Forecasting Daily Variability of the S and P 100 Stock Index using Historical, Realised and Implied Volatility Measurements

The increasing availability of financial market data at intraday frequencies has not only led to the development of improved volatility measurements but has also inspired research into their potential value as an information source for volatility forecasting. In this paper we explore the forecasting value of historical volatility (extracted from daily return series), of implied volatility (extracted from option pricing data) and of realised volatility (computed as the sum of squared high frequency returns within a day). First we consider unobserved components and long memory models for realised volatility which is regarded as an accurate estimator of volatility. The predictive abilities of realised volatility models are compared with those of stochastic volatility models and generalised autoregressive conditional heteroskedasticity models for daily return series. These historical volatility models are extended to include realised and implied volatility measures as explanatory variables for volatility. The main focus is on forecasting the daily variability of the Standard and Poor's 100 stock index series for which trading data (tick by tick) of almost seven years is analysed. The forecast assessment is based on the hypothesis of whether a forecast model is outperformed by alternative models. In particular, we will use superior predictive ability tests to investigate the relative forecast performances of some models. Since volatilities are not observed, realised volatility is taken as a proxy for actual volatility and is used for computing the forecast error. A stationary bootstrap procedure is required for computing the test statistic and its $p$-value. The empirical results show convincingly that realised volatility models produce far more accurate volatility forecasts compared to models based on daily returns. Long memory models seem to provide the most accurate forecasts

Ethical Awareness, Ethical Judgment and Whistleblowing: A Moderated Mediation Analysis

This study aims to examine the ethical decision-making (EDM) model proposed by Schwartz (J Bus Ethics, doi:10.1007/s10551-015-2886-8,2016), where we consider the factors of non-rationality and aspects that affect ethical judgments of auditors to make the decision to blow the whistle. In this paper, we argue that the intention of whistleblowing depends on ethical awareness (EAW) and ethical judgment (EJW) as well as there is a mediation–moderation due to emotion (EMT) and perceived moral intensity (PMI) of auditors. Data were collected using an online surveywith 162 external auditors who worked on audit firms in Indonesia as well as 173 internal auditors working in the manufacturing and financial services. The result of multigroup analysis shows that emotion (EMT) can mediate the relationship between EAW and EJW. The nature of this relationship is more complex and then tested by adding moderating variables using consistent partial least squares approach. We found that EMT and PMI can improve the relationship between ethical judgments and whistleblowing intentions. These findings indicate that internal auditors are more likely to blow the whistle than external auditors; and reporting wrongdoing internally and anonymously are the preferred way of professional accountants to blow the whistle in Indonesia

Green product development and performance of Brazilian firms: Measuring the role of human and technical aspects

The goal of this study is to present and test a conceptual framework that describes the technical aspects (TA), human/organizational aspects (HOA) of the adoption of green product development (GPD) practices and the effect of these practices on firms' environmental (EP), operational (OP) and market performance (MP). To this end, after reviewing the literature on these themes, a conceptual framework with 5 hypotheses is proposed. These hypotheses were tested on 62 Brazilian companies through structural equation modeling using SmartPLS 2.0M3. The main results of this study are as follows: (a) in general, the proposed framework obtained adequate goodness of fit statistics (GoF); (b) technological factors are shown to have an influence on the adoption of GPD practices, and those practices are related to company EP, OP and MP, thus confirming 4 hypotheses of the study; and (c) one of the study's hypotheses is not validated, indicating that the relationship of human/organizational aspects to GPD must be further analyzed. This work extends the literature because: (a) the conceptual framework tested in this study establishes several concepts that have been only partially tested in the previous literature; (b) this work presents evidence about Brazil, where the themes addressed herein have not been yet been thoroughly investigated; and (c) the non-validation of the hypothesis regarding the relationship between human/ organizational aspects with respect to the adoption of product-related environmental practices requires attention. © 2014 Elsevier Ltd. All rights reserved

The Effects of the COVID-19 Crisis on Startups' Performance: The~Role~of Resilience

Purpose: This paper aims to evaluate the impacts of the COVID-19 crisis on startups performance and the moderating effects played by several resilience-related startup characteristics during times of crisis. Design/methodology/approach: To achieve this, 94 Brazil-based startups were surveyed, and multivariate data techniques (PLS-SEM) were applied. Findings: The results show that despite the startups performance having been affected by the pandemic crisis, the response measures, when influenced by the resilience characteristics of these companies, moderated this effect. Furthermore, our findings suggest the future challenges to be faced by these organisations in the post-pandemic period. Research limitations/implications: Proposing a framework, our survey research contributes to the dynamic capabilities theory by showing that startups resilience is linked to the micro-foundations of sensing (e.g. innovation systems, resilience culture, pivoting practices, innovativeness products), seizing (e.g. leadership/focused skills, people development and selection, agility, clear vision of business process) and reconfiguring capabilities. Practical implications: Not only for theory, but this paper also contributes insights and guidelines for business practice in the face of challenges arising from times of crisis. By demonstrating the positive effect of early response measures based on resilience, our findings provide genuine managerial input that can help managers, funders and decision-makers in these companies operations against turbulent crises early on, thereby supporting the traction phase and sustaining their performance. Originality/value: Previous research has examined the effects of the COVID-19 crisis in several sectors and perspectives. However, this study is the first to empirically test and clarify how the resilience and singularities of these new business models based on innovation could react to the changes caused by the pandemic. \textcopyright 2022, Emerald Publishing Limited

The Role of Industry 4.0 in Developing Resilience for Manufacturing Companies during COVID-19

Humanity has faced many crises in the past, such as pandemics, wars, and economic crises, and other crises are certain to come in the future; however, emerging technologies have a role to play in improving companies' resilience in the face of such crises. The coronavirus (COVID-19) pandemic has led to human, technological, and managerial constraints for manufacturing companies due to scarce resources or supply chain (SC) disruptions. The research goal of this paper is to investigate whether Industry 4.0 implementation improved companies' resilience and whether companies' performance maintained stability during the COVID-19 outbreak. Composite-based structural equation modeling is applied to analyse data collected from 207 manufacturing companies. The theoretical model is grounded in the Practice-Based View (PBV) theory. The research findings show that operational responses based on Industry 4.0, smart manufacturing practices, and smart capabilities enable manufacturers to build resilience and quickly mitigate performance loss in times of global crisis. Therefore, the results demonstrate that Industry 4.0 implementation provides resilience for companies through flexibility, reliability, robustness, and responsiveness. The main practical implication of this study is to support managers in achieving manufacturing performance stability during disrupted times, such as the COVID-19 crisis, using Industry 4.0 approaches to make their companies more resilient and prepared to face future challenges and crises. \textcopyright 2022 Elsevier B.V