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The purpose of this paper is to explore the scope of robust dispersion control charts in a distribution-free environment, which is a specific case of non-normal control charts. These control charts are skewness-based structures designed to monitor skewed-type processes whilst equally performing under symmetric processes. Moreover, the choice of a suitable control chart for a particular non-normal situation is also suggested.

The probability control limits approach is considered as an alternative way to determine the skewness-based structure of dispersion control charts. The proposals of five robust and two conventional Shewhart-type dispersion control charts are suggested as efficient competitors of skewness correction (SC) dispersion control charts. The evaluation of robust proposals and competing dispersion control charts is done through false alarm rate (FAR) and probability to signal (PTS) measures.

The proposed dispersion control charts are found robust and efficient alternatives of SC dispersion control charts in both normal and non-normal distributions. The FARs and PTS properties of proposed control charts are impressive in all studied cases, and a real-data example also verifies the dominance of proposed control charts.

Conventional dispersion control charts quickly lose their efficiency as underlying process distribution deviates from normality; however, robust control charts emerge as most suitable candidates in such situations. This paper proposes the idea of robust dispersion control charts under a distribution-free structure for the skewed-type process, which is not yet explored.

This chapter analyzes the empirical performance of alternative option pricing models using Black and Scholes (1973) as a benchmark. Specifically, we consider the Heston (1993) and Corrado and Su (1996) models and price call options on the S&P 500 index over the period from November 2010 to April 2011, evaluating each model by computing in- and out-of-sample pricing errors. We find that the two proposed models reduce both types of errors and mitigate the smile effect with respect to the benchmark. Moreover, in most of the cases, the model in Corrado and Su (1996) beats that in Heston (1993). Then, we conclude that skewness and kurtosis matter for option pricing purposes.

This paper aims to enhance a co-skew-based risk measurement methodology initially introduced in Polimenis, by extending it for the joint estimation of the jump betas for two stocks.

The authors introduce the possibility of idiosyncratic jumps and analyze the robustness of the estimated sensitivities when two stocks are jointly fit to the same set of latent jump factors. When individual stock skews substantially differ from those of the market, the requirement that the individual skew is exactly matched is placing a strain on the single stock estimation system.

The authors argue that, once the authors relax this restrictive requirement in an enhanced joint framework, the system calibrates to a more robust solution in terms of uncovering the true magnitude of the latent parameters of the model, at the same time revealing information about the level of idiosyncratic skews in individual stock return distributions.

Allowing for idiosyncratic skews relaxes the demands placed on the estimation system and hence improves its explanatory power by focusing on matching systematic skew that is more informational. Furthermore, allowing for stock-specific jumps that are not related to the market is a realistic assumption. There is now evidence that idiosyncratic risks are priced as well, and this has been a major drawback and criticism in using CAPM to assess risk premia.

Since jumps in stock prices incorporate the most valuable information, then quantifying a stock's exposure to jump events can have important practical implications for financial risk management, portfolio construction and option pricing.

This approach boosts the “signal-to-noise” ratio by utilizing co-skew moments, so that the diffusive component is filtered out through higher-order cumulants. Without making any distributional assumptions, the authors are able not only to capture the asymmetric sensitivity of a stock to latent upward and downward systematic jump risks, but also to uncover the magnitude of idiosyncratic stock skewness. Since cumulants in a Levy process evolve linearly in time, this approach is horizon independent and hence can be deployed at all frequencies.

The purpose of this paper is to examine the relationship between participatory HRM and firm performance through a series of mediators.

Primary data were collected from 569 respondents belonging to 207 organizations operating in India. Structural equation modeling and bootstrapping via PROCESS were used to analyze the hypothesized relationships between participatory HRM and firm performance.

The study has highlighted that participatory HRM in the form of self-managed teams, flexible work arrangements and empowerment results in better organizational climate, heightened affective commitment, reduced intention to leave and enhanced firm performance. Furthermore, it has been established that organizational climate, affective commitment and intention to leave serially mediate the relationship between participatory HRM and firm performance.

The study gives strong indications that adopting bundle of participatory HRM practices is beneficial for generating positive organizational climate, enhanced employee attitudes and superior firm performance.

By establishing serial mediation through organizational climate, affective commitment and employees’ intention to leave, this study brings new insights into the interpretation of underlying mechanism existing between participatory HRM and firm performance, thus uniquely contributes to the HRM and OB literature.

The purpose of this paper is to provide a decision support model for the early diagnosis of housing bubbles in the UK during the maturity process of the phenomenon.

The development process of the model is divided into four stages. These stages are driven by the normal distribution theorem coupled with the case study approach. The application of normal distribution theory is allowed through the usage of several parametric tools. The case studies tested in this research include the last two UK housing bubbles, 1986 to 1989 and 2001/2002 to 2007. The central hypothesis of the model is that during housing bubbles, all speculative activities of market participants follow an approximate synchronisation, and therefore, an irrational, synchronous and periodic increase on a wide range of relevant variables must occur to anticipate the bubble component. An empirical application of the model is conducted on UK housing market data over the period of 1983-2011.

The new approach successfully identifies the well-known UK historical bubble episodes over the period of 1983-2011. The study further determines that for uncovering housing bubbles in the UK, house price changes have the same weight with the debt–burden ratio when their velocity is positive. Finally, the application of this model has led us to conclude that the model’s outputs fluctuate approximately in line with phases of the UK real estate cycle.

This paper proposes a new measure for studying the presence of housing bubbles. This measure is not simply an ex post detection technique but dating algorithms that use data only up to the point of analysis for an on-going bubble assessment, giving an early warning diagnostic that can assist market participants and regulators in market monitoring.

The variability of the results of a visual control is often high. This paper aims to propose a new tool to give information about what improvement actions can be carried out to reduce this variability.

The variability of a visual control can be measured by Kappa's Fleiss which measures the level of agreement between appraisers and experts. The R&R Gage is then classically used to give information about corrective actions which can be carried out in order to improve this level of agreement. The paper demonstrated that this information is not always sufficient.

By considering the two essential steps of a visual control (exploration and evaluation), the R2&E2 Gage proposed gives more precise information about the improvement actions to carry out to reduce the variability of a visual control. Repeatability and reproducibility, for detection and evaluation purposes, are considered separately.

This R2&E2 gage is one result of a European research program called INTERREG. The aim of this program, which brings together two laboratories from the University of Savoy and EPFL, two institutional partners (CTDEC and CETEHOR) and some Swiss and French industrial companies, is to create methodological support and the tools needed to improve the visual control of high added‐value products.

This R2&E2 gage has been used in six industrial companies involved in the European program INTERREG. Significant improvement of the visual control has been observed over a short time.

The paper fulfils an identified need of industrial firms to have efficient tools improving the visual control of their products.

This study argues that strategic information systems (SISs) are necessary for organizations’ survival and corporate performance in turbulent economic environments. Applying Miles and Snow’s strategy typology, the purpose of this paper is to explore how SIS supports business strategy and corporate performance.

This study uses quantitative survey data from 389 Brazilian companies during economic crises and analyzes them using structural equation modeling.

There is strong evidence that SIS promotes capacity and flexibility to create competitive strategies in response to environmental changes. SIS significantly and positively predicts firms’ use of prospector strategies, reducing the need to sacrifice efficiency for innovation. SIS can predict corporate performance more strongly than firms’ strategic orientations can.

The results provide organizations insights on how SIS enables strategic planning processes to create competitive strategy and improve performance during economic turbulence.

This research demonstrates SIS’s positive effects during economic turbulence on competitive strategy and performance, revealing that corporate performance is influenced more by SIS (strategic process) than strategic orientation (content). Hence, this study fills a research gap in the information systems strategy literature by contributing new insights about SIS.

The purpose of this paper is to examine the relationships between the higher moments of returns (realized skewness and kurtosis) and subsequent returns at the industry level, with a focus on both empirical predictability and practical application via trading strategies.

Daily returns for 48 US industries over the period 1970–2019 from Kenneth French’s data library are used to calculate the higher moments and to construct short- and medium-term single-sort trading strategies. The analysis adjusts returns for common risk factors (market, size, value, investment, profitability and illiquidity) to confirm whether conventional asset pricing models can capture these relationships.

Past skewness positively relates to subsequent industry returns and this relationship is unexplained by common risk factors. There is also a time-varying effect in which the predictive role of skewness is much stronger over business cycle expansions than recessions, a result consistent with varying investor optimism. However, there is no significant relationship between kurtosis and subsequent industry returns. The analysis confirms robustness using both value- and equal-weighted returns.

The calculation of realized moments conventionally uses high-frequency intra-day data, regrettably unavailable for industries. In addition, the chosen portfolio-sorting method may omit some information, as it compares only average group returns. Nonetheless, the close relationship between skewness and future returns at the industry level suggests variations in returns unexplained by common risk factors. This enriches knowledge of market anomalies and questions yet again weak-form market efficiency and the validity of conventional asset pricing models. One suggestion is that it is possible to significantly improve the existing multi-factor asset pricing models by including industry skewness as a risk factor.

Given the relationship between skewness and future returns at the industry level, investors may predict subsequent industry returns to select better-performing funds. They may even construct trading strategies based on return distributions that would generate abnormal returns. Further, as the evaluation of individual stocks also contains industry information, and stocks in industries with better performance earn higher returns, risks related to industry return distributions can also shed light on individual stock picking.

While there is abundant evidence of the relationships between higher moments and future returns at the firm level, there is little at the industry level. Further, by testing whether there is time variation in the relationship between industry higher moments and future returns, the paper yields novel evidence concerning the asymmetric effect of stock return predictability over business cycles. Finally, the analysis supplements firm-level results focusing only on the decomposed components of higher moments.

Using data from Turkey, this paper seeks to investigate whether relative price changes can help to explain the Phillips Curve relationship between inflation and output.

Building on work by Ball and Mankiw, the paper includes measures of the variance and skews of relative price adjustment in an otherwise standard model of the Phillips Curve. It employs a bounds‐testing approach based on an ARDL model to establish long‐run relationships. It then uses error correction models to analyze short‐run dynamics.

No evidence was found for a long‐run relationship between inflation and output. However, a long‐run relationship is in fact found, once the variance and skew of relative price changes are included as regressors. The error correction model implies plausible short‐term dynamics in this case.

This paper combines two distinct literatures, on the Phillips Curve and on the distribution of relative price changes, showing that insights from the latter can be essential in constructing coherent models of the Philips Curve.

We survey the literature on the Risk Augmented Mincer equation that seeks to estimate the compensation for uncertainty in the future wage to be earned after completing an education. There is wide empirical support for the predicted positive effect of wage variance and the negative effect of wage skew. We discuss robustness of the findings across specifications, potential bias from unobserved heterogeneity and selectivity and consider the core issue of students' information on benefits from education.