcriteria, the main goal of this research was satisfied by increasing the accuracy of selecting the appropriate VSM tools and their optimal sequence for lean implementation.

The purpose of this paper is to identify various factors influencing additive manufacturing (AM) implementation from operational performance in the Indian manufacturing sector and to establish the hierarchical relationship among them.

The methodology includes three phases, namely, identification of factors through systematic literature review (SLR), interviews with experts to capture industry perspective of AM implementation factors and to develop the hierarchical model and classify it by deriving the interrelationship between the factors using interpretive structural modeling (ISM), followed with the fuzzy Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) analysis.

This research has identified 14 key factors that influence the successful AM implementation in the Indian manufacturing sector. Based on the analysis, top management commitment is an essential factor with high driving power, which exaggerates other factors. Factors, namely, manufacturing flexibility, operational excellence and firm competitiveness are placed at the top level of the model, which indicates that they have less driving power and organizations need to focus on those factors after implementing the bottom-level factors.

Additional factors may be considered, which are important for AM implementation from different industry contexts. The variations from different industry contexts and geographical locations can foster the theoretical robustness of the model.

The proposed ISM model sets the directions for business managers in planning the operational strategies for addressing AM implementation issues in the Indian manufacturing sector. Also, competitive strategies may be framed by organizations based on the driving and dependence power of AM implementation factors.

This paper contributes by identification of AM implementation factors based on in-depth literature review as per SLR methodology and validation of these factors from a variety of industries and developing hierarchical model by integrative ISM-MICMAC approach.

The purpose of this paper is to review phytochemical potential of acacia and its associated health advantages. Acacia a moderate-sized, deciduous tree and recognised as health-promoting species because of availability of essential bioactive components. The bioactive compounds such as tannins, flavonoids, alkaloids, fatty acids and polysaccharides (gums) present in the plant parts of acacia, namely, bark, leaves, flowers, fruits, twigs and seeds, have medicinal value and thus are used to overlay the formulations of plant-based drugs and value-added foods.

Major well-known bibliometric information sources such as Web of Science, Scopus, Mendeley and Google Scholar were searched with keywords such as “nutrition value of acacia”, “bioactive compounds”, “health benefits”, “processing and safety” were chosen to obtain a database of 1,428 papers. The search considered papers in the English language from the past 18 years of publication in journals (2004–2022). The article selection process consisted of the screening of titles and abstracts, based on inclusion and exclusion criteria. Articles that did not have acacia components as a study objective were taken into consideration for exclusion. A final database of 87 scientific sources was made after sorting and classifying them according to different criteria based on topic relevance, country of origin and year of publication. Articles with other random descriptors were also searched to complement the discussion of the results obtained.

The literature reflected that acacia contains all necessary phytochemicals like polyphenols, flavonoids, terpenoids, glucosinolates, alkaloids and carotenoids along with essential macro, micro-nutrients. Furthermore, processing methods such as soaking, cooking, roasting and dehusking significantly reduced the anti-nutritional factors present in acacia seeds of different species. This review also focused on the processing methods that are used to eliminate or lower down the anti-nutritional factors from the seeds. Previous findings related to acacia plant parts with respect to food development are explored and mentioned.

This review emphasised mainly on recent studies that had been reported on ethnomedical acacia plants therapeutically, commercially and exponentially for further studies to increase the utilisation in food processing.

Purpose: This study aims to employ bibliometric analysis to condense multiple studies into a single publication that not only gives insights into the growth and advancement of the research area but also establishes a future research agenda. This study provides a summary of advances in academic research on money laundering. The research includes bibliometric analysis and visualisation of bibliographic data using the Scopus database. The results of the study show that there has been a significant increase in the number of publications in the field of money laundering research, with topics focussed on specific areas. This study will also benchmark existing and preliminary themes, designs, and methodological choices for future money laundering research.

Methodology: With the help of the ‘visualisation of similarities’ (VOS) viewer open-source software, bibliometric analysis was performed using Scopus data. Citation analysis, topic mapping, country collaboration, co-citation analysis, and keyword co-occurrence analysis are some of the approaches used in bibliometric analysis.

Findings: Based on a bibliometric analysis of 1,391 research papers retrieved from the Scopus database over the past three decades (1990–2021), the study identified the most prominent authors, studies, journals, affiliations, and countries in the field of money laundering, as well as the most co-cited authors and journals. The writers also highlight future study issues in the field of money laundering.

Practical implications: The study’s findings might provide academics and practitioners with information on the present state of money laundering research and trend subjects. It can also be used as a guideline for identifying possible research gaps in the existing literature.