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This study aims to examine whether the methodology proposed by the European Supervisory Authorities (ESAs) within Delegated Regulation (European Union) 2017/653 for the calculation of market risk of certain packaged retail and insurance-based investment products (PRIIPs) is the most appropriate.

Risk models are put into effect to validate the appropriateness of the methodology announced by ESAs. ESAs have announced that the unit-linked (UL) products, labeled as Category II PRIIPs, will be subject to the Cornish–Fisher value-at-risk (CFVaR) methodology for their market risk assessment. We test CFVaR at 97.5% confidence level on 70 UL products, and we test Cornish–Fisher expected shortfall (CFES) at the same confidence level, which acts as a counter methodology for CFVaR.

The paper provides empirical insights about the Cornish-Fisher (CF) expansion being a method that incorporates the possibility of financial instability. When CFVaR by ESAs is calculated, it is shown that CF is in general a more robust risk model than the simpler historical ones. However, when CFES is applied, important points are derived. First, only in half of the occasions the CF expansion can be considered as a reliable method. Second, the CFES is a more coherent risk measure than CFVaR. We conclude that the CF expansion is unable to accurately estimate the market risk of UL products when excessive fat-tailed or non-symmetrical distributions are present. Hence, we suggest that a different methodology could also be considered by the regulatory bodies which will capture the excessive values of products in financial distress.

Literature, both theoretical and applied, regarding PRIIPs, is not extended. Although business and regulators research has begun to intensify in the last two years, to our knowledge this is one of the first studies that uses the CFES methodology for market risk assessment of Category II PRIIPs. In addition, we use a unique data set from a country in the headwinds of the recent financial crisis. This research contributes both to the academic and business community by enriching the existing literature and aiding risk managers in assessing the market risk of certain Category II PRIIPs. Considering the recent efforts of the regulatory authorities at the beginning of 2020 to implement certain amendments to the PRIIPs, we indicate relative risks related with the calculation of the market risk of the aforementioned products. Our findings could contribute to regulatory authorities’ persistent efforts in wrapping up this ongoing project.

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.

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.

We examine the incremental cross-sectional role of several common risk factors related to size, book-to-market, and momentum in size-and-momentum-sorted portfolios. Unlike the existing literature, which focuses on the conditional mean specification only, we evaluate the common risk factors’ incremental explanatory power in the cross-sectional characterization of both average return and conditional volatility. We also investigate the role of ex-ante market risk in the cross-section. The empirical results demonstrate that the size-and-momentum-based risk factors explain a significant portion of the cross-sectional average returns and cross-sectional conditional volatility of the benchmark equity portfolios. We find that the Fama–French (1993) factors and the ex-ante market risk are priced in the cross-sectional conditional volatility. We conclude that the size-and-momentum-based factors provide a source of risk that is independent of the Fama–French factors as well as ex-post and ex-ante market risk. Our results bolster the risk-based explanation of the size and momentum effects.

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 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.

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.

As an essential component of Islamic governance for ensuring religious compliance, Shari’ah annual reports (SARs) play an important role in providing communication between Shari’ah board (SB) members and stakeholders. This paper aims to determine the ethical disclosure in SARs to identify how close the Shari’ah disclosure to the standards set by AAOIFI and also substantive morality of Islam. The research also aims to examine the factors determining disclosure performance.

Two disclosure indices are developed to generate data from the SARs: the AAOIFI standards for Shari’ah governance index for form related approach, an Islamic ethicality augmented index reflecting on substantive morality approach. The sample consists of 41 Islamic banks from 15 different countries for the period of 2007–2014. Sampled 305 SARs were examined through disclosure analysis in line with the two indices developed for this study. The econometric analysis was run to identify the factors determining disclosure performance.

The findings suggest that AAOIFI guidelines have an influence on the level of disclosure, even if Islamic banks have not adopted them. However, the level of disclosure for the ethically augmented index is found to be very limited with reliance on general statements in most of the cases. As part of determining factors, the popularity of Shari’ah scholars is significant for both indices, while the existence of an internal Shari’ah auditing department holds some explanatory power. The adoption of AAOIFI standards at the country level, the regulatory quality and the duration of Sharīʿah-compliance are particularly deterministic factors in terms of complying with AAOIFI standards for SARs.

Although SB is the most crucial division of corporate governance in Islamic banks in terms of securing the “Islamic” identity of these institutions, their most important communication instrument, namely, SAR, has not been explored sufficiently, alongside an insufficient attempt to constitute Islamic corporate governance. Initially, this study attempted to constitute an Islamic corporate governance framework as a theoretical construct, which provides context for the empirical part of the research and this should be considered a novel approach. Second, the empirical part of the research aims to fill the gap observed in the literature such as small sample size and index construction-related matters. This research is conducted with a larger sample size as compared to the available studies in the literature and it has developed two indices for disclosure analysis along with developing an Islamic morality-based index beside an index based on AAOIFI standards.

Purpose – The purpose of this chapter is to estimate non-Gaussian distributions by means of Johnson distributions. An empirical illustration on hedge fund returns is detailed.

Methodology/approach – To fit non-Gaussian distributions, the chapter introduces the family of Johnson distributions and its general extensions. We use both parametric and non-parametric approaches. In a first step, we analyze the serial correlation of our sample of hedge fund returns and unsmooth the series to correct the correlations. Then, we estimate the distribution by the standard Johnson system of laws. Finally, we search for a more general distribution of Johnson type, using a non-parametric approach.

Findings – We use data from the indexes Credit Suisse/Tremont Hedge Fund (CSFB/Tremont) provided by Credit Suisse. For the parametric approach, we find that the SU Johnson distribution is the most appropriate, except for the Managed Futures. For the non-parametric approach, we determine the best polynomial approximation of the function characterizing the transformation from the initial Gaussian law to the generalized Johnson distribution.

Originality/value of chapter – These findings are novel since we use an extension of the Johnson distributions to better fit non-Gaussian distributions, in particular in the case of hedge fund returns. We illustrate the power of this methodology that can be further developed in the multidimensional case.