The COVID-19 pandemic has caused significant global economic disruptions, particularly increasing non-performing loans (NPLs) in the banking sector. This study examines the impact of bank specific factors and macroeconomics on NPLs in banks across ASEAN countries. Panel data from 39 publicly listed banks over the period 2010–2024 were examined using the Generalized Method of Moments (GMM) and system GMM approach. The results indicate that lagged NPLs, return on equity, total assets, credit growth, GDP growth, and lending interest rates significantly influence NPLs, while the Tier 1 capital ratio, non-interest income, and unemployment rate show no significant effect. This study provides important implications for bank management and policymakers in enhancing credit risk management and strengthening banking supervision within the ASEAN region.

This study focuses on a two-stage hybrid framework for dynamic portfolio optimization. While existing two-stage approaches often rely mainly on price data, symmetric downside measures, or static allocation, they provide limited insight into fundamental efficiency and how assets behave during severe market downturns. In the first stage, Data Envelopment Analysis (DEA) is used to pre-select efficient stocks based on fundamental financial indicators. In the second stage, several risk-based allocation strategies are applied to the selected assets, including mean-standard deviation, semi-deviation, conditional value at risk, maximum Sharpe ratio, and a proposed mean-asymmetric downside risk model. This proposed model introduces an exponential penalty function to emphasize larger losses and better capture downside risk during market downturns. Portfolio performance is evaluated using a rolling-window approach with weekly rebalancing over a 65-week out-of-sample period. The DEA-based selection effectively reduced the investment universe from 36 to 9 efficient stocks. Empirical results show that the simple DEA+1/N portfolio outperformed the SET50 benchmark, and the proposed DEA+mAsymRisk delivered higher returns with lower downside risk compared to alternative strategies. These findings highlight that combining fundamental screening with downside-sensitive allocation strengthens portfolio robustness and offers practical value for dynamic risk management in financial markets.

Corporations must comply with various laws and regulations, subject to their markets and industry. To manage their compliance risks, corporations are expected to design and implement compliance programs based on risk assessments. This study investigates the impact of risk assessments on the implementation of recommended practices in Corporations’ Compliance Programs (CCPs). Through survey interviews with compliance officers from 93 Forbes 2000 companies, the research examines the relationship between risk levels and 33 recommended practices across Anti-Bribery & Corruption, Data Privacy, and Third-Party compliance risks. Contrary to the initial hypothesis, findings reveal that only nine practices significantly relate to risk levels, including rule-based policies and compliance training testing. Unexpectedly, several practices showed negative relations, particularly in the Third-Party compliance domain, suggesting that higher risk levels do not always lead to broader implementation of recommended practices. The study uncovers mixed results in the Anti-Bribery & Corruption CCP, limited risk-based alignment in the Third-Party CCP, and better alignment in the Data-Privacy CCP. These findings suggest that the relationship between risk and compliance implementation is domain-specific and may be influenced by whether the risk is perceived as core (e.g., Data Privacy) or non-core (e.g., Third-Party). They highlight the need for improved regulatory alignment with corporate practices and further exploration of CCP impacts on risk management. This study offers a novel empirical contribution by systematically examining the link between risk levels and the implementation of specific compliance practices across three compliance areas, providing a granular benchmark for future research.

This pragmatic research strives to reveal the return volatility transmission throughout Asian stock exchanges, by employing variance decomposition technique of Vector autoregressive (VAR) based framework. Additionally, the current examination exerts a Granger causality approach to detect short-term cause and effect among the stock exchanges. The consequence of volatility spill-over exhibits the dominancy of Indian, Chinese and Japanese exchanges in terms of net volatility transmitter. Further, it is found that Korean, Thai, and Malaysian stock exchanges seem to be net receiver of volatility in Asia. Additionally, the outcome of current investigation reveals neutrality of Bangladeshi and Pakistani stock exchange, as the returns volatility of these stock exchange are not influenced by any other Asian stock exchanges. Furthermore, the result of Granger causality analysis signifies the existence of unidirectional causality among the Asian stock exchanges. In terms of policy implication, it is imperative for investors and policymakers to closely monitor the behaviour of the Japanese stock exchange, as it plays a significant role as a net transmitter of volatility to other stock exchanges in Asia. By keeping a vigilant eye on the Japanese stock exchange, investors can better assess and manage potential risks and opportunities in the region.

Shapley value theory, which originally emerged from cooperative game theory, was established for the purpose of measuring the exact contribution of agents playing the game. Subsequently, the Shapley value was used in finance to decompose the risk of optimal portfolios, attributing to the various assets their exact contribution to total risk and return. In the present paper, the Shapley value results of Shalit [Annals of Finance 17(1) (2021), 1–25] are extended by using weighted Shapley values to decompose the risk of optimal portfolios. The weighted concept, as axiomatized by Kalai and Samet [Journal of Game Theory 16(3) (1987), 205–222], provides a solution to cooperative games when the symmetry of players cannot be justified. The weighted Shapley value theory is applied to model efficient mean-variance portfolios and price their constituents. The computation is carried out for the 13 most traded US stocks in 2020 and the results are compared with the standard Shapley values.

Implementing risk management to an entire organization, though being increasingly applied, may appear challenging, often perceived as requiring substantial effort with unclear benefits. This paper aims at providing road transport organizations and practitioners with key references and a roadmap for designing and implementing Enterprise Risk Management. The methodology relies on synthesizing existing good practices from the road transport sector. A brief overview of state-of-art practices with applications using different structures are provided and implementation drivers, expected benefits and maturity levels are highlighted. Practical considerations and key success factors are discussed, followed by the suggestion of an integrated implementation framework including key concepts and tools.

This paper is an empirical study investigating the effect of using a fractional Black–Scholes model for pricing Call options in comparison to the classical Black–Scholes model. We estimate the Hurst parameter by nine different methods available in software R, for a set of fifteen assets from four d ifferent industries and for various lengths of used historical data. We use implied volatility for calculation of option price with a one month time to maturity. As a main result, we provide a table of tendencies of suitability for using each of the Hurst exponent estimation methods depending on the length of underlying data set.

International audience

This paper provides finite-time performance bounds for the approximate policy iteration (API) algorithm in discounted Markov decision models. This is done for a class of approximation operators called averagers. An averager is a positive linear operator with norm equal to one with an additional con tinuity property. The class of averagers includes several of the usual interpolation schemes as lineal and multilinear approximations, kernel-based approximators among others. The API algorithm is studied under two settings. In the first one, the transition probability is completely known and the performance bounds are given in terms of the approximation errors and the stopping error of the policy iteration algorithm. In the second setting, the system evolution is given by a difference equation where the distribution of the random disturbance is unknown. Thus, in addition to the errors by the application of the API, in this case the performance bounds also depend on a statistical estimation error. The results are illustrated with numerical approximations for a class of inventory systems.

Pricing formulas for components of a portfolio of temperature-based weather derivatives, as well as the corresponding hedging formula, are derived using the recent general theory of neutral and indifference pricing and hedging in incomplete markets.