GAICF proposes a framework for managing generative AI risks in banking.
problem Generative AI's impact on financial decision-making and governance.
method SR 26-2-compatible governance framework for generative AI.
result GAICF aligns generative AI practices with SR 26-2 supervisory expectations.
Modeling financial systemic risk with optimal control theory for stability.
problem Analyzing and stabilizing systemic risk in interconnected financial entities.
method Developed a theoretical model using optimal control theory, including steps for synthesizing stabilizing controllers.
result The model ensures that the H∞ norms of the mappings from disturbance to output are less than a predefined constant, stabilizing the system. GAICF proposes a framework for governing generative AI in banking.
problem Generative AI's impact on financial decision-making and governance.
method SR 26-2-compatible governance framework for generative AI applications.
result GAICF aligns generative AI practices with SR 26-2 supervisory expectations.
This study designs a financial risk control platform using big data and machine learning.
problem Traditional risk management models are inadequate for modern financial complexities.
method Big data mining, real-time streaming data processing, statistical analysis, and precise customer behavior mining.
result The platform effectively identifies and responds to potential risks in real-time.
This paper improves risk control for financial markets by calibrating VaR forecasts using conformal methods.
problem Nonstationary and regime-dependent losses in financial markets.
method Regime-weighted conformal risk control (RWC) for VaR forecasting.
result RWC improves regime-conditional stability in some settings with modest conservativeness changes.
Motivated by the AIG bailout case in the financial crisis of 2007-2008, we consider an insurer who wants to maximize the expected utility of the terminal wealth by selecting optimal investment and risk control strategies. The insurer's risk process is modelled by a jump-diffusion process and is negatively correlated wi…
GenAI offers financial benefits but requires risk management.
problem Managing risks in financial applications of AI.
method Balancing AI's potential with risk control strategies.
result Proper risk management is essential for AI growth in finance.
Paper proposes optimal investment and reinsurance strategies considering financial and insurance risks dependence.
problem Optimal investment and reinsurance strategies under dependent financial and insurance risks.
method Stochastic control approach to maximize expected exponential utility of terminal wealth.
result Minimal dependence between financial and insurance risks significantly impacts investment and reinsurance strategies.
Financial institutions face new model risks with AI, requiring enhanced model risk management.
problem New model risks from Generative AI applications in financial institutions.
method Enhanced model risk framework with additional testing and controls.
result Financial institutions need to enhance their model risk management for Generative AI applications.
Examines financial risks' impact on EU-15 economic growth.
problem The impact of financial risks on economic growth in EU-15.
method Panel estimated generalized least squares method with additional control variables.
result Financial risks significantly impact economic growth in EU-15.
The insufficient understanding of the credit network structure was recognized as a key factor for regulators' underestimation of the destructive systematic risk during the financial crisis that started in 2007. The existing credit network research either took a macro perspective to clarify the topological properties of…
Regshock visualizes financial risks to help regulators manage systemic shocks.
problem Managing systemic risks in financial networks.
method Risk-island visualization algorithm and regshock visual exploration approach.
result Demonstrated improved risk management and control capabilities.
New risk measure improves creditor protection in financial regulation.
problem Current solvency requirements fail to control the size of recovery on creditors' claims.
method Developed Recovery Value at Risk (Recovery VaR) to control recovery on creditors' claims.
result Recovery VaR flexibly controls recovery on creditors' claims and integrates protection needs into management incentives.
Online surveillance detects systemic risk in financial markets.
problem Detecting and monitoring systemic risk in financial markets.
method Online monitoring procedures for multiple series, controlling for false rejections.
result Procedures allow timely detection of financial distress.
We propose a numerical recipe for risk evaluation defined by a backward stochastic differential equation. Using dual representation of the risk measure, we convert the risk valuation to a stochastic control problem where the control is a certain Radon-Nikodym derivative process. By exploring the maximum principle, we s…
The global crisis of 2008 provoked a heightened interest among scientists to study the phenomenon, its propagation and negative consequences. The process of modelling the spread of a virus is commonly used in epidemiology. Conceptually, the spread of a disease among a population is similar to the contagion process in e…
This paper considers an optimal control of a big financial company with debt liability under bankrupt probability constraints. The company, which faces constant liability payments and has choices to choose various production/business policies from an available set of control policies with different expected profits and…
PRUDEX-Compass evaluates FinRL methods on 6 axes for financial market investments.
problem Insufficient evaluation of FinRL methods in financial markets.
method Introduces PRUDEX-Compass with 6 axes and 17 measures for evaluation.
result Demonstrates the effectiveness of PRUDEX-Compass on 4 real-world datasets.
Optimal investment and risk control strategies for insurers are derived using a time-consistent approach.
problem Optimal investment and risk control for insurers under mean-variance criterion.
method Introducing a deterministic forward auxiliary process to formulate a time-consistent problem.
result Optimal strategy and value function obtained in closed-form for the new problem.
Paper optimizes financial trading strategies under uncertain market conditions.
problem Guaranteeing robust positive expected profits in financial systems.
method Transformed semi-infinite constraints into structured policies and proposed a novel graphical approach.
result Demonstrated superior risk-adjusted returns and downside risk compared to conventional strategies.
Central bank optimizes bailout cash injection to limit defaults.
problem Optimizing cash injection to limit defaults in a system of mutual obligations.
method Proved convergence and solved a drift controlled Stefan problem using mean field control and policy gradient methods.
result Optimal strategies involve subsidizing banks with equity values in a time-dependent region.
Delta hedging, which plays a crucial rôle in modern financial engineering, is a tracking control design for a "risk-free" management. We utilize the existence of trends in financial time series (Fliess M., Join C.: A mathematical proof of the existence of trends in financial time series, Proc. Int. Conf. Systems Theory…
In this paper, we consider the asset-liability management under the mean-variance criterion. The financial market consists of a risk-free bond and a stock whose price process is modeled by a geometric Brownian motion. The liability of the investor is uncontrollable and is modeled by another geometric Brownian motion. W…
Optimal reinsurance strategy found to minimize financial risk.
problem Minimizing financial risk in insurance companies through optimal reinsurance.
method Solving the problem via neural networks and a Cramér-Lundberg model.
result Optimal reinsurance strategy found to control terminal wealth and ruin probability.
Two firms compete in a financial market, choosing dividend strategies to avoid default and maximize profits.
problem Strategic interaction between two financially constrained firms in a market with default risk.
method Construct Nash equilibria in feedback form for a class of two-person stochastic games of singular control.
result Explicit identification of optimal strategies and equilibrium payoffs for different initial conditions.
New AI governance framework tackles risks in finance.
problem Risks from evolving AI models in finance.
method Agent-based framework with modular governance architecture.
result Controls quarantine harmful behavior in real time.
Banks in the interbank network can not assess the true risks associated with lending to other banks in the network, unless they have full information on the riskiness of all the other banks. These risks can be estimated by using network metrics (for example DebtRank) of the interbank liability network which is availabl…
Study optimal portfolio selection with Recovery Average Value at Risk, showing better control over liabilities.
problem Optimizing portfolios with a new risk measure under known or uncertain distributions.
method Existence results for mean-risk optimal portfolios under different distributional assumptions.
result Portfolio selection under Recovery Average Value at Risk provides better control over liabilities.
The global financial crisis in 2007-2009 demonstrated that systemic risk can spread all over the world through a complex web of financial linkages, yet we still lack fundamental knowledge about the evolution of the financial web. In particular, interbank credit networks shape the core of the financial system, in which …
Financial markets have developed a lot of strategies to control risks induced by market fluctuations. Mathematics has emerged as the leading discipline to address fundamental questions in finance as asset pricing model and hedging strategies. History began with the paradigm of zero-risk introduced by Black & Scholes st…
The paper examines sizing strategies for algorithmic trading in volatile markets.
problem High volatility creates challenges for algorithmic traders.
method Investigates different sizing models and backtesting techniques for financial trading.
result Sizing models can lower Value at Risk (VaR) during crisis events.
Optimal bailout policies identified for financial institutions using AI.
problem Managing systemic financial risk during crises.
method Modelled bailout decisions as a Markov Decision Process (MDP) with network dynamics.
result Identified optimal investment policies to limit financial crises effects.
We review the recently introduced concept of variety of a financial portfolio and we sketch its importance for risk control purposes. The empirical behaviour of variety, correlation, exceedance correlation and asymmetry of the probability density function of daily returns is discussed. The results obtained are compared…
In addition to constraining bilateral exposures of financial institutions, there are essentially two options for future financial regulation of systemic risk (SR): First, financial regulation could attempt to reduce the financial fragility of global or domestic systemically important financial institutions (G-SIBs or D…
This research improves value-at-risk estimation during financial crises using non-extensive statistical methods.
problem Underestimation of value-at-risk during financial crises.
method Non-extensive value-at-risk model based on Tsallis entropy and q-Gaussian probability density function.
result The q-Gaussian model provides better value-at-risk estimation during financial crises.
We refine Expected Shortfall by controlling different tail portions, offering tailored risk assessments.
problem Risk assessment in financial positions, especially in tail regions.
method Introducing adjusted Expected Shortfall measures that control different tail portions.
result Adjusted Expected Shortfall measures ensure risk does not exceed specified thresholds for various probability levels.
Paper uses DRL to optimize portfolios, balancing risk and return.
problem Optimizing portfolios under market uncertainty and risk constraints.
method Integrates Sharpe ratio-based reward with risk control mechanisms, uses PPO for adaptive asset allocation.
result DRL agent stabilizes volatility but sacrifices risk-adjusted returns.
Computing risk measures of a financial portfolio comprising thousands of derivatives is a challenging problem because (a) it involves a nested expectation requiring multiple evaluations of the loss of the financial portfolio for different risk scenarios and (b) evaluating the loss of the portfolio is expensive and the …
Study examines credit risk's impact on Vietnamese banks' financial performance.
problem Impact of credit risk on commercial banks' financial performance in Vietnam.
method Dynamic Difference Generalized Method of Moments (dynamic Difference GMM) approach to address autocorrelation, non-constant variance, and endogeneity issues.
result ROE and NIM persist from one year to the next, while NPLR negatively affects ROA and ROE.
Derivative-informed models improve financial surrogates for accurate hedging and risk management.
problem Developing fast surrogate models for financial derivatives and risk quantities.
method Derivative-informed operator learning framework combining neural operators, random features, and tangent sensitivity equations.
result The framework reduces hedging and risk errors by 40-76% compared to standard surrogates.
Tail-Safe hedging uses reinforcement learning with a safety layer to manage financial risks.
problem Managing financial risks in derivatives trading with robustness and explainability.
method Combines distributional reinforcement learning with a CBF-QP safety layer to enforce financial constraints.
result Improves risk management without degrading central performance and avoids hard constraint violations.
Evaluation of systemic risk in networks of financial institutions in general requires information of inter-institution financial exposures. In the framework of Debt Rank algorithm, we introduce an approximate method of systemic risk evaluation which requires only node properties, such as total assets and liabilities, a…
L2GMOM learns financial networks and optimizes momentum strategies.
problem Expensive databases and financial expertise limit network construction accessibility.
method End-to-end machine learning framework (L2GMOM) that learns networks and optimizes trading signals.
result Significant improvement in portfolio profitability and risk control with Sharpe ratio of 1.74.
Editorial discusses nine challenges in modern algorithmic trading.
problem Challenges in modern algorithmic trading and controls.
method Discussion of challenges without proposing solutions.
result No specific new results or findings.
Combines RL and BF for risk-managed portfolio optimization.
problem Risk management in RL-based portfolio optimization under high volatility.
method Integrates reinforcement learning with barrier functions for dynamic risk control.
result Demonstrates superior performance in real-world data compared to RL-only approaches.
Paper studies systemic robustness in financial networks using particle systems.
problem Budget control and default risk in regional financial networks.
method Mean-field particle system approach, McKean-Vlasov equations, asymptotic analysis.
result Systemic robustness measured by the proportion of surviving entities in large particle systems.
Financial portfolio management is one of the problems that are most frequently encountered in the investment industry. Nevertheless, it is not widely recognized that both Kelly Criterion and Risk Parity collapse into Mean Variance under some conditions, which implies that a universal solution to the portfolio optimizat…
The paper optimizes insurance purchases for financial goals.
problem Maximizing probability of achieving financial goals with insurance.
method Analyzes deferred term insurance in deterministic and stochastic frameworks, considering income, consumption, and risky investment.
result Provides optimal insurance and investment strategies for achieving financial goals.