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This paper aims to introduce a heteroskedastic hidden truncation normal (HTN) model that allows for conditional volatilities, skewness and kurtosis, which evolve over time and are linked to economic dynamics and have economic interpretations.

The model consists of the HTN distribution introduced by Arnold et al. (1993) coupled with the NGARCH type (Engle and Ng, 1993). The HTN distribution nests two well-known distributions: the skew-normal family (Azzalini, 1985) and the normal distributions. The HTN family of distributions depends on a hidden truncation and has four parameters having economic interpretations in terms of conditional volatilities, kurtosis and correlations between the observed variable and the hidden truncated variable.

The model parameters are estimated using the maximum likelihood estimator. An empirical application to market data indicates the HTN-NGARCH model captures stylized facts manifested in financial market data, specifically volatility clustering, leverage effect, conditional skewness and kurtosis. The authors also compare the performance of the HTN-NGARCH model to the mixed normal (MN) heteroskedastic MN-NGARCH model.

The paper presents a structure dynamic, allowing us to explore the volatility spillover between the observed and the hidden truncated variable. The conditional volatilities and skewness have the ability at modeling persistence in volatilities and the leverage effects as well as conditional kurtosis of the S&P 500 index.

This chapter takes a closer look at outliers and extreme outliers identified in the data derived from a complete case treatment of missing values in the European and North American datasets and consistently observe significant negatively skewed distributions with high excess kurtosis across all industries. We then plot the density functions for return on assets (ROA) across different industries in the two datasets and find pervasive observations in the tails where negative returns and outlying observations constitute a frequent and recurring phenomenon. We analyze the persistency of outliers and find noticeable percentages of outlying over- and underperformers hovering around 3–6% dependent on industry context. We further analyze potential size effects associated with extreme negative skewness but do not find that (even sizeable) elimination of extreme values reduce the phenomenon. Finally, we analyze the percentage of firm observations that must be eliminated to reach at distributions that fulfill the characteristics of a normal distribution and reach at a substantial percentage of around 5–10% dependent on industry. To conclude, the often-assumed normally distributed performance outcomes are typically wrong and discards the substantial number of outliers in the samples.

This chapter first analyzes how the data-cleaning process affects the share of missing values in the extracted European and North American datasets. It then moves on to examine how three different approaches to treat the issue of missing values, Complete Case, Multiple Imputation Chained Equations (MICE), and K-Nearest Neighbor (KNN) imputations affect the number of firms and their average lifespan in the datasets compared to the original sample and assessed across different SIC industry divisions. This is extended to consider implied effects on the distribution of a key performance indicator, return on assets (ROA), calculating skewness and kurtosis measures for each of the treatment methods and across industry contexts. This consistently shows highly negatively skewed distributions with high positive excess kurtosis across all the industries where the KNN imputation treatment creates results with distribution characteristics that are closest to the original untreated data. We further analyze the persistency of the (extreme) left-skewed tails measured in terms of the share of outliers and extreme outliers, which shows consistent and rather high percentages of outliers around 15% of the full sample and extreme outliers around 7.5% indicating pervasive skewness in the data. Of the three alternative approaches to deal with missing values, the KNN imputation treatment is found to be the method that generates final datasets that most closely resemble the original data even though the Complete Case approach remains the norm in mainstream studies. One consequence of this is that most empirical studies are likely to underestimate the prevalence of extreme negative performance outcomes.

As the crucial support component of the propeller power system, the reliability of the operation of submersible pumps is influenced by the lubrication performance of water-lubricated thrust bearings. When the water-lubricated thrust bearings are under start-stop or heavy load conditions, the effect of surface morphology is crucial as the mixed lubrication regime is encountered. This paper aims to develop one mixed lubrication model for the water-lubricated thrust bearings to predict the effects of surface skewness, kurtosis and roughness orientation on the loading carrying capacity and tribological behavior.

This paper developed one improved mixed lubrication model specifically for the water-lubricated thrust bearing system. In this model, the hydrodynamic model was improved by using the height of the rough surface and its probability density function, combined with the average flow model. The asperity contact model was improved by using the equation for the Pearson system of frequency curves to characterize the non-Gaussian aspect of surface roughness distribution.

According to the results, negative skewness, large kurtosis and lateral surface pattern can improve the tribological performance of water-lubricated thrust bearings. Optimizing the surface morphology is a reasonable design method that can improve the performance of water-lubricated thrust bearings.

In this paper, one mixed lubrication model specifically for the water-lubricated thrust bearing with the effect of surface roughness into consideration was developed. Based on the developed model, the effect of surface morphology on tribological behavior can be evaluated.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0247/

In this chapter, we perform more detailed analyses and present the distribution characteristics and risk-return relationships of accounting-based financial returns (ROA) across different industry contexts and between periods with different economic conditions. We first display the frequency diagrams of the return measure (ROA) and its two components, net income and total assets, that show entirely different contours in the density graphs that must be reconciled. This is partially accomplished by analyzing the skewness, kurtosis, cross-sectional, and longitudinal risk-return characteristics of each of the three variables. The analyses further considers potential effects of accounting manipulation, and different organizational and executive traits, that identifies significant effects on the accounting-based return measures. We find extremely left-skewed return distributions with high negative correlations between the average return and risk measures, which reproduces the “Bowman paradox” as originally conceived. The same analysis is performed on net income and operating cash flows, the latter being less susceptible to accounting manipulation, which should display similar effects even though these performance distributions show positive skewness. We find negative but insignificant cross-sectional risk-return relations that nevertheless reappear in analyses performed within the specific industry contexts. The study further uncovers effects from prevailing economic conditions where left-skewness and kurtosis as well as negative risk-return correlations are much more significant during periods of high economic growth and business expansion where competition is more pronounced.

The purpose of this study is to unfold the existing information channel in the higher moments of currency futures for different time horizons.

The authors use a quasi-Bayesian local likelihood approach within a time-varying parameter vector autoregression (TVP-VAR) framework and a dynamic connectedness measure to study the volatility, skewness and kurtosis of most traded currency futures.

The authors’ results suggest a time-varying presence of dynamic connectedness within higher moments of currency futures. Most spillovers pertain to shorter time horizons. The authors find that in net terms, CHF, EUR and JPY are the most important contributors to the system, while the authors emphasize that the role of being a transmitter or a receiver varies for pairwise interactions and time windows.

To the best of the authors’ knowledge, this is the first study that looks upon the connectivity vis-á-vis uncertainty, asymmetry and fat tails in currency futures within a dynamic Bayesian paradigm. The authors extend the current literature by proposing new insights into asset distributions.

This study aims to use a machine learning (ML) model for the prediction of traction coefficient and asperity load ratio for different surface topographies of non-conformal rough contacts.

The input data set for the ML model is generated using a mixed-lubrication model. Surface topography parameters (skewness, kurtosis and pattern ratio), rolling speed and hardness are used as input features in the multi-layer perceptron (MLP) model. The hyperparameter tuning and fivefold cross-validation are also performed to minimize the overfitting.

From the results, it is shown that the MLP model shows excellent accuracy (R² > 90%) on the test data set for making the prediction of mixed lubrication parameters. It is also observed that engineered rough surfaces with high negative skewness, low kurtosis and isotropic surface patterns exhibit a significant low traction coefficient. It is also concluded that the MLP model gives better accuracy in comparison to the random forest regression model based on the training and testing data sets.

Mixed lubrication parameters are predicted by developing a regression-based MLP model. The machine learning model is trained using several topography parameters, which are vital in the mixed-EHL regime because of the lack of regression-fit expressions in previous works. The accuracy of MLP with random forest models is also compared.

This study aims to determine how changes in macroeconomic indicators and the housing prices index (HPI) are related. These factors can cause short-term and long-term changes in the housing market in Spain.

The study used cointegrating regression, fully modified ordinary least squares and dynamic ordinary least squares methodologies. The models are trained using quarterly time series data for these parameters from 2010 to 2022. A comprehensive examination is conducted to explore the relationship between macroeconomic issues and fluctuations in the HPI.

The results indicate statistically significant short-run effects (p < 0.05) of economic growth, inflation, Spanish stock indices, foreign trade and the interest rate on HPI. The inflation variables, Spain’s stock indices, interest rate and monetary rate, have statistically significant long-run effects (p < 0.05) on HPI. The exchange rate, unemployment and money supply have no substantial impact on HPI in Spain.

The study’s findings significantly contribute to increased information concerning the level of investing activity in the Spanish housing sector. After conducting an in-depth study of both the long-run and short-run connections with HPI, the study proved to be highly effective in formulating appropriate policies.

In this chapter, we first examine the distribution characteristics of firm performance across different competitive industry contexts and periodic economic conditions of growth, recession, and recovery. There is mounting evidence that the contours of accounting-based economic returns consistently display (extreme) left-skewed leptokurtic distributions with negative risk-return relationships, which implies the existence of many negative performance outliers and some positive outliers. We note how negative skewness, excess kurtosis, and inverse risk-return relationships prevail in industries with more intense competition and in economic growth scenarios where more innovative initiatives compete. As the study of outliers typically is ignored in mainstream management studies, we extract a total of 23 extreme performers using a conventional winsorization technique that identifies 16 negative and 7 positive outliers. We study the performance trajectories of these firms over the full period and find that negative performers typically operate in capital-intensive innovative industries whereas positive performers operate in activities that cater to prevailing demand conditions and expand the business in a balanced manner. The firms that under- and over-perform as measured by the financial return ratio both constitute smaller firms compared to the total sample and show how relative movements in the ratio numerator and denominator affect the recorded return measure. However, the negative outliers generally use their public listing to access capital for investment in more risky development efforts that require a certain scale to succeed and thereby limits their flexibility. The positive outliers appear to expand their business activities in incremental responses to evolving market demands as a way to enhance maneuverability and secure competitive advantage by honing their unique firm-specific capabilities.

This study investigates the efficiency of value relevance and faithful representation when stock market price derivates from its firm value to the investigated IT companies listed in FTSE Taiwan 50. The empirical investigation reveals one financial indicators: Return on equity (ROE) has explanatory ability among seven financial indicators, earnings per share (EPS), book value (BV), dividend yield (Div.), price–earnings ratio (P/E), ROE, return on assets (ROA), and return on operating asset (ROOA) to both sampled companies, United Microelectronics Corporation, UMC, (2303) and Taiwan Semiconductor Manufacturing Company Limited, TSMC, (2330). Furthermore, the empirical results indicate that the higher order moments, skewness and kurtosis, of price deviation do not provide a reliable prediction or explanatory power for stock price trends.