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The beta coefficient used for the cost of equity calculation is at the heart of the valuation process. This study conducts comparative analyses of the classical capital asset pricing model (CAPM) and downside CAPM risk parameters to gain further insight into which risk parameter leads to better performing risk measures at explaining stock returns.

The study conducts a comparative analysis of 16 risk measures at explaining the stock returns of 4531 companies of 20 developed and 25 emerging market index for 2000–2018. The analyses are conducted using both the global and local indices and both USD and local currency returns. Calculated risk measures are analyzed in a panel data setup using a univariate model. Results are investigated in country-specific and model-specific subsets.

The results show that (1) downside betas are better than CAPM betas at explaining the stock returns, (2) both risk measure groups perform better for emerging markets, (3) global downside beta model performs better than global beta model, implying the existence of the contagion effect, (4) high significance levels of total risk and unsystematic risk measures further support the shortfall of CAPM betas and (5) higher correlation of markets after negative shocks such as pandemics puts global CAPM based downside beta to a more reliable position.

The data are limited to the index securities as beta could be time varying.

Results overall provide insight into the cost of equity calculation and emerging market assets valuation.

The framework and methodology enable us to compare and contrast CAPM and downside-CAPM risk measures at the firm level, at the global/local level and in terms of the level of market development.

This paper examines dynamic return spillovers and connectedness networks among international stock exchange markets. The authors account for asymmetry by distinguishing between positive and negative returns.

This paper employs the spillover index of Diebold and Yilmaz (2012) to measure the volatility spillover index for total, positive and negative volatility.

The results show time-varying and asymmetric volatility spillovers among the stock markets under investigation. During the coronavirus disease 2019 (COVID-19) pandemic, bad volatility spillovers are more pronounced and dominated over good volatility spillovers, indicating contagion effects.

The presence of confirmed COVID-19 cases positively (negatively) affects the good and bad spillovers under low and intermediate (upper) quantiles. Both types of spillovers at various quantiles agree also influenced by the number of COVID-19 deaths.

This paper reviews various optimization approaches used to address a variety of issues related to risk in agricultural finance and farm management. The central focus is in the Markowitz mean‐variance model, which represents the classical approach to balancing risk and returns in an optimization framework. We also review other models that have been used historically to solve linearizations of the mean‐variance problem including MOTAD and target MOTAD. Specialized optimization models such as Target semivariance and direct expected utility maximization are also discussed.

The purpose of this paper is to present the method for efficient computation of risk measures using Fourier transform technique. Another objective is to demonstrate that this technique enables an efficient computation of risk measures beyond value-at-risk and expected shortfall. Finally, this paper highlights the importance of validating assumptions behind the risk model and describes its application in the affine model framework.

The method proposed is based on Fourier transform methods for computing risk measures. The authors obtain the loss distribution by fitting a cubic spline through the points where Fourier inversion of the characteristic function is applied. From the loss distribution, the authors calculate value-at-risk and expected shortfall. As for the calculation of the entropic value-at-risk, it involves the moment generating function which is closely related to the characteristic function. The expectile risk measure is calculated based on call and put option prices which are available in a semi-closed form by Fourier inversion of the characteristic function. We also consider mean loss, standard deviation and semivariance which are calculated in a similar manner.

The study offers practical insights into the efficient computation of risk measures as well as validation of the risk models. It also provides a detailed description of algorithms to compute each of the risk measures considered. While the main focus of the paper is on portfolio-level risk metrics, all algorithms are also applicable to single instruments.

The algorithms presented in this paper require little computational effort which makes them very suitable for real-world applications. In addition, the mathematical setup adopted in this paper provides a natural framework for risk model validation which makes the approach presented in this paper particularly appealing in practice.

This is the first study to consider the computation of entropic value-at-risk, semivariance as well as expectile risk measure using Fourier transform method.

The purpose of this paper is to consider compromise solutions of multiple attribute decision-making methods (TOPSIS, VIKOR, and similarity-based approach) in order to evaluate and rank mutual funds and to compare the capabilities of different approaches based on the different traditional indices of mutual funds assessment. In addition, a new algorithm for ranking mutual funds was proposed subsequently.

In this research, three groups of indices including general, risk-modified performance evaluation, and risk-modified performance evaluation indices using semivariance were used in the mutual funds assessment, which led to the comparison between selected mutual funds, using three mentioned methods and three different groups of criteria. The results of this comparison were compiled and synthesized with linear assignment method. At the end, an algorithm for decision making and investing in mutual funds for professional and unprofessional investors was proposed.

Using different methods and different criteria proved that the results of similarity-based approach as a MADM technique have the ability to rank and evaluate mutual funds regardless of the criteria used compared to TOPSIS and VIKOR. Furthermore, the authors propose the algorithm of this research as a new model of mutual funds evaluation which considers a wide range of variables with respect to amateur and professional points of view.

The originality of this paper is threefold: first, different criteria were considered to make the evaluation more comprehensive. Second, four different approaches were used to make the results more authentic. Third, a holistic algorithm with its implication was proposed.

The purpose of this paper is to provide conclusive evidence that infrastructure constitutes a separate asset class and cannot be classified as real estate from an investment point‐of‐view. Furthermore, optimal allocations are determined for direct and indirect infrastructure within a multi‐asset portfolio.

Portfolio allocations are optimized by using an algorithm, which accounts for downside risk, rather than variance. This approach is more in accordance with the actual investor behaviour and might meet their investment objectives more effectively. An Australian dataset comprising stocks, bonds, direct real estate, direct infrastructure and indirect infrastructure is applied for portfolio construction.

Although infrastructure and real estate have common characteristics, the conclusion is that that they constitute two different asset classes. Furthermore, the diversification benefits of direct and indirect infrastructure within multi‐asset portfolios are highlighted and determine efficient allocations up to 78 percent for target rates of 0.0 percent, 1.5 percent and 3.0 percent quarterly.

The results will help investors and portfolio managers to efficiently allocate funds to various asset classes. Most institutional investors are not familiar with investments in infrastructure. The study facilitates a better understanding of the asset class infrastructure and yields some important implications for the optimal allocation of infrastructure within institutional investment portfolios.

This is the first study to examine the role of direct and indirect infrastructure within a multi‐asset portfolio by applying a downside‐risk approach.

The extensive normative literature on capital budgeting decision models tends to ignore many factors that influence choice processes. This paper identifies task factors, context factors, and decision maker factors which influence perceived risk in capital budgeting decisions. Central issues explored in the paper are (a) whether some context and decision maker factors can be included in a capital budgeting decision support system's knowledge system, and (b) whether a decision support system can adapt its choice models and interface to different decision situations based on knowledge about task factors, context factors, and decision maker factors.

This paper aims to identify a possible mismatch between the theory found in academic research and the practices of investment managers in Brazil.

The chosen approach is a field survey. This paper considers 78 survey responses from 274 asset management companies. Data obtained are analyzed using independence tests between two variables and multiple regressions.

The results show that most Brazilian investment managers have not adopted current best practices recommended by the financial academic literature and that there is a significant gap between academic recommendations and asset management practices. The modern portfolio theory is still more widely used than the post-modern portfolio theory, and quantitative portfolio optimization is less often used than the simple rule of defining a maximum concentration limit for any single asset. Moreover, the results show that the normal distribution is used more than parametrical distributions with asymmetry and kurtosis to estimate value at risk, among other findings.

This study may be considered a pioneering work in portfolio construction, risk management and performance evaluation in Brazil. Although academia in Brazil and abroad has thoroughly researched portfolio construction, risk management and performance evaluation, little is known about the actual implementation and utilization of this research by Brazilian practitioners.

Present a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts.

Using the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lightning prediction.

The techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction.

The research is limited to the data collected in support of weather prediction work through the 45th Weather Squadron of the United States Air Force.

These methods are important due to the increasing reliance on sensor systems. These systems often provide incomplete and chaotic data, which must be used despite collection limitations. This work establishes a viable data imputation methodology.

Improved lightning prediction, as with any improved prediction methods for natural weather events, can save lives and resources due to timely, cautious behaviors as a result of the predictions.

Based on the authors’ knowledge, this is a novel application of these imputation methods and the forecasting methods.

Returns to a model farm are simulated to assess the impact of marketing and insurance risk management tools as measured by mean net returns and returns at 5% value‐at‐risk (VaR). Results indicate that revenue insurance strategies and strategies involving a combination of price and yield protection provide substantial downside revenue protection, while mean net returns only modestly differ from the benchmark harvest sale strategy when considering all years between 1986 and 2000.