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Revenue insurance is the most popular form of insurance available in the US federal crop insurance program. The majority of crop revenue policies are sold with a harvest price replacement feature that pays out on lost crop yields at the maximum of a realized or projected harvest price. The authors introduce a novel actuarial and statistical approach to rate revenue insurance policies with exotic price coverage: the payout depends on an order statistic or average of prices. The authors examine the price implications of different dependence models and demonstrate the feasibility of policies of this type.

Hierarchical Archimedean copulas and vine copulas are used to model dependence between prices and yields and serial dependence of prices. The authors construct several synthetic exotic price coverage insurance policies and evaluate the impact of copula models on policies covering different types of risk.

The authors’ findings show that the price of exotic price coverage policies is sensitive to the choice of dependence model. Serial dependence varies across the growing season. It is possible to accurately price exotic coverage policies and we suggest these add-ons as a possible avenue for developing private crop insurance markets.

The authors apply hierarchical Archimedean copulas and vine copulas that allow for flexibility in the modeling of multivariate dependence. Unlike previous research, which has primarily considered dependence across space, the form of exotic price coverage requires modeling serial dependence in relative prices. Results are important for this segment of the agricultural insurance market: one of the main areas that insurers can develop private products around the federal program.

The purpose of this paper is to investigate the dynamic relationship among Gold, Crude oil, Indian Rupee-US Dollar and Stock market-Sensex (gold, oil, dollar and stock market (GODS)) in the pre-crisis, the crisis and the post-crisis periods in the Indian context.

The authors use Johansen’s cointegration technique, Vector Error Correction Model (VECM), Vector Auto Regression, VEC Granger Causality/Block Exogeneity Wald Test, and Granger Causality and Toda Yamamoto modified Granger causality to study long-run relationship and causality.

Johansen’s cointegration test results indicate that there is a long-run equilibrium relationship among the variables in the pre-crisis and the crisis periods but not in post-crisis period. VECM results report that none of four models of the variables show long-run causality in the pre-crisis period. During the crisis period, both crude oil and Sensex models show long-run causality. However, in some cases, results indicate short-run causality. The authors find one-way causality from USD and Sensex to crude oil, and from gold and Sensex to USD. Thus, the authors conclude that the relationship among GODS is dynamic across global financial crisis.

The research findings of this study are vital to the large group of stakeholders and participants of gold, crude oil, US dollar and stock market in emerging economies like India. The results are useful to importers, exporters, government, policy makers, corporate houses, retail investors, portfolio managers, commodity traders, treasury and fund managers, other commercial traders, etc.

This study is one of its kinds as it investigates the relationship among GODS in India in different sub-periods like before, during and after the global financial crisis of 2008. None of the studies compare phase-wise relationship among GODS in the Indian context. The study contributes to the economic theory and the body of knowledge. It highlights the need to revisit the economic theory to explain the interplay mechanism among GODS.

The purpose of this paper is to provide the maintenance personnel with a methodology for modeling and estimating the reliability of critical machine systems using the historical data of their inter‐failure times.

The failure patterns of five different machine systems were modeled with NHPP‐log linear process and HPP belonging to stochastic point process for predicting their reliability in future time frames. Besides the classical approach, Bayesian approach was also used involving Jeffreys's invariant non‐informative independent priors to derive the posterior densities of the model parameters of NHPP‐LLP and HPP with a view to estimating the reliability of the machine systems in future time intervals.

For at least three machine systems, Bayesian approach gave lower reliability estimates and a larger number of (expected) failures than those obtained by the classical approach. Again, Bayesian estimates of the probability that “ROCOF (rate of occurrence of failures) would exceed its upper threshold limit” in future time frames were uniformly higher for these machine systems than those obtained with the classical approach.

This study indicated that, the Bayesian approach would give more realistic estimates of reliability (in future time frames) of the machine systems, which had dependent inter‐failure times. Such information would be helpful to the maintenance team for deciding on appropriate maintenance strategy.

With the help of Bayesian approach, the posterior densities of the model parameters were found analytically by considering Jeffreys's invariant non‐informative independent prior. The case study would serve to motivate the maintenance teams to model the failure patterns of the repairable systems making use of the historical data on inter‐failure times and estimating their reliability in future time frames.

The purpose of this paper is to deal with profit maximization problem of two-layer supply chain (SC) under fuzzy stochastic demand having finite mean and unknown variance. Buyback policy is employed from the retailer to supplier. The profit of the supplier solely depends on the order size of the retailers. However, the loss of shortage items is related to loss of profit and goodwill dependent. The authors develop the profit function separately for both the retailer and supplier, first, for a decentralized system and, second, joining them, the authors get a centralized system (CS) of decision making, in which one is giving more profit to both of them. The problem is solved analytically first, then the authors fuzzify the model and solve by fuzzy Hausdorff distance method.

The analytical models are formed for both centralized and decentralized systems under non-cooperative and cooperative environment with suitable constraints. A significant assumption on density function, namely Cauchy-type density function, is introduced for demand rate because of its wider range of the retailers’ satisfactions. Fuzzy Hausdorff metric is incorporated within the fuzzy components of the fuzzy sets itself. Using this method, the authors find out closure values of both centralized and decentralized policies, which is an essential part of any cooperative and non-cooperative two-layer SC models. Moreover, the authors take care of the profit values with corresponding ambiguities for both the systems explicitly.

It is found that the centralize policy of SC could only be able to maximize the profit of both the retailers and suppliers. All analytical results are illustrated numerically along with sensitivity analysis and side by side comparative studies between Hausdorff and Euclidean distance measure are done exclusively.

The main focus of attention of the proposed model is given to usefulness of Hausdorff distance. Unlike other distances, Hausdorff distance can take special care on the similarity measures of different fuzzy sets. Researchers have been engaged to use Hausdorff distance on the different fuzzy sets but, in this study, the authors have used it within the components of a same fuzzy set to gain more closure values than other methods.

The use of this Hausdorff distance approach is totally new as per literature survey suggested yet. However, the Cauchy-type density function has not been introduced anywhere in SC management problems by modern researchers still now. In crisp model, the sensitivity on goodwill measures really provides a special attention also.

This paper aims to analyze the stock price reaction because of dividend reduction (DR) announcements in the Indian equity market, controlling for share repurchases.

The sample comprises National Stock Exchange (NSE) 500 companies listed in the NSE Ltd. covering a time span from year 2009 to 2019. Using the event study methodology, the authors measure the impact of DR announcements on security prices around the event day. The authors also examine the price response to DRs at the interim stage versus the final stage and identify the factors that drive the decision to reduce dividends at the interim level versus final level.

The authors find that overall DR announcements negatively impact abnormal returns. Firms that experience stronger adverse price reaction following DR announcements resort to share repurchase in the same year to boost stock prices. The authors find that interim DRs create more negative price reactions than final DRs. Finally, firms experiencing lower levels of prior year earnings, firms with smaller sizes and overvalued firms tend to reduce dividends at the interim level instead of postponing the reduction to the final level.

This paper examines stock price reaction because of DR announcements of Indian firms. The sample comprises firms that reduce dividends with contemporaneous share repurchases as well as firms that reduce dividends without contemporaneous repurchase activity. To the best of the authors’ knowledge, studies on substitution effect of dividends with buyback in the context of Indian equity market are rare. Further, investigating the difference in stock price movement because of DRs at the interim level versus the final level is the unique contribution of this paper.

The present study aims to construct ensemble machine learning (EML) algorithms for groundwater potentiality mapping (GPM) in the Teesta River basin of Bangladesh, including random forest (RF) and random subspace (RSS).

The RF and RSS models have been implemented for integrating 14 selected groundwater condition parametres with groundwater inventories for generating GPMs. The GPM were then validated using the empirical and bionormal receiver operating characteristics (ROC) curve.

The very high (831–1200 km²) and high groundwater potential areas (521–680 km²) were predicted using EML algorithms. The RSS (AUC-0.892) model outperformed RF model based on ROC's area under curve (AUC).

Two new EML models have been constructed for GPM. These findings will aid in proposing sustainable water resource management plans.

This paper aims to make an attempt to identify the determinants of dividend policy by analyzing 125 real estate companies, which are selected on the basis of consistent dividend distribution throughout the study period. Most of these companies either listed with Bombay Stock Exchange or National Stock Exchange.

This paper applies three alternative methods to verify and validate the results obtained from each other method, namely, fully modified ordinary least square (FMOLS), dynamic ordinary least square and generalized method of moments (GMM). Data collected of the selected companies’ post-recession period i.e. 2009-2017. The selected companies have age either 5 years old or more when data are retrieved from the above-mentioned sources. Due to much volatility in the recession period in the real estate firms at the global level, no data have been taken of the firms before March 2009. Moreover, for arriving at good analysis and an adequate number of observations for the study more recent data have been taken.

Empirical findings of this research paper depict that firm previous dividend, firm risk and liquidity are strong predictors of future dividend payout ratios (DPRs). The results indicate that firm risk as measured through price-earnings ratio (PE ratio) has a positive association with a DPR of selected real estate firms. Lagged DPR used in the GMM test as an exogenous variable is showing positive significant association with DPR. Firm’s growth is found significant in FMOLS and GMM techniques. On the other firm’s size is found significant according to cointegration techniques.

The present study shall be useful to different stakeholders of real estate companies. Various significant determinants as identified can be used by management for designing optimum dividend policy and providing maximum benefits to existing shareholders. Similarly existing and prospective shareholders may predict the future payment of dividend and accordingly they may take investment decisions in these firms, as the future fund’s requirement of a firm depends upon dividend payment and retention ratio.

As per the authors’ knowledge, there is no single study carried in the post-recession period to predict determinants of dividend policy of real estate sector using three alternatives of methods to verify and validate the results obtained from each other method. The study is carried out after exploring determinant from a diverse range of period of studies (oldest one to latest one).

The aim of this paper is to show how a bank's brand value is quantitatively assessed using the Interbrand methodology, taking into account the specifics of the banking market. Therefore, the objective of this paper is to review the ways in which brands contribute to the higher market value of banks by strengthening intellectual capital (IC), as reflected in increased levels of competitiveness and the reputation that the bank maintains in the minds of customers.

This paper applies the Interbrand methodology, which indicates that the assessment of brand value implies the determination of economic profit as the difference between the net operating profit after tax and the cost of capital. The brand profit is then calculated as the product of the economic profit and the index of the brand role. Brand value is obtained as the product of the brand's profit and the discount rate of the brand. In order to further test the results obtained through the application of the Interbrand methodology, linear regression was applied to the panel data in order to provide more efficient econometric estimates of the model parameters.

This research has shown that the Interbrand methodology's empirical foundations lie in the Montenegrin banking market, but also that, out of all of the analyzed parameters, the greatest significance is obtained from the profit of the brand, which influences the value of bank brands.

This research is related to the service sector–in this case, financial services – meaning that it is necessary to adjust the calculation of the weighted average cost of capital. Although the banking sector is a very competitive market, a limitation exists in the fact that the research was conducted only in Montenegro. In other words, in order to achieve a more detailed analysis, this methodology should be applied to more countries, such as those within the Western Balkans, as they have a relatively similar level of development.

A main contribution of this paper is that the assessment of the banks' brand value could be useful to future investors. Therefore, the improvement of the financial sector–in this case, banks–as institutions that hold a dominant position in the financial market in Montenegro, is a particularly important issue. It is important to point out that the research conducted could serve as a means by which to bridge the gap between theory and practice, since the methodology of the consulting company Interbrand has been optimized and adjusted to the Montenegrin banking market.

On considering the fact that most countries of the Western Balkans are at a similar level of development, the authors can conclude that, with the help of this adapted form of methodology, this research can be applied to assess banks' brand value in neighboring countries.

This paper serves as the basis for further research as the analysis of banking institutions that comprise both marketing and financial aspects, i.e. the application of the Interbrand methodology, was not conducted in Montenegro. Also, this paper overcomes the literal gap between theory and practice as there is little research thus far involving the application of the Interbrand methodology to the field of finance; especially in the field of banking. The authors point out the specifics of the banking sector as a key explanation for this. This is why it is necessary to make certain adjustments to the methodology. The research has positive implications for banks' internal and external stakeholders. The originality of this research is reflected in the fact that the Interbrand methodology has been optimized in order to assess the brand of banks, taking into account the specificity of the analyzed market. Brand is analyzed as a component of IC: another factor that exemplifies the value of this research.