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Since the 1990s, there has been a discussion about the use of weather index‐based insurance, also called weather derivatives, as a new instrument to hedge against volumetric risks in agriculture. It particularly differs from other insurance schemes by pay‐offs being related to objectively measurable weather variables. Due to the absence of individual farm yield time series, the hedging effectiveness of weather index‐based insurance is often estimated on the basis of aggregated farm data. The authors expect that there are differences in the hedging effectiveness of insurance on the aggregated level and on the individual farm‐level. The purpose of this paper is to estimate the magnitude of bias which occurs if the hedging effectiveness of weather index‐based insurance is estimated on aggregated yield data.

The study is based on yield time series from individual farms in central Germany and weather data provided by the German Meteorological Service. Insurance is structured as put‐option on a cumulated precipitation index. The analysis includes the estimation of the hedging effectiveness of insurance on aggregated level and on individual farm‐level. The hedging effectiveness is measured non‐parametrically regarding the relative reduction of the standard deviation and the value at risk of wheat revenues.

Findings indicate that the hedging effectiveness of a weather index‐based insurance estimated on aggregated level is considerably higher than the realizable hedging effectiveness on the individual farm‐level. This refers to: hedging effectiveness estimated on the aggregated level is higher than the mean of realized hedging effectiveness on the individual farm‐level and almost every evaluated individual farm in the analysis realizes a lower hedging effectiveness than estimated on the aggregated level of the study area. Nevertheless, weather index‐based insurance designed on the aggregated level can lead to a notable risk reduction for individual farms.

To the authors’ knowledge, this paper is the first that analyzes the influence of crop yield aggregation with regard to the hedging effectiveness of weather index‐based insurance.

The accounting literature has traditionally focused on firm-level studies to examine the capital market implications of earnings and other accounting variables. We first develop the arguments for studying capital market implications at the aggregate level as well. A central issue is that diversification makes equity investors at least partially and potentially almost completely immune to several firm-level properties of earnings by holding diversified portfolios. Diversification is particularly important when assessing the welfare consequences of random errors in accounting measurement (imperfect accruals) and, to the extent it is independent across firms, of deliberate manipulation (earnings management). Consequently, some firm-level metrics of association, timeliness, value relevance, conservatism and other earnings properties do not map easily into investor welfare. Similarly, earnings-related risk manifests itself to equity investors largely through systematic earnings risk (covariation with aggregate earnings and/or other macroeconomic indicators). We conclude that the design and evaluation of financial reporting must adopt at least in part an aggregate perspective. We then summarize the literature in accounting, economics and finance on aggregate earnings and stock prices. Our review highlights the importance of studying earnings at the aggregate level.

This paper aims to examine the nexus between the pricing of market-wide volatility risk and distress risk in the cross-section of portfolio returns for the 1990-2011 time period. The author expands upon prior research by constructing an ex post factor that mimics aggregate volatility risk based on the new VIX index of the Chicago Board Options Exchange, termed FVIX, as well as focuses on volatility risk in crisis versus non-crisis time periods.

The author investigates the relationship between volatility and distress risk using several techniques in the empirical finance literature. Specifically, the author investigates the behavior of correlations between risk factors as well as the correlations between factor loadings when using the Fama and French research portfolios as our test assets for different time periods. Additionally, the author examines the variation in the volatility factor loadings across the size- and value-sorted portfolios and assesses whether augmenting conventional pricing models with a volatility factor leads to a higher goodness-of-fit in pricing the 25 size- and value-sorted portfolios.

The author’s results suggest that factor volatilities are high during periods of market turmoil. In addition, the author presents evidence indicating that a factor mimicking innovation in volatility (based on the new VIX) is correlated with the market and momentum factors, while exhibiting the uncorrelated behavior with respect to the size, value and liquidity factors when using data from 1990 through 2011. In this paper, the author finds that the aggregate volatility factor’s correlation with the market and momentum factors increases during crisis periods. In periods of relative market tranquility, correlations decrease significantly. In examining multivariate factor loadings for the test assets, the results provide no clear pattern with regard to the variation of the volatility loadings across the book-to-market and size dimensions. Furthermore, the author finds that conventional pricing models are comparable to FVIX-augmented pricing models, in terms of goodness-of-fit, when pricing the 25 Fama-French size- and value-sorted portfolios. Additionally, when using the FVIX volatility factor to proxy for aggregate volatility risk, the coefficients are never significant statistically, thus revealing that innovations in aggregate volatility based on the new VIX index do not constitute a priced risk factor in the cross-section of returns.

The author’ finding indicates an absence of strong variation of the volatility factor loadings across the Fama-French research portfolios. In particular, the asset pricing results cast doubt on whether a factor mimicking innovations in aggregate volatility based on the new VIX index is priced. In agreement with prior research, the author believes that the inseparability of volatility and jump risk in the VIX can be a possible explanation of the current findings in this paper.

We introduce a risk management framework to assess food security, which is interpreted as the probability of fatality or adverse health effects due to lack of food and which is a product of food availability, access, and vulnerability.

We derive cost-minimizing policies to achieve food security objectives by addressing availability, access, and vulnerability, and taking into account how randomness, uncertainty, and heterogeneity affect the system.

Ignoring key sources of variability, particularly heterogeneity, may lead to biases because food security policies require targeting the most vulnerable populations, which may each have unique features such as age, location, and health status. Establishing any policy solution requires making tough choices about policy criteria. Outcomes will differ when the criteria is to minimize overall risk or to minimize risk to the most vulnerable.

Policies addressing food security crises should balance enhanced supply with targeting available food and the provision of emergency health services to vulnerable populations.

The purpose of this paper is to generalize the traditional risk evaluation methods and to specify a multi-level risk evaluation framework, in order to prepare customized risk evaluation and to enable effectively integrating the elements of risk evaluation.

A real case study of an electric motor manufacturing company is presented to illustrate the advantages of this new framework compared to the traditional and fuzzy failure mode and effect analysis (FMEA) approaches.

The essence of the proposed total risk evaluation framework (TREF) is its flexible approach that enables the effective integration of firms’ individual requirements by developing tailor-made organizational risk evaluation.

Increasing product/service complexity has led to increasingly complex yet unique organizational operations; as a result, their risk evaluation is a very challenging task. Distinct structures, characteristics and processes within and between organizations require a flexible yet robust approach of evaluating risks efficiently. Most recent risk evaluation approaches are considered to be inadequate due to the lack of flexibility and an inappropriate structure for addressing the unique organizational demands and contextual factors. To address this challenge effectively, taking a crucial step toward customization of risk evaluation.

The authors analyze the equilibrium effects of non-tradable assets on optimal policy portfolios. They study how the existence of non-tradable assets impacts optimal asset allocation decisions of investors who own such assets and of investors who do not have access to non-tradable assets.

In this theoretical analysis, the authors analyze a model with tradable and non-tradable asset classes whose cash flows are jointly normally distributed. There are two types of investors, with and without access to non-tradable assets. All investors have constant absolute risk aversion preferences. The authors derive closed form solutions for optimal investor demand and equilibrium asset prices. They calibrated the model using US data for listed equity, bonds and private equity. Further, the authors illustrate the sensitivities of quantities and prices with respect to the main parameters.

The study finds that the existence of non-tradable assets has a large impact on optimal asset allocation. Investors with (without) access to non-tradable assets tilt their portfolios of tradable assets away from (toward) assets to which non-tradable assets exhibit positive betas.

The model provides important insights not only for investors holding non-tradable assets such as private equity but also for investors who do not have access to non-tradable assets. Investors who ignore the effect of non-tradable assets when reverse-engineering risk premia from asset covariances and market capitalizations might severely underestimate the equity risk premium.

The authors provide the first comprehensive analysis of the equilibrium effects of non-tradability of some assets on optimal policy portfolios. Thus, this paper goes beyond analyzing the effects of market imperfections on individual portfolio choices.

The paper aims to analyse the drivers of changes in European equity tail risk.

For this purpose, the paper uses a panel data model with fixed effects based on five explanatory variables including the VIX, the variance risk premium (VRP), the one-year lagged slope of the riskless term-structure, the default spread and market-specific illiquidity via the measure of Bao et al. (2011). The study analyses a comprehensive database of representative European equity indices from February 2003 to December 2013. The database just contains markets of euro member states to avoid biases due to different currencies. To measure equity tail risk, the ex post realized value-at-risk was used.

There is empirical evidence that the VIX, the VRP and the default spread are key determinants of equity tail risk changes across all markets. Moreover, the results reveal that market-specific illiquidity is an important determinant in PIIGS markets and the one-year lagged term-structure slope in core markets. The analysis also documents that market-specific risk premia are a relevant determinant of equity tail risk changes. Another finding is that risk premia in PIIGS markets are basically higher as in core markets, which reflect the higher risk involved in investing in PIIGS markets.

The paper offers a unique perspective on equity tail risk in aggregate equity markets and helps both investors and risk managers to get a comprehensive understanding of relevant drivers.

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.

The purpose of this paper is to study how the market correlation changes in Chinese stock market and how the market correlation affects stock returns.

The authors first examine the relationship between the market correlation and the market return. Then, the authors run formal multiple regressions to see whether correlation risk is priced in security returns.

The authors find that market correlation increases when the market index falls down. Though market correlation risk is partly influenced by macroeconomic shocks, volatility risk, liquidity risk and higher moment risk, market correlation contains unique information that measures the benefit investors gain from diversification strategies. The market correlation risk is negatively priced. This conclusion remains valid even if the authors have considered the influence of other risk factors and the impact of conditional information.

Subjected to the limited history of the Chinese stock market, the authors cannot use more accurate and specific empirical methodology to fulfill the empirical research. And this renders further study.

This research provides empirical evidence in a new data sample and it sheds lights on correlation strategies for institutional investors in China.

This study investigates how this pandemic impacted the systemic risk in Indonesia’s Islamic commercial banks (ICBs) and conventional commercial banks (CCBs). The authors use quantitative methods, and systemic risk is measured using value at risk (VaR) and Conditional Value at Risk (CoVaR). This study provides empirical evidence regarding the estimation and determination of systemic risk. By using spillover measures, the authors find a significant increase in systemic risk among the sample banks. The novelty in this research is the measurement of the level of banking risk in the dual banking system in Indonesia. This study makes profound contributions to the literature and suggests various policy recommendations, including identifying essential institutions and testing the benefits of policy responses in containing systemic risk. These findings need to be considered by the government and financial authorities in making accurate regulations and policies.