Conditional risk measures and their associated risk contribution measures are commonly employed in finance and actuarial science for evaluating systemic risk and quantifying the effects of risk interactions. This paper introduces various types of contribution ratio measures based on the multivariate conditional value-at-risk (MCoVaR), multivariate conditional expected shortfall (MCoES), and multivariate marginal mean excess (MMME) studied in [34] (Ortega-Jiménez, P., Sordo, M., & Suárez-Llorens, A. (2021). Stochastic orders and multivariate measures of risk contagion. Insurance: Mathematics and Economics, vol. 96, 199–207) and [11] (Das, B., & Fasen-Hartmann, V. (2018). Risk contagion under regular variation and asymptotic tail independence. Journal of Multivariate Analysis 165(1), 194–215) to assess the relative effects of a single risk when other risks in a group are in distress. The properties of these contribution risk measures are examined, and sufficient conditions for comparing these measures between two sets of random vectors are established using univariate and multivariate stochastic orders and statistically dependent notions. Numerical examples are presented to validate these conditions. Finally, a real dataset from the cryptocurrency market is used to analyze the spillover effects through our proposed contribution measures.

This paper examines the impact of financially constrained intermediate inputs on within-industry total factor productivity loss. Utilizing exogenous tax reforms in China as a natural experiment, our difference-in-difference analysis reveals that reduced tax burdens lead to increased firm-level intermediate inputs, particularly among financially constrained firms. We incorporate financially constrained intermediate inputs into a partial equilibrium model of firm dynamics. Our calibration suggests that financially constrained intermediate inputs play a quantitatively more important role in accounting for misallocation than financially constrained capital. The presence of financially constrained intermediate inputs introduces a downward bias in the measurement of value-added productivity, especially for firms in the top decile of gross-output productivity. As a result, the average “efficient” levels of capital and labor for the top decile firms in the standard Hsieh and Klenow (2009) exercise are lower than what is truly efficient.

In the traditional multidimensional credibility models developed by Jewell ((1973) Operations Research Center, pp. 73–77.), the estimation of the hypothetical mean vector involves complex matrix manipulations, which can be challenging to implement in practice. Additionally, the estimation of hyperparameters becomes even more difficult in high-dimensional risk variable scenarios. To address these issues, this paper proposes a new multidimensional credibility model based on the conditional joint distribution function for predicting future premiums. First, we develop an estimator of the joint distribution function of a vector of claims using linear combinations of indicator functions based on past observations. By minimizing the integral of the expected quadratic distance function between the proposed estimator and the true joint distribution function, we obtain the optimal linear Bayesian estimator of the joint distribution function. Using the plug-in method, we obtain an explicit formula for the multidimensional credibility estimator of the hypothetical mean vector. In contrast to the traditional multidimensional credibility approach, our newly proposed estimator does not involve a matrix as the credibility factor, but rather a scalar. This scalar is composed of both population information and sample information, and it still maintains the essential property of increasingness with respect to the sample size. Furthermore, the new estimator based on the joint distribution function can be naturally extended and applied to estimate the process covariance matrix and risk premiums under various premium principles. We further illustrate the performance of the new estimator by comparing it with the traditional multidimensional credibility model using bivariate exponential-gamma and multivariate normal distributions. Finally, we present two real examples to demonstrate the findings of our study.

A method to hedge variable annuities in the presence of basis risk is developed. A regime-switching model is considered for the dynamics of market assets. The approach is based on a local optimization of risk and is therefore very tractable and flexible. The local optimization criterion is itself optimized to minimize capital requirements associated with the variable annuity policy, the latter being quantified by the Conditional Value-at-Risk (CVaR) risk metric. In comparison to benchmarks, our method is successful in simultaneously reducing capital requirements and increasing profitability. Indeed the proposed local hedging scheme benefits from a higher exposure to equity risk and from time diversification of risk to earn excess return and facilitate the accumulation of capital. A robust version of the hedging strategies addressing model risk and parameter uncertainty is also provided.

Many seasonal workers are housed in transitory accommodations, including tents and vehicles. In this study, we analyze the supply side of this problem by assuming that a public agent must house the workers through direct public investment. A peak load model is adapted to develop investment rules for the least-cost provision of seasonal worker housing, adding an interacting multi-season component to existing models. Based on this model and the data from three prototype projects, the majority of the least-cost investment would be in permanent, but seasonally occupied, housing.

We use survey data to study the degree to which new farming operations inAlabama were financially constrained after the 2008 financial crisis. Next,we control for farmers' self-selection out of the credit market and identifywhich farmers were able to secure loans during the period of 2009–2010. Theresults show that new farmers that started any part of their operation after2005 were financially constrained but no evidence that their financingconstraints were affected by the crisis. As expected, we find that lendingwas collateral-driven, although lenders also considered farmers'profitability and cash flows.

The reform of corporate governance is again on the agenda in the wake of Enron and excessive risk-taking by financial institutions. However, the search for new and better forms of governance often seems to lack guiding principles. A theory of corporate governance ought to emerge from a theory of the firm. Yet, the literature shows how this project is both difficult and far from complete. In this paper we review how existing theory provides a variety of arguments favouring either a shareholder or a stakeholder orientation. These arguments may depend on whether the prime objective for governance is improved current performance or a more long-term focus for firms. A brief review of recent US governance reforms is given as a backdrop to discussing more far-reaching proposals that have emerged in the recent literature; a greater role for institutional investors on the one hand or a return to managerial capitalism on the other.

Trigger values are derived for investment projects that are affected by uncertainty and construction periods. The importance of both aspects on the decision to invest is shown by simulation results and empirically corroborated by estimation results obtained with national (non-)residential structures and equipment data. The main conclusion is that time-to-build projects faced with uncertainty, like non-residential structures, are disproportionally more affected than projects that do not have either of these aspects.