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In order to develop our understanding of valuation models and so extend this to encompass the important area of performance measurement and its interpretation, it is essential to have a framework which will enable such developments to take place. This paper presents a theoretical model based on a certainty equivalent approach which enables the market risk of individual properties and portfolios to be assessed on an expectations basis. The data requirements for using the model are not onerous and with simple extensions it can be adapted to cope with changes in risk that occur when variations in the lease structure are anticipated. Understanding the influence of systematic or market risk is essential if our understanding of valuation is to improve. Systematic risk is the single most important factor which determines the premium which should be allowed to compensate for risk. This aspect has been largely ignored in the property literature with the result that risk premium figures are frequently assumed to be constant across all sectors and properties. This paper derives a model which attempts to overcome some of these problems. Due to data limitations empirical tests of the model cannot be regarded as conclusive. However, those tests that have been carried out suggest that the model could be used for estimating the required rate of return of both sectors and individual properties. It also has considerable potential in estimating growth expectations for groups of properties and can thus be used in the decision‐making process. Much, however, remains to be done.

This study evaluates the impacts on gross revenue distributions of the use of alternative crop insurance products across different coverage levels and across locations with differing yield risks. Results are presented in terms of net costs, values‐at‐risk, and certainty equivalent returns associated with five types of multi‐peril crop insurance across different coverage levels. Findings show that the group policies often result in average payments exceeding their premium costs. Individual revenue products reduce risk in the tails more than group policies, but result in greater reductions in mean revenues. Rankings based on certainty equivalent returns and low frequency VaRs generally favor revenue products. As expected, crop insurance is associated with greater relative risk reduction in locations with greater underlying yield variability.

The purpose of this paper is to provide theory for some popular models and strategies used by practitioners in constructing optimal portfolios. King (2007), for example, advocated adding a diversification term to mean-variance problems to create better portfolios and provided clear empirical evidence that this is beneficial.

The authors provide an analytical framework to help us understand different portfolio construction practices that may incorporate diversification and conviction strategies; this allows us to connect our analysis to ideas in psychophysics and behavioural finance. The critical psychological ideas are cognitive dissonance and entropy; the economics are based on expected utility theory. The empirical section uses the theory outlined and provides the basis for constructing such portfolios.

The model presented allows the incorporation of different strategies within a mean-variance framework, ranging from diversification and conviction strategies to more ESG-oriented ones. The empirical analysis provides a practical application.

To the best of the authors’ knowledge, this model is the first to bridge the gap between portfolio optimisation and the psychological ideas mentioned in a coherent analytical framework.

The purpose of this paper is to establish a deterministic equivalent income model (DEIM) based on the risk cost (RC) and risk aversion of investors. The model fully considers both subjective and objective factors that affect risk investment and reasonably evaluates risk investment schemes to choose the correct investment scheme and gain greater investment returns.

The utility function is used to measure the extent to which an investor is satisfied by investment returns in various scenarios. Risk aversion expresses subjective attitude of investors to risk. RC represents risk loss in currency. This methodology is based on risk aversion function, utility function and RC theory to establish DEIM.

This study shows that investors with different risk preferences have different certainty equivalent returns (CER), so their choices of investment options change accordingly.

In this paper, the authors use DEIM to test an investment case and conclude that the CER and investment scheme both change with different risk preferences. At the same time, case analysis shows that DEIM is reasonable and stable when evaluating risk investment schemes.

In this study, the authors innovate by introducing both the RC and risk aversion degree into risk investment schemes evaluation and by deriving a utility function from the absolute risk aversion function to build a utility decision matrix and establish DEIM. The model combines the subjective and objective factors that influence risk investment decisions.

In capital markets, research risk factor loadings and characteristics are considered as opposing explanations for the cross-sectional dispersion in average stock returns. However, there is little known about the performance an investor would obtain who believes either in the characteristics explanation (CB-investor) or in the risk factor loadings explanation (RB-investor). The purpose of this paper is to compare the performance of CB- and RB-investors.

To compare the competing strategies, the authors propose a simple new approach to equity portfolio optimization in the style of Brandt et al. (2009) by modeling the portfolio weight in each asset as a function of the asset's risk factor loadings or characteristics. The authors perform an empirical analysis on the German stock market, exploiting the risk factor loadings from the Carhart (1997) four-factor model and the respective characteristics size, book-to-market equity ratio and momentum.

The results show that investment strategies relying on characteristics (particularly on momentum) outperform risk-based investment strategies in horse races. These findings hold in- and out-of-sample. Furthermore, the characteristics-based investment strategies outperform a value-weighted market portfolio strategy in- and out-of-sample.

The authors introduce a portfolio optimization approach that enables investors to directly link portfolio decisions to the firm’s characteristics or risk factor loadings.

This chapter focuses on examining how changes in the liquidity differential between nominal and TIPS yields influence optimal portfolio allocations in U.S. Treasury securities. Based on a nonparametric estimation technique and comparing the optimal allocation decisions of mean-variance and CRRA investor, when investment opportunities are time varying, I present evidence that liquidity risk premium is a significant risk-factor in a portfolio allocation context. In fact, I find that a conditional allocation strategy translates into improved in-sample and out-of-sample asset allocation and performance. The analysis of the portfolio allocation to U.S. government bonds is particularly important for central banks, specially in developing countries, given the fact that, collectively they have accumulate a large holdings of U.S. securities over the last 15 years.

This article analyzes the difference between the budgetary expense and the opportunity cost of defense inputs. If inputs are obtained by the government from a market economy with undistorted prices, the price paid for the last unit of each input acquired equals the opportunity cost. However, taxes create a distortion between opportunity cost and unit price. An additional complication, discussed using the case of military personnel, is that premarginal units may have an opportunity cost lower than the unit price determined at the margin. Principles used to determine the social discount rate are also discussed in the analysis.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

This study looks at the characteristics and current status of retail structured product market of Korea and tries to explain, in particular, issues related to issue price, cost of hedging, and overpricing. We also analyzed the perspective of the government and the related regulatory policies. We examined various performance measures for portfolios composed of the KOSPI200 Covered Call Index and other assets in order to change the viewpoint of the authorities that the trading of structured products, such as ELS (equity-linked securities) and DLS (debt-linked securities), is in fact not a zero-sum game between the issuers and investors. The empirical results show that the KOSPI200 Covered Call Index has a superior performance compared to the KOSPI200 Index and the others. In addition, from the perspective of certainty equivalent excess returns, the KOSPI200 Covered Call Index also displays the possibility of improving the utility level of risk-averse retail investors. However, it is difficult in reality for individual investors to construct efficient portfolios that employ covered call strategies using options. Hence, individual investors can form optimal portfolios that benefit indirectly from such covered call strategies via investment in financial derivative products issued by securities firms that are able to more easily utilize investment strategies that incorporate options to form optimum portfolios. This means that both the issuer and investor can profit from these financial derivative products and, therefore, it is not a zero-sum game.

This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.

This study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons; providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.

Green returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.

This study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries’ green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects; which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.