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The use of risk proxies in internal models remains a popular modelling solution. However, there is some risk that a proxy may not constitute an adequate representation of the underlying asset in terms of capturing tail risk. Therefore, using empirical examples for the financial collateral haircut model, this paper aims to critically review available statistical tools for measuring the adequacy of capturing tail risk by proxies used in the internal risk models of banks. In doing so, this paper advises on the most appropriate solutions for validating risk proxies.

This paper reviews statistical tools used to validate if the equity index/fund benchmark are proxies that adequately represent tail risk in the returns on an individual asset (equity/fund). The following statistical tools for comparing return distributions of the proxies and the portfolio items are discussed: the two-sample Kolmogorov–Smirnov test, the spillover test and the Harrell’s C test.

Upon the empirical review of the available statistical tools, this paper suggests using the two-sample Kolmogorov–Smirnov test to validate the adequacy of capturing tail risk by the assigned proxy and the Harrell’s C test to capture the discriminatory power of the proxy-based collateral haircuts models. This paper also suggests a tool that compares the reactions of risk proxies to tail events to verify possible underestimation of risk in times of significant stress.

The current regulations require banks to prove that the modelled proxies are representative of the real price observations without underestimation of tail risk and asset price volatility. This paper shows how to validate proxy-based financial collateral haircuts models.

The purpose of this paper is to propose a new method based on goodness of fit tests for shift detection problems.

In this method, although the distribution of gathered data from the process is the subject of control, but any out-of-control signal could also be generalized to the overall state of the process including the parameters of the distribution.

Results of simulation study denote that among goodness of fit tests, the χ2 test has a better performance than the Kolmogorov-Smirnov test in detecting shifts of process. Also comparison of proposed method with traditional methods denotes that, proposed method generally has smaller probabilities of first and second type errors.

To the best of author’s knowledge, no attention has previously been paid to application of goodness of fit tests in process control.

The purpose of this paper is to propose a new statistical approach to evaluate complex open innovation projects on a quantitative basis. In certain circumstances, open innovation entails a radical change of policy that involves various different functions of a company such as R&D, production, and management over a period of years and gives rise to mechanisms of mutual interaction with several business partners, such as collaboration with other companies, universities and R&D institutions, and new suppliers. Then, the question arises of how to measure the impact of such complex open innovation processes on the overall performances of companies.

A holistic case study is applied to analyze the effect of open innovation projects on a corporate company’s stock price dynamics. The scope is to identify two different scenarios pre- and post-adoption of an open innovation model by a multinational company, Fujifilm. In particular, a stochastic model, namely the log-normal model, is applied along with three statistical tests: Kolmogorov-Smirnov, Cramer von Mises, and F-test for equal variances, in order to verify if the adoption of an open innovation model causes any significant change in the stock price dynamics of the corporate company.

From the findings emerges evidence that open innovation projects have a moderate effect on Fujifilm’s stock price dynamics, but a greater improvement of the perception of Fujifilm’s stock value. This enhances the management and financial literature review by offering a novel, empirical perspective on the effect of the adoption of an open innovation model on a corporate company’s stock price dynamics.

This research is limited to a single case study, but it can be extended to other stock market companies and therefore improve on the present study.

An original application of Kolmogorov-Smirnov tests to detect and measure the differences between the two regimes of pre-open innovation and post-innovation regimes.

Purpose: Worldwide economies have been shattered by the alarming increase in Non-Performing Assets (NPAs) in Banking Sector. In India, the rise in NPA levels gives a clear insight into the health of industry and state. This study aims to determine how NPAs in India impact the profitability of eight banks chosen from the public and private sectors; specifically: Punjab National Bank (PNB), Bank of India (BOI), UCO Bank, Punjab and Sind Bank (PSB), HDFC Bank, Axis Bank, ICICI Bank, and Yes Bank; during the period 2009/2010 to 2017/2018.

Design/methodology/approach: The study utilised IBM SPSS version 20 application to carry out our statistical analysis of measures of central location (mean and median), measures of dispersion (standard deviation), to carry out the Kolmogorov–Smirnov test to check the normality of data, the Mann–Whitney U test (for two groups) for median comparison between private and public sector banks and the Kruskal–Wallis test (for more than two groups) for median comparison for more than two banks. p ≤0.01 and p ≤0.05 were the two-tailed significance level used for determining the significance of all statistical tests.

Findings: Trend analysis and statistical tests show that the trend in public sector banks to have NPAs is higher compared to private sector banks, and losses arising from NPA impact the banks’ profitability.

Practical implications: It is apparent that NPAs are a large threat to banks in India as it reflects the state of the Indian economy. The growth of the economic cycle is predominantly dependent on the smooth and profitable functioning of private and public sector banks. This current study focusses on and compares the impact of NPAs on the profitability of public and private sector banks. NPAs have grown exponentially more in the case of public sector banks than private sector banks, which has affected the former banks’ financial health and performance. Increases in the level of NPAs adversely affect the working style and long-term stability of public and private sector banks in the economy.

Social Implications: NPAs have a negative influence on the profitability of the banks as well as on the economic growth of the country too. However, it is recommended that management in the banking sector, particularly the public banks, should use various preventive and recovery strategies to reduce the risk of failure and to keep track of NPAs to stay safe.

Originality/value: This study aims to determine how NPAs in India impact the profitability of eight banks chosen from the public and private sectors; specifically: PNB, BOI, UCO Bank, PSB, HDFC Bank, Axis Bank, ICICI Bank, and Yes Bank; during the period 2009/2010 to 2017/2018.

Value at risk (VaR) and expected shortfall (ES) are popular market risk measurements. The former is not coherent but robust, whereas the latter is coherent but less interpretable, only conditionally backtestable and less robust. In this chapter, we compare an innovative artificial neural network (ANN) model with a time series model in the context of forecasting VaR and ES of the univariate time series of four asset classes: US large capitalization equity index, European large cap equity index, US bond index, and US dollar versus euro exchange rate price index for the period of January 4, 1999, to December 31, 2018. In general, the ANN model has more favorable backtesting results as compared to the autoregressive moving average, generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) time series model. In terms of forecasting accuracy, the ANN model has much fewer in-sample and out-of-sample exceptions than those of the ARMA-GARCH model.

Almost four years after the COVID-19 pandemic, the changes in the labour market and legislation, but also in people's lives, do not stop. At the same time, employees' perceptions regarding the change in the legislative and contractual framework, as well as in the working conditions and the use of telework, also change. Therefore, the aim of the paper is to identify the perceptions of employees regarding the use of telework in the post-pandemic period.

The research was based on a survey carried out on 128 teleworkers in the post-pandemic period. The statistical hypotheses were tested using Kolmogorov–Smirnov and Kruskal–Wallis tests, multiple linear regression and pairwise comparison analysis.

The results of the study demonstrate that the modification of the legislative and contractual framework and of the working conditions, as well as of the way of using information and communication technology in the post-pandemic era, generates a positive and significant impact on the use of telework by employees. Some of the main advantages valued by teleworkers included the possibility of benefitting from a flexible work schedule and the possibility of reducing transport costs.

The study highlights the need to continuously develop and update labour policies and strategies in line with current and future labour market requirements, considering the implications of telework on the perceptions of employees, so that government organisations and managers who want to protect the rights and interests of teleworkers, aspects of their lives and organise an appropriate work environment manage to do so in order to achieve the expected results.

This paper aims to: design a comprehensive, review‐based and statistically tested corporate social responsibility disclosure (CSRD) index; measure item‐wise and theme‐wise the social performance of the top 82 companies in India; and investigate item‐wise and theme‐wise the variations in CSRD.

The paper presents an empirical study of CSRD in 2009‐2010, using content analysis, Cronbach's α, the Kolmogorov‐Smirnov and Shapiro‐Wilk tests of normality and a six point scale (0‐5), mean, skewness, kurtosis, and Levene's, Kruskal‐Wallis's and Mood's median tests for analysis and interpretation.

CSRD shows less satisfactory social performance, mainly narrative, and varies significantly among items and themes. Community development, with a mean score of 14.30, is the most disclosed theme, followed by HR, with a score of 11.20. The human element is the center of social performance in India. More than equal focus should be given to the environment and to emissions, which impact the greater interests of the world. Some burning global issues like water usage, alternative sources of energy, product safety and innovation have not received adequate attention.

The study offers ample scope for the further studies as each and every theme and item considered in the model/index requires individual focus to serve the future generations of mankind. Longitudinal/transnational studies in the area of CSR could be carried out to set the scene for further studies.

The paper recommends mandatory CSR norms leading to improved disclosure, the sharing of innovative knowledge, cost reductions and enhanced effectiveness in managing scarce resources.

The paper evaluates social performance in the economic, social, religious environment and highlights the emerging philanthropic attitude. The paper improves an existing model by incorporating an emerging dimension, i.e. “Emissions of carbon and other harmful gases”. The CSEEE index designed here is highly appropriate for developing economies like India. The paper measures CSRD using six‐point scales for the first time.

The purpose of this paper is to know whether the authors’ productivity pattern of library and information science (LIS) open access journals adheres to Lotka’s inverse square law of scientific productivity. Since the law was introduced, it has been tested in various fields of knowledge, and results have varied. This study has closely followed Lotka’s inverse square law in the field of LIS open access journals to find a factual result and set a baseline for future studies on author productivity of LIS open access journals.

The publication data of selected ten LIS open access journals pertain to authorship, citations were downloaded from the Scopus database and analysed using bibliometric indicators like authorship pattern, collaborative index (CI), degree of collaboration (DC), collaborative coefficient (CC) and citation counts. This study has applied Lotka’s inverse square law to assess authors’ productivity pattern of LIS open access journals and further Kolmogorov-Smirnov (K-S) goodness-of-fit test applied for testing of observed and expected author productivity data.

Inferences were drawn for the set objectives on authorship pattern, collaboration trend and authors’ productivity pattern of LIS open access journals covered in this study. The single authorship pattern is dominant in LIS open access journals covered in this study. The CI, DC and CC are found to be 1.95, 0.47 and 0.29, respectively. The expected values as per Lotka’s law (n = −2) significantly vary from the observed values as per the chi-square test and K-S goodness-of-fit test. Hence, this study does not adhere to Lotka’s inverse square law of scientific productivity.

Researchers may find an idea about the authors’ productivity patterns of LIS open access journals. This study has used the K-S goodness-of-fit test and the chi-square test to validate the authors’ productivity data. The inferences found out from this study will be a baseline for future research on author productivity of LIS open access journals.

This study is significant from the viewpoint of the growing research on open access journals in the field of LIS and to identify the authorship pattern, collaboration trend and author productivity pattern of such journals.