This article studies a dynamic corporate risk management problem by considering the decision-making of risk-averse managers who exert costly effort and select project risk. We study how a Value-at-Risk (VaR) constraint affects managerial decisions and the distribution of firm value when the manager's objective is non-concave with a fixed salary and options. By the concavification technique, we analyze the optimal terminal firm value on the concave envelope of the objective function. Applying the quantile formulation and the martingale approach, we can derive explicit solutions for optimal effort, terminal firm value, and project choice. The optimal terminal firm value can be divided into nine cases by carefully discussing the choices of VaR floor and tail probability. Compared with the benchmark case, we find that a VaR manager will smooth terminal firm value across states, reducing it in good states while supporting it in adverse states. Moreover, a VaR requirement generally improves downside protection and reduces bankruptcy probability when the VaR floor is low or moderate. However, when the VaR floor is sufficiently high, it can increase bankruptcy probability and induce gambling-for-recovery behavior in adverse states. Our sensitivity analysis indicates that greater managerial effort uniformly improves firm value. Moreover, more incentive options make managers more responsible, leading to a smoother terminal firm value across states. In contrast, a high fixed salary makes the manager less responsible and ultimately causes a more dispersed firm value.
Option instruments are frequently used in corporate finance to reduce the risk of a decline in price asymmetrically and do not restrict the upward movement of the price. Research on the application of options in enterprise risk management. The above options are relatively more suitable for handling the firms' uncertain exposures and non-linear risks and demand for strategic flexibility. Options are relatively precise hedging instruments for foreign exchange risk, commodity price risk and interest-rate risk, etc., and can be used to manage equity exposure of a company. Based on the study of corporate option use and real options and option incentives, the four dimensions of value in this paper are downside protection, flexible hedging, strategic real-option value, and risk governance. In short, options are for speculation; however, if they are well-managed by the company, the risk can be reduced and firm value increased at the same time.
Ziyu Peng· International Journal of Fin...· 0 citations
This paper provides an overview of risk management in the insurance sector, combining theoretical principles with practical and regulatory perspectives. Starting with a simplified model of risk pooling, we demonstrate how diversification creates benefits for risk-averse policyholders. In a next step, we present a simple model to illustrate the main goal of quantitative risk management: The optimization of performance subject to a variety of constraints rather than a pure minimization of risks. We continue by reviewing the main fields of application of risk management and elaborate on the rationality of risk management due to market frictions. Finally, our discussion of solvency regulation and alternative policyholder protection mechanisms highlights the trade-offs between financial resilience, costs, and market efficiency. Overall, the paper demonstrates that, in addition to being a regulatory requirement, risk management in insurance is a strategic instrument for balancing policyholder protection, economic efficiency, and long-term sustainability.
Manuel Rach, H. Schmeiser· Journal of Business Economic...· 0 citations
The reallocation of state-owned capital is bringing a growing number of non-core and underperforming assets to market. Although many of these assets retain productive value, they differ in whether their existing use remains viable, whether suitable industry buyers can acquire them, and whether an alternative use can be established at reasonable cost. Treating them as a single class of distressed assets can lead to inefficient holding, financing, or redevelopment decisions. This paper develops a contingent framework for choosing among three divestiture routes: sale to an industry buyer, sale to financial or other non-industry investors, and limited repurposing before sale. Each route addresses a different constraint: weak buyer capacity, high information and due-diligence costs, or uncertainty about alternative use. The paper then explains how asset management companies can support each route through debt restructuring, creditor coordination, improved disclosure, buyer identification, and carefully limited investment. The framework shifts attention from the volume of assets acquired to the quality of route selection and the value realized through transfer to a more suitable owner.
Classical portfolio theory frequently assumes frictionless markets, but in reality, transaction costs, like fees and market impact, can erode returns and cause excessive turnover. Incorporating these costs transforms rebalancing from a mechanical rule into a strategic decision: determining exactly when and how much to trade to restore desirable exposures efficiently. This study proposes a dynamic rebalancing framework that explicitly models proportional transaction costs within a multi-period optimization setting. By introducing a transaction cost function, portfolio adjustment becomes a rigorous optimization problem balancing expected returns, risk exposure, and total rebalancing costs. This cost-aware framework empowers investors to avoid unnecessary trading, preserving risk control while improving net performance. To solve this, specific algorithmic procedures are designed to enhance computational efficiency in realistic, multi-asset scenarios. Empirical evaluations using historical financial data compare this cost-aware strategy against conventional periodic and threshold-based methods. Performance is assessed across metrics including cumulative return, portfolio volatility, turnover rate, and net returns after costs. The empirical results demonstrate that explicitly integrating transaction costs into optimization significantly improves strategy performance. The proposed model successfully reduces unnecessary trading activity and lowers portfolio turnover while maintaining competitive risk-adjusted returns. Furthermore, sensitivity analyses reveal that transaction cost levels dictate the optimal rebalancing frequency and adjustment magnitude. Overall, this study provides a systematic modeling framework and strong empirical evidence for cost-aware dynamic portfolio rebalancing, offering practical insights for investors navigating complex environments.
This paper studies optimal insurance design in a competitive market with one policyholder and multiple insurers. The policyholder’s risk preferences are modeled by a distortion risk measure, and the loss distribution depends on prevention effort, which reduces the loss amount; insurers price contracts using a common distortion premium principle. Insurers price contracts based on an effort benchmark. Because effort is unobservable, premiums cannot be conditioned on realized effort, creating moral hazard. The policyholder selects the optimal effort and indemnity to minimize the risk measure, taking into account the loss distribution, insurance cost, and effort cost; insurers design contracts to induce the policyholder to match the effort level assumed in the premium. Under this alignment, all insurers earn zero risk-adjusted profits in equilibrium, making them indifferent between offering coverage and not offering it, which is consistent with full competition among insurers. We focus on cases in which the policyholder’s distortion risk measure is value-at-risk or tail value-at-risk. The paper also examines relationships among parameters under exponentially distributed losses and extends the analysis to a general strictly concave distortion function for the policyholder. Under symmetric information, where insurers directly observe actual effort, we provide sufficient conditions under which the policyholder’s objective value is strictly lower than that under asymmetric information, thereby demonstrating moral hazard.
T. Boonen, Li-Wei Zheng· ASTIN Bulletin: The Journal...· 1 citation
This paper explores the multi-dimensional impact of FinTech on commercial banks' risk control from the perspectives of risk-taking, liquidity creation, and green credit with ESG. Based on a review of existing literature and theoretical analysis, this study finds that FinTech exerts a dual effect on bank risk management. On the one hand, technologies such as big data and artificial intelligence improve risk identification efficiency, optimize liquidity allocation, and enhance green credit and ESG performance by alleviating information asymmetry between banks and borrowers. On the other hand, intensified market competition may induce excessive risk-taking, while technical integration barriers, data governance defects, and regulatory lag constrain the positive role of FinTech. The effects also exhibit significant heterogeneity across banks with different ownership structures, scales, and digital capabilities, with state-owned banks generally better positioned to leverage the advantages of FinTech than small private banks. The synergy between internal governance and external supervision is therefore critical for balancing FinTech innovation and risk control. This study enriches the theoretical framework linking FinTech and bank risk control and provides practical references for commercial banks to strengthen risk management and for regulators to formulate targeted policies that promote the healthy integration of FinTech and traditional banking.