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The purpose of this paper is to bridge together seemingly disparate yet interconnected paradigmatic antecedents of e-tailing and servicescape, i.e., product assortment, order fulfillment, shopping assistance and its consequences for shopping efficiency.

The proposed conceptual model is well grounded in the extensive literature from e-tailing as well as retailing domain and to assess the plausibility of the model. Total 246 female online apparel shoppers were surveyed from an Indian university and the data were analyzed using structural equation modeling through SmartPLS.

The outcomes of the study indicate that the e-customer may derive a substantial share of shopping assistance and service interface through product assortment offered by e-tailing sites. Customer-perceived performance of this e-shopping process – a crucial element of e-tail servicescape – directly affects the shopping assistance, along with order fulfillment capability of retail scope.

The study used a sample of graduate students at a north-west university in India, which limits the generalizability of the research to other consumer groups. The paper links a significant body of literature within a conceptually developed framework and identifies key research areas in the e-tailing realm.

By better understanding the role of product assortment as a value-added feature in online value co-creation process, the e-tail managers can leverage the proposed integrated capability to improve e-tailing performance and customer outcomes in the form of business.

With rapid advancements in internet-led communication, we are witnessing the dawn of a new era of e-tail innovations around us which is expected to change the way people experience shopping.

This research is an attempt to enrich the level of understanding about online shopping environment in light of relationships among virtual and physical facets of e-tail, i.e., product assortment, order fulfillment, shopping assistance and shopping efficiency. The authors investigate customer-perceived product assortment performance in e-tailing and its significances on shopping outcomes.

The purpose of this paper is to determine dependencies between a rotor-blade shape and a rotor performance as well as to search for optimal shapes of blades dedicated for helicopter main and tail rotors.

The research is conducted based on computational methodology, using the parametric-design approach. The developed parametric model takes into account several typical blade-shape parameters. The rotor aerodynamic characteristics are evaluated using the unsteady Reynolds-averaged Navier–Stokes solver. Flow effects caused by rotating blades are modelled based on both simplified approach and truly 3D simulations.

The computational studies have shown that the helicopter-rotor performance may be significantly improved even through relatively simple aerodynamic redesigning of its blades. The research results confirm high potential of the developed methodology of rotor-blade optimisation. Developed families of helicopter-rotor-blade airfoils are competitive compared to the best airfoils cited in literature. The finally designed rotors, compared to the baselines, for the same driving power, are characterised by 5 and 32% higher thrust, in case of main and tail rotor, respectively.

The developed and implemented methodology of parametric design and optimisation of helicopter-rotor blades may be used in future studies on performance improvement of rotorcraft rotors. Some of presented results concern the redesigning of main and tail rotors of existing helicopters. These results may be used directly in modernisation processes of these helicopters.

The presented study is original in relation to the developed methodology of optimisation of helicopter-rotor blades, families of modern helicopter airfoils and innovative solutions in rotor-blade-design area.

The purpose of the paper is to back-test value-at-risk (VaR) models for conditional distributions belonging to a Generalized Hyperbolic (GH) family of Lévy processes – Variance Gamma, Normal Inverse Gaussian, Hyperbolic distribution and GH – and compare their risk-management features with a traditional unconditional extreme value (EV) approach using data from future contracts return data of S&P500, FTSE100, DAX, HangSeng and Nikkei 225 indices.

The authors apply tail-based and Lévy-based calibration to estimate the parameters of the models as part of the initial data analysis. While the authors utilize the peaks-over-threshold approach for generalized Pareto distribution, the conditional maximum likelihood method is followed in case of Lévy models. As the Lévy models do not have closed form expressions for VaR, the authors follow a bootstrap method to determine the VaR and the confidence intervals. Finally, for back-testing, they use both static calibration (on the entire data) and dynamic calibration (on a four-year rolling window) to test the unconditional, independence and conditional coverage hypotheses implemented with 95 and 99 per cent VaRs.

Both EV and Lévy models provide the authors with a conservative proportion of violation for VaR forecasts. A model targeting tail or fitting the entire distribution has little effect on either VaR calculation or a VaR model’s back-testing performance.

To the best of the authors’ knowledge, this is the first study to explore the back-testing performance of Lévy-based VaR models. The authors conduct various calibration and bootstrap techniques to test the unconditional, independence and conditional coverage hypotheses for the VaRs.

This chapter outlines the major analytical efforts performed as part of the overarching research project with the aim to investigate the organizational and environmental circumstances around the extreme negatively skewed performance outcomes regularly observed across firms. It presents the collection and treatment of comprehensive European and North American datasets where subsequent analyses reproduce the contours of performance distributions observed in prior empirical studies. Key theoretical perspectives engaged in prior studies of performance data and the implied risk-return relationships are presented and these point to emerging commonalities between empirical findings in the management and finance fields. The results from extended analyses of more fine-grained data from North American manufacturing firms uncover the subtle effects of leadership and structural features, and computational simulations demonstrate how the implied adaptive processes can lead to the empirically observed performance distributions. Finally, the findings from the analytical project activities are set in context and the implications of the observed results are discussed to reach at a final conclusion.

The global environments that surround contemporary business activities are uncertain, fast-changing, and frequently exposed to abrupt unexpected events with the potential to inflict extreme impacts where the ability to respond and adapt the organization effectively becomes a primary strategic concern. However, various firms that operate across diverse industry contexts approach this adaptive challenge in distinct ways that lead to quite diverse outcomes with many negative performers and some high performers with positive risk features. The heterogeneous approaches appear to consistently form extreme left-tailed performance distributions with inverse risk-return features but we are not really able to explain why and how these regularly observed phenomena come about. Hence, we want to study these organizational artifacts by collecting an extensive updated dataset to test the proposed relationships, explore alternative explanations, and learn from the extreme exemplars often referred to as outliers. There are extensive literatures in (strategic) management and finance that have dealt with the distribution of firm returns from slightly different angles but with some emerging commonalities that can inspire further analyses of the performance data. As a precursor for this, we discuss the odds of effective strategic adaptation in complex dynamic environments and introduce resilience as a proper outcome when simple solutions are scarce, and consider conditions that may facilitate these aims. The premises for the ensuing analyses are laid out and the main contents of the following chapters are presented.

In view of a disruptive environment, the authors consider theories that explain left-skewed performance outcomes and inverse risk–return relationships where some rationales imply causal dependencies with slightly differing outcomes while others refer to spurious artifacts. These literatures are briefly outlined and dynamic response capabilities introduced as an alternative perspective expressed as strategic responsiveness where commonly observed performance outcomes derive from heterogeneous response capabilities among firms that compete in dynamic environments. Financial performance outcomes are analyzed empirically based on a comprehensive corporate dataset where computational simulations of adaptive strategy making among firms generate comparable outcomes from a simple strategic responsiveness model. The findings demonstrate how diverse adaptive strategy-making processes can generate a substantial part, if not all, of the commonly observed artifacts of firm financial performance. The implications of these results are discussed pointing to propitious approaches of analyzing the impact of dynamic adaptive strategies.

Even though Bitcoin has been often labelled as a safe haven asset class in the literature, the influence of economic policy uncertainty (EPU) on the diversifying opportunities offered by Bitcoin in relation to other assets needs to be investigated. This paper aims to investigate how the EPU affects diversification of commodity, conventional, Islamic and sustainable equity returns in relation to its impact on Bitcoin returns.

The authors use advanced time-series econometrics, namely, multivariate generalized autoregressive conditional heteroscedastic-dynamic conditional correlation and continuous wavelet transformation, for the analysis of the daily returns for the aforementioned assets between 01 August 2011 and 01 September 2019.

First, the authors found a strong evidence of Bitcoin’s mean reverting trend in the long run while its volatility has decreased significantly since 2013. After separating the EPU into two regimes (high and low), diversification opportunities with Bitcoin seems to disappear in a high EPU period, while the hedging opportunity tends to prevail in a low EPU period for all classes of assets. Importantly, the findings indicate that Bitcoin offers short-term diversification for sustainable and Islamic equity as well as energy stocks during a low uncertainty period. Consequently, in relation to the policy uncertainty, Bitcoin provides similar hedging opportunities than commodities like Gold and Silver. Overall, the study shows that EPU is remarkably important in explaining the average portfolio returns of Bitcoin, suggesting that this indicator can be perceived as a decent explanatory factor for portfolio diversification.

The study significantly extends the empirical literature of Bitcoin’s portfolio diversification by taking EPU into consideration. To the best of authors’ knowledge, this is one of the few studies to investigate the asymmetric effects of US EPU on Bitcoin’s hedging capabilities by taking into account major conventional equity, sustainable equity, Islamic equity, gold, silver and oil.

The authors apply their method to analyze which portfolios are capable of providing superior performance to those based on the Sharpe ratio (SR).

In this paper the authors illustrate the use of conditional copulas for identifying differences in alternative portfolio performance strategies. The authors analyze which portfolios are capable of providing superior performance to those based on the SR.

The results show that under the Gaussian copula, both expected tail ratio (ETR) and skewness-kurtosis ratio portfolios exhibit remarkably low correlations respecting the SR portfolio. This means that these two portfolios are different respecting the SR one. The authors also find that copulas which focus on either the upper tail (Gumbel) or the lower tail (Clayton) render significant differences. In short, the copula analysis is useful to understand what kind of equity-screening strategy based on its corresponding performance measure (PM) performs better in relation to the SR portfolio.

Copula methods for evaluating relative tail forecasting performance provide an alternative tool when forecast differences are very small or found non statistically significant through standard tests.

Our copula methods to evaluate models' performance differences are significant because when models' performance is rather similar, conclusions on statistical differences, can be defective as they may hinge on the subsample type or size used, leading to inefficient investment decisions. Our method based in copula is novel in this research topic.

The paper aims to investigate the relative performance of the most popular value‐at‐risk (VaR) estimates with an emphasis on the extreme value theory (EVT) methodology for seven Middle East and North Africa (MENA) countries.

The paper calculates tails distributions of return series by EVT. This allows computing VaR and comparing the results with Variance‐Covariance method, Historical simulation, and ARCH‐type process with normal distribution, Student‐t distribution and skewed Student‐t distribution. The paper assesses the performance of the models, which are used in VaR estimations, based on their empirical failure rates.

The empirical results demonstrate that the return distributions of the MENA markets are characterized by fat tails which implies that VaR measures relies on the normal distribution will underestimate VaR. The results suggest that the extreme value approach, by modeling the tails of the return distributions, are more relevant to measure VaR in most of the MENA.

The results show that the use of conventional methodologies such as the normal distribution model to estimate the financial market risk in MENA countries may lead to faulty estimation of risk in the world of volatile markets.

The paper tried to fill the gap in the literature and perform an evaluation of the relative performance of the most popular VaR estimates with an emphasis on the EVT methodology in seven MENA emerging stock markets. A comparison of the performance between EVT and other VaR techniques should support the decision whether more or less sophisticated methods are appropriate in order to assess stock market risks in the MENA countries.

Prior research has documented inconclusive and/or mixed empirical evidence on the timing performance of hybrid funds. Their performance inferences generally do not efficiently control for fixed-income exposure, conditioning information, and cross-correlations in fund returns. This study examines the stock and bond timing performances of hybrid funds while controlling and accounting for these important issues. It also discusses the inferential implications of using alternative bootstrap resampling approaches.

We examine the stock and bond timing performances of hybrid funds using (un)conditional multi-factor benchmark models with robust estimation inferences. We also rely on the block bootstrap method to account for cross-correlations in fund returns and to separate the effects of luck or sampling variation from manager skill.

We find that the timing performance of portfolios of funds is neutral and sensitive to controlling for fixed-income exposures and choice of the timing measurement model. The block-bootstrap analyses of funds in the tails of the distributions of stock timing performances suggest that sampling variation explains the underperformance of extreme left tail funds and confirms the good and bad luck in the bond timing management of tail funds. We report inference changes based on whether the Kosowski et al. or the Fama and French bootstrap approach is used.

This study provides extensive and robust evidence on the stock and bond timing performances of hybrid funds and their sensitivity based on (un)conditional linear multi-factor benchmark models. It examines the timing performances in the extreme tails funds using the block bootstrap method to efficiently identify (un)skilled fund managers. It also highlights the sensitivity of inferences to the choice of testing methodology.