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High-dimensional dependence modeling remains a crucial challenge in quantitative project cost and time risk analysis. Building a complete and mathematically consistent correlation matrix becomes unrealistically restrictive as the number of uncertain performance units in a project (i.e., activity times and costs) increases, regardless of using empirical data or with subjective judgment. This article presents a pair of additive factor dependence models that provide analytic solutions to the generation of a complete and mathematically consistent correlation matrix. The additive risk factor (ARF) models account for multiple risk factors in two classes (i.e., extra-marginal and intramarginal) while providing additional flexibility for a strategic tradeoff between the accuracy and the scalability to high-dimensional project risks. We extend the ARF models to present an analytic solution to the program evaluation and review technique (PERT) problem with correlated activity times. Numerical examples demonstrate the accuracy and computational efficiency of the ARF approaches. The ARF approaches and the ARF-PERT would serve as a quick and sensible alternative to large-scale Monte Carlo simulation, in particular during the early stage of the project life-cycle.

Firms in global markets often belong to business groups. We argue that this feature can have a profound influence on international asset pricing. In bad times, business groups may strategically reallocate risk across affiliated firms to protect core “central firms.” This strategic behavior induces co-movement among central firms, creating a new intertemporal risk factor. Based on a novel data set of worldwide ownership for 2002–2012, we find that central firms are better protected in bad times and that they earn relatively lower expected returns. Moreover, a centrality factor augments traditional models in explaining the cross section of international stock returns.

Speed is a critical risk factor, which makes its management the central point of the Vision Zero approach. Driving speed is influenced by speed choice, and in turn by the perception of the road parameters, as well as by the characteristics of drivers. Credible speed limits and self-explaining roads have been suggested as promising countermeasures; however, these rather theoretical concepts have only rarely been operationalized and quantified. The aim of this study was to investigate the relationships between speed indicators (official speed limit, perceived speed limit, preferred speed), as well as their differences, and to determine which road and personality characteristics influence them. Compared to previous studies, we introduced several innovative features, including a richer personality dataset based on three different questionnaires, a focus on all three types of roads (urban, rural, transition), and representative data on observed speeds. Using statistical models of the speed indicators, we found that both speed limit belief and speed choice, as well as their differences, are often influenced by the same characteristics. These are mainly more generous road design (higher road class, higher speed limit, and wider road) and the presence of additional elements (vegetation, pavements, and pedestrian crossings). These characteristics may help improve the credibility of speed limits and self-explaining performance.

This article aims to investigate the research and development (R&D) premium and explore the three most prominent asset pricing models: capital asset pricing and the three-and five-factor models (Fama & French, 1993; 2015). The results show that India's annualized average R&D premium is significantly higher than the existing value, market, profitability, size and investment premiums, implying that the R&D premium is a more significant concern for Indian investors, particularly for high R&D firms. It was also observed that by applying the GRS test and the Fama and MacBeth (1973) two-pass procedure, the R&D risk factor augmented the CAPM, FF3F and FF5F models outperforming the existing CAPM, FF3F and FF5F models, respectively. We can also report that R&D is, unquestionably, a priced ingredient and a critical factor in developing pricing models for developing markets such as India. The paper's conclusions add to the current literature in R&D and asset pricing and assist investment professionals in developing better investment and trading strategies.

This study attempts to explain the relationship between ESG and financial performance. It utilises a new method for constructing an ESG portfolio with a high exposure towards ESG that eliminates the inherent correlation between size and ESG. In that perspective, a zero initial investment portfolio that goes long in responsible companies and short in irresponsible companies is adopted;hence, developing a 'Responsible Minus Irresponsible' (RMI) factor mimicking portfolio. A pricing anomaly test on this portfolio suggests that ESG exerts superior financial performance, mostly as a result of a significant lower market risk. Performing a cross-sectional analysis of different factor models on an international set of company returns indicates a negative effect of ESG on expected returns. However, the ESG factor becomes insignificant once multiple factors are introduced as explanatory variables. Consequently, ESG represents a pricing anomaly but does not act as an independent risk factor.

This paper proposes a "double adverse selection channel" of international transmission. It shows, theoretically and empirically, that financial systems with both global and local banks exhibit double adverse selection in credit allocation across firms. Global (local) banks have a comparative advantage in extracting information on global (local) risk, and this double information asymmetry creates a segmented credit market where each bank lends to the worst firms in terms of the unobserved risk factor. Given a bank funding (e.g., monetary policy) shock, double adverse selection affects firm financing at the extensive and price margins, generating spillover and amplification effects across countries.

Rational investors should account for risk factor exposure when allocating capital to mutual funds. Two recent influential studies use mutual fund flows to test whether investors distinguish between performance driven by managers' skill and systematic risk factors. Both studies found that investors use the Capital Asset Pricing Model (CAPM), and one concluded that the CAPM is the "closest to the true asset pricing model." We re-examine these results and show that, in fact, fund flow data are most consistent with investors relying blindly on fund rankings (specifically, Morningstar ratings) and chasing recent returns. We find no evidence that investors account for any of the common systematic risk factors when allocating capital among mutual funds.

Growth mixture models (GMMs) are applied to intervention studies with repeated measures to explore heterogeneity in the intervention effect. However, traditional GMMs are known to be difficult to estimate, especially at sample sizes common in single-center interventions. Common strategies to coerce GMMs to converge involve post hoc adjustments to the model, particularly constraining covariance parameters to equality across classes. Methodological studies have shown that although convergence is improved with post hoc adjustments, they embed additional tenuous assumptions into the model that can adversely impact key aspects of the model such as number of classes extracted and the estimated growth trajectories in each class. To facilitate convergence without post hoc adjustments, this paper reviews the recent literature on covariance pattern mixture models, which approach GMMs from a marginal modeling tradition rather than the random effect modeling tradition used by traditional GMMs. We discuss how the marginal modeling tradition can avoid complexities in estimation encountered by GMMs that feature random effects, and we use data from a lifestyle intervention for increasing insulin sensitivity (a risk factor for type 2 diabetes) among 90 Latino adolescents with obesity to demonstrate our point. Specifically, GMMs featuring random effects—even with post hoc adjustments—fail to converge due to estimation errors, whereas covariance pattern mixture models following the marginal model tradition encounter no issues with estimation while maintaining the ability to answer all the research questions.

Background Mendelian randomization (MR) applies instrumental variable (IV) methods to observational data using a genetic variant as an IV. Several Monte-Carlo studies investigate the performance of MR methods with binary outcomes, but few consider them in conjunction with binary risk factors. Objective To develop a novel MR estimator for scenarios with a binary risk factor and outcome; and compare to existing MR estimators via simulations and real data analysis. Methods A bivariate Bernoulli distribution is adapted to the IV setting. Empirical bias and asymptotic coverage probabilities are estimated via simulations. The proposed method is compared to the Wald method, two-stage predictor substitution (2SPS), two-stage residual inclusion (2SRI), and the generalized method of moments (GMM). An analysis is performed using existing data from the CLEAR study to estimate the potential causal effect of smoking on rheumatoid arthritis risk in African Americans. Results Bias was low for the proposed method and comparable to 2SPS. The Wald method was often biased towards the null. Coverage was adequate for the proposed method, 2SPS, and 2SRI. Coverage for the Wald and GMM methods was poor in several scenarios. The causal effect of ever smoking on rheumatoid arthritis risk was not statistically significant using a variety of genetic instruments. Conclusions Simulations suggest the proposed MR method is sound with binary risk factors and outcomes, and comparable to 2SPS and 2SRI in terms of bias. The proposed method also provides more natural framework for hypothesis testing compared to 2SPS or 2SRI, which require ad-hoc variance adjustments.

Research background: Among academicians, a growing interest in brand valuation methods can be observed since the 1980s, when it became obvious that firms have off-balance sheet assets which have a significant effect on their value. Moreover, in a number of cases, the need to value the brand arises due to the reporting requirements or transactional and other intrafirm reasons. The existing methods used so far have commonly focused on changes in variables such as sale prices, changes in customer behaviour, or sales volumes and very often lead to different results, even when valuing the same brand. We believe that the risk factor has been neglected in these methods, although having a significant impact on the brand valuation. Purpose of the article: The aim of this paper is to formulate an alternative brand valuation approach based on the risk difference. This is defined as the difference between the risk to which a producer with a certain brand is exposed and the risk of the producer without a brand. Methods: Firstly, a set of assumptions was defined concerning the issue what conditions are required to be applied to use the proposed methodological approach. Next, the concept itself is formulated and tested while using the case study approach. Hence, in conditions of a model company, the method was verified with specific data. The results were also compared with the reproduction cost approach. Findings & value added: This paper presents a novel brand valuation method based on the risk difference. Building on a thought experiment, we compare an incumbent with a brand rather than with an average producer, which is a commonly used approach, with a new entrant to the market. We argue that in comparison to existing methods, our methodological approach reduces the number of unobservable inputs in the brand valuation process, and thus increases the accuracy and reliability of its results. Our method supports both researchers and practitioners to establish a better understanding between the well-established financial theories and new directions in brand valuation research.

Previous research has provided evidence that in the last decade, investing according to screening based on environmental, social, and governance (ESG) criteria would have allowed investors to considerably improve the ESG quality of their portfolio without deterioration of its financial performance. However, a drawback of such a screening process is that it may generate undesirable regional, sectoral, and risk factor exposures. In this article, the authors propose an investment strategy that maximizes the ESG quality of the portfolio while maintaining regional, sectoral, and risk factor exposures within stated limits. They provide evidence that such a portfolio would have produced risk-adjusted performance at least as high as the standard MSCI benchmark for a wide range of ESG criteria and regions over the 2007–2018 investment period. TOPICS:ESG investing, analysis of individual factors/risk premia, risk management, performance measurement Key Findings ▪ The authors show that improving the environmental, social, and governance (ESG) score of a portfolio through screening can generate undesirable regional, sectoral, and risk factor exposures. ▪ They describe an investment strategy that maximizes the ESG quality of the portfolio while maintaining regional, sectoral, and risk factor exposures within stated limits. ▪ Such a strategy would have produced a risk-adjusted performance at least as high as the standard benchmark for a wide range of ESG criteria and regions over the 2007–2018 investment period.

The literature on cross-sectional stock return predictability has documented over 450 factors. We take the perspective of an institutional investor and navigate this zoo of factors by focusing on the evidence relevant to the practicalities of factor-based investment strategies. Establishing a sound theoretical rationale is key to identifying “true” factors, and we emphasize the need to recognize data-mining concerns that may cast doubt on the relevance of many factors. From a practical investment perspective, much of the factor evidence documented by academics may be more apparent than real. The performance of many factors is dependent on the inclusion of small- and micro-cap stocks in academic studies, although such stocks would likely be excluded from the real investment universe due to illiquidity and transaction costs. Nevertheless, a parsimonious set of factors emerges in equities and other asset classes, including currencies, fixed income, and commodities. These factors can serve as meaningful ingredients to factor-based portfolio construction.

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Originality/value 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.

In a mean-variance framework with a representative agent, any linear model for the cross section of expected returns can be supported as an equilibrium as long as the market portfolio is spanned by the factor mimicking portfolios. Any set of factors is admissible as long as the spanning condition is satisfied. Factors based on size, book-to-market, momentum, investment, profitability, behavioral biases, principal components, or any combination of these can be used as equilibrium factors. An equilibrium model with M risk factors can be reduced to a collection of M models where each model has a single risk factor, which is covariance with the market portfolio.

The aim of this empirical study was to estimate and compare the term structure of risk factor premiums in developed and emerging markets. Most studies use dividend and variance swap data, but as th...

This paper investigates a negative relationship between skewness and expected returns in China's commodity futures market. Unlike the impact of skewness in the US commodity markets, the impact of skewness in Chinese commodity markets is completely monotonic and asymmetric, which indicates more potential arbitrage opportunities in China's commodity markets. Also, we demonstrate that skewness is an effective risk factor in China's commodity futures market that contains different information from traditional risk factors. Investors require positive risk compensation for lower skewness. Empirical findings are shown to be robust with alternative skewness measures in different business cycles.

Multi-state model (MSM) is a useful tool to analyze longitudinal data for modeling disease progression at multiple time points. While the regularization approaches to variable selection have been widely used, extending them to MSM remains largely unexplored. In this paper, we have developed the L1-regularized multi-state model (L1MSTATE) framework that enables parameter estimation and variable selection simultaneously. The regularized optimization problem was solved by deriving a one-step coordinate descent algorithm with great computational efficiency. The L1MSTATE approach was evaluated using extensive simulation studies, and it showed that L1MSTATE outperformed existing regularized multi-state models in terms of the accurate identification of risk factors. It also outperformed the un-regularized multi-state models (MSTATE) in terms of identifying the important risk factors in situations with small sample sizes. The power of L1MSTATE in predicting the transition probabilities comparing with MSTATE was demonstrated using the Europe Blood and Marrow Transplantation (EBMT) dataset. The L1MSTATE was implemented in the open-access R package ‘L1mstate’.

This paper presents a robust economic Model Predictive Control (EMPC) formulation for discrete-time uncertain nonlinear systems. The proposed controller not only ensures that the closed-loop system is robust to disturbances, but also ensures that the economic performance does not deteriorate in the presence of the disturbances. The key idea is to have the controller track a robust control invariant subset of the state space with specified economic properties at all times, and within the zone optimize the process economics. To this end, we introduce the notion of risk factor in the controller design and provide an algorithm to determine the economic zone to be tracked. The risk factor determines the conservativeness of the controller. Our proposed controller is computationally less demanding as it only makes use of the system model without disturbances. A nonlinear CSTR example is presented to demonstrate the performance of the proposed formulation.

Various well-known health diseases affect millions of people worldwide. In the early stage, the clinicians may not recognize various clinical symptoms due to lack of reflection or anything else matter. So, such diseases are not easier to identify, and there may have chances to grow these illnesses and affect millions of people worldwide. Whenever an accurate early prediction is possible, the risk factor of such diseases severity can be lessened. This study presents an innovative multi-tier weighted ensemble learning model (MTWEL) for predicting several diseases such as diabetes and hepatocellular carcinoma (HCC) and, therefore, reduces such above-said problems from the sufferers and lessens the chances of mortality. In the MTWEL model, we have utilized two lists of base classifiers in which six various machine learning (ML) classifiers are assigned in each list to develop two weighted ensemble learning (EL) models and combine them to form the proposed model by employing a weighted voting approach. In the MTWEL model, the parameters of all employed classifiers are tuned through the genetic algorithm-enabled hyperparameter optimization technique to form the optimized base models. The weight of each chosen optimized base model and generated EL model(s) is calculated using Matthews correlation coefficient value with the optimized weight value. In this study, neighborhood component analysis is employed to reduce the dimension of the given input dataset. The suggested model’s experimental outcomes are conducted on two real-world datasets to exhibit its performance. The suggested approach receives the best result in AUC values: 1.0 and 1.0, F1-score values: 0.9957 and 0.9947, and accuracy values: 0.9952 and 0.9929. Such outcomes in the form of performance exhibit that the proposed model is the best-suited model to predict several diseases than other techniques, and hence it helps clinicians make accurate decisions.

We test the impact of herding behaviour on the risk pricing in the Egyptian Stock Exchange (EGX) by adding an additional risk factor reflecting herding behaviour to the Fama and French three-factor model. We construct a portfolio to mimic an additional risk factor related to herding behaviour, in addition to the original risk factors in the Fama and French three-factor model. The three-factor model will be tested in its original form and re-tested after adding the herding behaviour factor. The study is based on Hwang and Salmon methodology, in which the state space approach based on Kaman’s filter was used to measure herding behaviour. We used monthly excess stock returns of 50 stocks listed on the EGX from January 2014 to December 2018. The results do not support Fama and French model before and after adding the herding behaviour factor, therefore, there is no effect of herding behaviour on the risk pricing in the Egyptian Stock Exchange.

This paper examines the role of financial distress premium in explaining the stock returns of banking sector in Pakistan using the sample of twenty listed banks for the period of 2008 to 2018. The study has used two methodologies. Firstly, multifactor model approach of Fama and French (1992) is used to test the financial distress premium (additional risk factor) where portfolio returns are regressed with factor premiums in time series framework. Fama and French (1993) argue that the relationship between the stock return and the selected characteristics occur for that reason these characteristics are proxies for non-diversifiable factor risk. So, the characteristic based model approach of Huang (2009) is used in cross-sectional regression framework where stock returns are regressed with the characteristics. The results indicate that the proposed four factor model is applicable in the banking sector of Pakistan where financial distress premium is priced by the market. The characteristic based model shows insignificant impact of distress proxy of Altman Z score on the banking returns. It suggests that the cross-sectional returns are explained on the covariance structure of returns not the characteristics in the Pakistani banking stocks. The findings of the study suggests that the financial distress is important and consider while forming their portfolios.

This paper aimed to evaluate and compare individual performances and contributions of seven well-known factors, selected from four widely cited asset pricing models: (1) the capital asset pricing model of Sharpe (1964), (2) the three-factor model of Fama and French (1993) the augmented four-factor model of Carhart (1997), (3) the five-factor model of Fama and French (2015), and (4) the illiquidity model of Amihud, et al. (2015) in capturing the time-series variation of stock returns and absorbing the 12 prominent anomalies. The anomalies were constructed by forming long-short portfolios, and regressions were run to examine their monthly returns from 2000 to 2019. We found that there is no definite and absolute “king” in the factor zoo in the Chinese stock market, and size is the relative “king” that can absorb the maximum number of anomalies. Evidence also indicates that the three-factor model of Fama and French may still play an important role in pricing assets in the Chinese stock market. The results can provide investors with a reliable risk factor and help investors form an effective investment strategy. This paper contributes to asset pricing literature in the Chinese market.G1

This study investigates the multiscale intertemporal capital asset pricing model. We focus upon differences across timescales since they represent heterogeneities of investors in markets. This study employs a wavelet approach to decompose return data into multiple timescales. Furthermore, we impose a same risk-aversion parameter constraint into all portfolios, which is proposed by Engel and Bali (2010) who show that the constraint provides a reasonable equity risk premium at a daily frequency. We observe positive relations between the expected returns on portfolios and the covariance of the market at a daily frequency, while these relations change as timescales increase. We find that a negative risk-return relation, which might be related to a correction process of overreaction at an approximately weekly frequency (2 days to 16 days). The strongest positive relation is observed at an approximately monthly frequency (16 to 32 days). Monthly portfolio rebalances are widely used and might impact stock market return patterns. The equity risk premium in the longer frequency ranges from 8.64% to 11.10%. Our results are robust after controlling for macroeconomic variables, market implied volatility and test portfolios. Moreover, we investigate size and value factors and reveal that the risk premia disappear in the longer frequency, which suggests that Intertemporal CAPM is satisfied.

Finansal analistler, yazdıkları raporlarla pay piyasasında sağlıklı bilgi akışına katkıda bulundukları gibi, önerileriyle de büyük bir yatırım sektörünün portföy kararlarını etkileme gücüne sahipler. Bu çalışma, Borsa İstanbul (BİST) hisse senetleri için yapılan analist önerileri doğrultusunda oluşturulan portföylerin getirilerini risk faktörleriyle açıklamayı ve analist önerilerinin ek getiri sağlamadaki başarısını ölçmeyi amaçlamaktadır. Bu bağlamda analist önerileri dikkate alınarak oluşturulan eşit ve değer ağırlıklı portföyler; CAPM, Fama-French ve Carhart varlık fiyatlama modelleri ile test edilmiştir. Bununla birlikte, analist önerilerinin ekonomik durgunluk dönemlerinde bir değer yaratıp yaratmadığı incelenmiştir. Her ne kadar analistlerin olumlu önerileri göreceli yüksek getiri getirse de bu getiriler bilinen risk faktörleri ile açıklandığında ekonomik ve istatistiksel olarak anlamlı bir ek getiriden bahsetmek mümkün değildir. Elde ettiğimiz bulgular ışığında, BİST için örneklem dönemimizde, analistlerin önerileri ile oluşturulan portföylerin piyasa getirisine kıyasla ve risk faktörlerinin verdiği bilgilerin ötesinde bir ek getiri üretemediği sonucuna ulaşmaktayız.

The novel coronavirus COVID‐19 is spreading all across the globe. By June 29, 2020, the World Health Organization announced that the number of cases worldwide had reached 9 994 206 and resulted in more than 499 024 deaths. The earliest case of COVID‐19 in the Kingdom of Saudi Arabia (KSA) was registered on March 2 in 2020. Since then, the number of infections as per the outcome of the tests increased gradually on a daily basis. The KSA has 182 493 cases, with 124 755 recoveries and 1551 deaths on June 29, 2020. There have been significant efforts to develop models that forecast the risks, parameters, and impacts of this epidemic. These models can aid in controlling and preventing the outbreak of these infections. In this regard, this article details the extent to which the infection cases, prevalence, and recovery rate of this pandemic are in the country and the predictions that can be made using the past and current data. The well‐known classical SIR model was applied to predict the highest number of cases that may be realized and the flattening of the curve afterward. On the other hand, the ARIMA model was used to predict the prevalence cases. Results of the SIR model indicate that the repatriation plan reduced the estimated reproduction number. The results further affirm that the containment technique used by Saudi Arabia to curb the spread of the disease was efficient. Moreover, using the results, close interaction between people, despite the current measures remains a great risk factor to the spread of the disease. This may force the government to take even more stringent measures. By validating the performance of the applied models, ARIMA proved to be a good forecasting method from current data. The past data and the forecasted data, as per the ARIMA model provided high correlation, showing that there were minimum errors.

We study delinquent and non‐performing loans in consumer credit markets and their implications for consumer behaviour. By introducing endogenously non‐payment of debt in the inter‐temporal optimization problem of a representative borrowing household, we derive analytically an augmented consumption Euler equation featuring a risk factor in terms of expected non‐performing debt and delinquent debt. We find that the presence of the risk factor differentiates the estimated values of the preference parameters and enhances the model's structure in comparison to the benchmark representative agent model with full debt repayment, which seems to be an incomplete description of consumer behaviour.

In insurance and finance it is often important to have a satisfactory estimate for an extreme quantile, like the one underlying capital requirements in Solvency II and Basel III. If credibility techniques on means are used for the determination of such quantiles, this can lead to quite unsatisfactory results, in particular in the presence of outliers in the data. Quantile credibility models themselves, however, cannot perform effectively when the set of data has a nested (hierarchical) structure. This paper develops multi-stage nested classification hierarchical credibility models for quantiles as an alternative to Jewell’s (G Ist Ital Attuari 38:1–16, 1975) approach, where more than one risk factor divides the portfolio into different sectors or classes. We establish hierarchical quantile credibility estimators and illustrate their performance in two numerical illustrations.

A semiquantitative risk factor has 2 components: any exposure (yes/no) and the quantitative amount of exposure (if exposed). We describe the statistical properties of alternative analyses with such a risk factor using linear, logistic, or Cox proportional hazards models. Often analyses employ the amount exposed as a single quantitative covariate, including the nonexposed with value zero. However, this analysis provides a biased estimate of the exposure coefficient (slope) and we describe the magnitude of the bias. This bias can be eliminated by adding a binary covariate for exposed versus not to the model. This 2-factor analysis captures the full risk-factor effect on the outcome. However, the coefficient for any exposure versus not does not have a meaningful interpretation. Alternatively, when exposure values among those exposed are centered (by subtracting the mean), the estimate of this coefficient represents the difference in the outcome between those exposed versus not in aggregate. We also show that the biased model provides biased estimates of the coefficients for other covariates added to the model. Proper analysis of a semiquantitative risk factor should start with a 2-factor model, with centering, to assess the joint contributions of the 2 components of the risk-factor exposure. Properties of models were illustrated using data from a multisite study in North America (1983–2019).

Pedestrian foot overbridges are sparsely used compared to the at-grade crosswalks in Indian cities. At the signalised intersections, in the presence of at-grade crosswalk, the usage rate of foot overbridge is even less despite the additional pedestrian waiting time. The uneven utilization of the crossing facilities affects the safety and mobility of pedestrians. The objective of this study is to analyse the behaviour of pedestrians and their choices between an at-grade signalised crosswalk and a coexisting foot overbridge (which is quite common in Indian cities) for crossing the street. The actual crossing choice behaviour of 1134 pedestrians was observed from a video survey in Mumbai city, India. The usage rate of foot overbridge in the sample was 26% only. Binary logit model was estimated to find the significant variables influencing the pedestrian choice of crossing facility. The results showed that pedestrian’s total crossing time ratio (ratio of crossing time by foot overbridge to total crossing time by at-grade crossing which includes additional waiting time at signal) affects the choice of crossing facilities significantly. The probability of selecting foot overbridge increases by about 60% as the pedestrian’s total crossing time ratio reduces from 1.5 to 0.5, For more than 50% of pedestrians to use foot overbridge, the total crossing time ratio should be within 0.75. It was also inferred that choices of women and older pedestrians are influenced primarily by convenience to cross, whereas the choices of pedestrians accompanied by children are driven by the safety benefits of the crossing facility. Another important finding from the study was that pedestrians’ crossing behaviour depends on the level of risk they take while crossing (in terms of signal violation). The conclusions from this study may be helpful in strengthening the policy measures in the direction of the design of safe and efficient pedestrian crossing facilities.

We investigate the predictive power of several innovative tradable risk factors that have proved to be competent factors in recent asset pricing studies. Our evidence indicates that all these risk factors can predict the future state of the economy to some significant extent, and they appear to perform better in short‐horizon than in long‐horizon forecasting. Using a bootstrap simulation, our estimations of bootstrapped critical values robustly reject the criticism that our significance of statistics is overstated or understated. Such results lend support to Cochrane's argument: that a competent pricing risk factor in a plausible pricing kernel may predict the future state of economy.

In this article, we investigate industry momentum strategies. We find that industry portfolios that outperformed in the previous month generate on average significantly higher returns in the holding period than those that underperformed. Plain and risk‐managed strategies using this short‐run industry momentum are not subject to optionality effects. Also, the tail risks of these strategies are uncorrelated with traditional industry momentum strategies. The spread associated with the risk‐managed strategy both meets necessary conditions as a risk factor and is significantly priced in the cross‐section of U.S. industry portfolios.

Background : The problem of variable selection for risk factor modeling is an ongoing challenge in statistical practice. Classical methods that select one subset of exploratory risk factors dominate the medical research field. However, this approach has been criticized for not taking into account the uncertainty of the model selection process itself. This limitation can be addressed by a Bayesian model averaging approach: instead of focusing on a single model and a few factors, Bayesian model averaging considers all the models with non-negligible probabilities to make inference. Methods : This paper reports on a simulation study designed to emulate a matched case-control study and compares classical versus Bayesian model averaging selection methods. We used Matthews’s correlation coefficient to measure the quality of binary classifications. Both classical and Bayesian model averaging were also applied and compared for the analysis of a matched case-control study of patients with methicillin-resistant Staphylococcus aureus infections after hospital discharge 2011-2013. Results : Bayesian model averaging outperformed the classical approach with much lower false positive rates and higher Matthew’s correlation scores. Bayesian model averaging also produced more reliable and robust effect estimates. Conclusion : Bayesian model averaging is a conceptually simple, unified approach that produces robust results. It can be used to replace controversial P-values for case-control study in medical research. Key words: Bayesian model averaging; Gibbs variable selection; matched case control; model selection; Zellner’s g-prior

In this paper, we present a backward deep BSDE method applied to Forward Backward Stochastic Differential Equations (FBSDE) with given terminal condition at maturity that time-steps the BSDE backwards. We present an application of this method to a nonlinear pricing problem - the differential rates problem. To time-step the BSDE backward, one needs to solve a nonlinear problem. For the differential rates problem, we derive an exact solution of this time-step problem and a Taylor-based approximation. Previously backward deep BSDE methods only treated zero or linear generators. While a Taylor approach for nonlinear generators was previously mentioned, it had not been implemented or applied, while we apply our method to nonlinear generators and derive details and present results. Likewise, previously backward deep BSDE methods were presented for fixed initial risk factor values $X_0$ only, while we present a version with random $X_0$ and a version that learns portfolio values at intermediate times as well. The method is able to solve nonlinear FBSDE problems in high dimensions., Comment: 25 pages

Nowadays, the sustainability risks and opportunities start to affect strongly insurance companies in regard to the resulting additional variability of future values of variables taken into account in the decision processes. This is important especially in the era of sustainable non-life insurance promoting, among others, the use of ecological car engines or ecological systems of building heating. The fundamental issue in non-life insurance is to predict future claims (e.g., the aggregate value of claims or the number of claims for a single policy) in a heterogeneous portfolio of policies taking account of claim experience. For this purpose, the so-called credibility theory is used, which was initiated by the fundamental Bühlmann model modified to the Bühlmann–Straub model. Several modifications of the model have been proposed in the literature. One of them is the development of the relationship between the credibility models and statistical mixed models (e.g., linear mixed models) for longitudinal data. The article proposes the use of the parametric bootstrap algorithm to estimate measures of accuracy of the credibility predictor of the number of claims for a single policy taking into account new risk factors resulting from the emergence of green technologies on the considered market. The predictor is obtained for the model which belongs to the class of Generalised Linear Mixed Models (GLMMs) and which is a generalization of the Bülmann–Straub model. Additionally, the possibility of predicting the number of claims and the problem of the assessment of the prediction accuracy are presented based on a policy characterized by new green risk factor (hybrid motorcycle engine) not previously present in the portfolio. The paper presents the proposed methodology in a case study using real insurance data from the Polish market.

BackgroundMendelian randomization (MR) uses genetic variants as instrumental variables to investigate the causal effect of a risk factor on an outcome. A collider is a variable influenced by two or more other variables. Naive calculation of MR estimates in strata of the population defined by a variable affected by the risk factor can result in collider bias.MethodsWe propose an approach that allows MR estimation in strata of the population while avoiding collider bias. This approach constructs a new variable, the residual collider, as the residual from regression of the collider on the genetic instrument, and then calculates causal estimates in strata defined by quantiles of the residual collider. Estimates stratified on the residual collider will typically have an equivalent interpretation to estimates stratified on the collider, but they are not subject to collider bias. We apply the approach in several simulation scenarios considering different characteristics of the collider variable and strengths of the instrument. We then apply the proposed approach to investigate the causal effect of smoking on bladder cancer in strata of the population defined by bodyweight.ResultsThe new approach generated unbiased estimates in all the simulation settings. In the applied example, we observed a trend in the stratum-specific MR estimates at different bodyweight levels that suggested stronger effects of smoking on bladder cancer among individuals with lower bodyweight.ConclusionsThe proposed approach can be used to perform MR studying heterogeneity among subgroups of the population while avoiding collider bias.

Mendelian randomization is a powerful tool for inferring the presence, or otherwise, of causal effects from observational data. However, the nature of genetic variants is such that pleiotropy remains a barrier to valid causal effect estimation. There are many options in the literature for pleiotropy robust methods when studying the effects of a single risk factor on an outcome. However, there are few pleiotropy robust methods in the multivariable setting, that is, when there are multiple risk factors of interest. In this article we introduce three methods which build on common approaches in the univariable setting: MVMR‐Robust; MVMR‐Median; and MVMR‐Lasso. We discuss the properties of each of these methods and examine their performance in comparison to existing approaches in a simulation study. MVMR‐Robust is shown to outperform existing outlier robust approaches when there are low levels of pleiotropy. MVMR‐Lasso provides the best estimation in terms of mean squared error for moderate to high levels of pleiotropy, and can provide valid inference in a three sample setting. MVMR‐Median performs well in terms of estimation across all scenarios considered, and provides valid inference up to a moderate level of pleiotropy. We demonstrate the methods in an applied example looking at the effects of intelligence, education and household income on the risk of Alzheimer's disease.

Regime-switching models have been recently becoming increasingly important as they allow structural changes to be taken into account in modelling financial data. Application of such models to describe prices behaviour is especially valuable in commodities markets, where China’s industrialisation in the past three decades, created a huge demand for metals and energy, changing fundamentals of the market and affecting commodity prices. Moreover, in the financial time series very often we observe the non-Gaussian behaviour, which is manifested by large observations related to the market conditions. Considering the mentioned features of the financial data (especially commodity prices) in this paper we propose a stochastic model which takes under consideration the possible regime changes, the non-Gaussian character of the data and finally, the non-constant in time characteristics (like the mean function). We describe the main properties of the considered model and introduce a novel estimation procedure for the estimation of its parameters. Finally, we apply the proposed model to the real data describing the copper prices, one of the main risk factor for the KGHM mining company.

With the prominent role of government debt in economic growth in recent decades, one would expect that government debt alongside economic growth to be a risk factor priced in the time series of stock returns. In this paper, this idea is investigated by applying a nonparametric model, namely, a local-linear kernel smoother with the aim of forecasting long-term stock returns where the model and smoothing parameters are chosen by cross-validation. While a wide range of predictive variables are examined, we find that our newly introduced debt-by-price ratio and the third to fourth quarter economic growth are robust predictors of stock returns, beating the well-known predictive variables in the literature by a significant difference. The combination of these two covariates can explain almost 30% variation of stock returns at a one-year horizon. This is very crucial considering the difficulty in capturing even a small proportion of movements in stock returns.

We develop a new factor selection methodology of spanning the space of hedge fund risk factors with all available exchange traded funds (ETFs). We demonstrate the efficacy of the methodology with out‐of‐sample individual hedge fund return replication by ETF clone portfolios. This is consistent with our interpretation of ETF returns as proxies to risk factors driving hedge fund returns. We further consider portfolios of “cloneable” and “noncloneable” hedge funds, defined as top and bottom in‐sample R2 matches, and demonstrate that our ETF clone portfolios slightly outperform cloneable hedge funds out of sample.

Using features extracted from StockTwits messages between July 2009 and September 2012, we show through simulations that: (1) message volume and sentiment can be used as a risk factor in an asset pricing model framework; (2) message volume and sentiment help explain the diffusion of price information over several days, and (3) message volume and sentiment can be used as features to predict asset price directional moves. Our findings suggest statistics derived from message volume and sentiment can improve asset price forecasts and leads to a profitable trading strategy.

In this article, an event studies approach is utilized to assess the influence of 51% attacks on proof-of-work (PoW) cryptocurrency prices. The study uses an exhaustive sample of 14 individual attacks on 13 cryptocurrencies. Across multiple event studies techniques, majority attacks on blockchains are consistently shown to immediately decrease corresponding coin prices by 12% to 15%. Significantly negative price response is robust in various event windows. Coin prices do not recover to pre-attack levels one week after the event. There is evidence of pump-and-dump schemes prior to the 51% attack, however the market demonstrates high efficiency after the attacks. 51% attacks are suggested to be a fundamental risk factor for cryptocurrency investments, primarily characteristic of small PoW coins with low hash rates. TOPIC:Currency Key Findings • 51% attacks on Proof-of-Work cryptocurrencies decrease their market prices by 12.60% on average. • The effect is robust to different measurement techniques and in various event windows. • There is evidence of insider trading and “pump-and-dump” schemes prior to the attacks.

The concept of acturial fairness stems from an Aristotelian tradition in which fairness requires equality between the goods exchanged. When dealing with aleatory contracts, this principle evolved, among medieval scholars, into equality in risk: benefits and losses should be proportional to the risks undertaken. The formalization of this principle gave rise to the concept of mathematical expectation, first implemented in the calculation of the fair price of gambles. The concept of an actuarial fair price was first theoretically articulated in the 17th century as an implementation of this same Aristotelian principle in the field of life insurance. For a practical estimation of fair actuarial prices it was necessary to build mortality tables, assuming that the major risk factor was age. Yet, in the 18th and 19th centuries, we find no agreement among proto-actuaries about the proper construction of these tables. Among the obstacles they found, we want to highlight their early awareness of the possibility of adverse selection: buyers and sellers could manipulate the risk assessment for their own private interests, in a way that would either make fair companies collapse or fair customers be cheated. The paradox in the concept of actuarial fairness is that as soon as it was formally articulated, markets made clear it could never be implemented in actual pricing.

The classification of wind energy resources can provide reference for assessing optimal turbine locations. The traditional wind energy classification scheme is usually a single consideration of partial wind energy factors. This study aims to construct a micro-scale classification scheme of wind energy resource that incorporates a comprehensive consideration of wind energy factors, environment risk factor and cost factors. The wind energy classification of the New Zealand waters is presented as a case study by calculating the expectation values of wind energy. Results show that the expectation values of wind energy in most of the New Zealand are above 0.6 (belongs to rich area). Only a small range coast area is relatively low in expectation values of wind energy, but still above 0.5 (belongs to subrich area). The North Cape waters, the East Cape waters, the South Taranaki Bight and its northwest waters are relative large center of expectation values of wind energy. Comparing with the traditional wind energy classification schemes, our scheme can exhibit the regional difference of wind energy classification more obviously. However, the weight coefficient of the factors considered in this study is not fixed. Reasonably adjusting, adding or deleting the weight coefficients of the related factors according to different focus, the wind energy classification scheme proposed by this study can be widely used to provide scientific reference for the site selection of wind energy development under different actual demands.

The Fama-French (FF) five-factor model is cast into a dynamic setting to capture the impact of illiquidity over the phases of the business cycle on the returns of the passive FF twelve sector portfolios. We use two dynamic approaches, Kalman filtering and a recursive/rolling robust instrumental variables (IV) algorithm cast into a GMM framework, to determine time-varying alpha and beta estimates. Our principal result is that the Kalman filter approach supports the hypothesis that illiquidity is an important risk factor in a dynamic context. However, the only factor found to matter in the dynamic GMM approach is the market risk premium. Nevertheless, illiquidity may be prescient with respect to financial crises. Keywords: Illiquidity, Fama-French five-factor model, Kalman filter, Robust IV algorithm, JEL Classification: C58, G12

We take a new look on the Fama and French (1993) three-factor asset pricing model by extracting risk premium contributions for each factor based on solving a system of linear equations. The risk premium contributions become uncorrelated with the underlying factor excess returns and capture the isolated compensation of a given risk factor. We show that the risk premium contributions feature a January-seasonality, which exhibits a negative shift after the year 1993. Furthermore we find that after 1993 the risk premium contributions of the SMB and HML factor are strongly related to sentiment and predictable by dividend yield.

A risk factor shared by all musicians is a potential for noise-induced hearing loss. A study was conducted to explore what factors determine noise dosage during musical events experienced by college student musicians. First, noise exposure during specific activities was explored, including during ensemble rehearsal and personal practice. Next, full-day noise exposure was investigated by measuring levels experienced by student musicians during a typical daily routine. Factors such as instrument played, type of activity, location within ensemble, and room environment were related to noise dosage. Disparities in results using different standards to calculate noise dosage were also explored. Risk of noise overexposure was found to be greater in some instrument classes, such as wind instruments, than in others, such as string instruments, and can yield vastly different results depending on the metric used for noise dosage calculation.

Empirical evidence shows that a single-factor model using the Aumann-Serrano riskiness index dominates both the CAPM and Fama-French three-factor model because the index captures information on hig...

We analyze the use of alternative performance measures to rank and select assets. Previous literature centers on the effects of non-normality on rank correlations between orderings. Instead, we select the assets recommended by each performance measure (ordering) and analyze out-of- sample returns of the portfolio that contains them. The overall empirical findings show that performance measures are definitively relevant for subsequent portfolio returns. Assets selected by the Generalized Rachev and Value-at-Risk ratios dominate other selections showing high cumulative returns after the 2008 downturn. The good performance is connected to the fact that these asset returns show high excess kurtosis but positive skewness and are insensitive to the momentum risk factor. This work was supported by the Generalitat Valenciana (PROMETEO/2017/158), the Spanish Department of Economy and Competitiveness (ECO2015-67035-P and ECO2017-87069-P), and the Bank of Spain (Excellence in Education and Research Grant 2016).

Bitcoin is a digital currency started in early 2009 by its inventor under the pseudonym of Satoshi Nakamoto. In the last few years, Bitcoin has received much attention and has shown a surprising price increase. Bitcoin is currently traded on many web-exchanges making it a rare example of a good for which different prices are readily available; this feature implies important issues about arbitrage opportunities since prices on different exchanges are shown to be driven by the same risk factor. In this paper, we show that simple strategies of strong arbitrage arise by trading across different Bitcoin exchanges taking advantage of the common risk factor. The suggested arbitrage strategies are based on two alternative model specifications. Precisely, we consider the multivariate versions of Black and Scholes model and of an attention-based dynamics recently introduced in the literature.