Operational risk management and new computational needs in banks

Basel II banking regulation introduces new needs for computational schemes. They involve both optimal stochastic control, and large scale simulations of decision processes of preventing low-frequency high loss-impact events. This paper will first state the problem and present its parameters. It then spells out the equations that represent a rational risk management behavior and link together the variables: Levy processes are used to model operational risk losses, where calibration by historical loss databases is possible ; where it is not the case, qualitative variables such as quality of business environment and internal controls can provide both costs-side and profits-side impacts. Among other control variables are business growth rate, and efficiency of risk mitigation. The economic value of a policy is maximized by resolving the resulting Hamilton-Jacobi-Bellman type equation. Computational complexity arises from embedded interactions between 3 levels: * Programming global optimal dynamic expenditures budget in Basel II context, * Arbitraging between the cost of risk-reduction policies (as measured by organizational qualitative scorecards and insurance buying) and the impact of incurred losses themselves. This implies modeling the efficiency of the process through which forward-looking measures of threats minimization, can actually reduce stochastic losses, * And optimal allocation according to profitability across subsidiaries and business lines. The paper next reviews the different types of approaches that can be envisaged in deriving a sound budgetary policy solution for operational risk management, based on this HJB equation. It is argued that while this complex, high dimensional problem can be resolved by taking some usual simplifications (Galerkin approach, imposing Merton form solutions, viscosity approach, ad hoc utility functions that provide closed form solutions, etc.) , the main interest of this model lies in exploring the scenarios in an adaptive learning framework ( MDP, partially observed MDP, Q-learning, neuro-dynamic programming, greedy algorithm, etc.). This makes more sense from a management point of view, and solutions are more easily communicated to, and accepted by, the operational level staff in banks through the explicit scenarios that can be derived. This kind of approach combines different computational techniques such as POMDP, stochastic control theory and learning algorithms under uncertainty and incomplete information. The paper concludes by presenting the benefits of such a consistent computational approach to managing budgets, as opposed to a policy of operational risk management made up from disconnected expenditures. Such consistency satisfies the qualifying criteria for banks to apply for the AMA (Advanced Measurement Approach) that will allow large economies of regulatory capital charge under Basel II Accord.REGULAR - Operational risk management, HJB equation, Levy processes, budget optimization, capital allocation

Modèles de mesure du risque opérationnel : quelle convergence dans les banques ?

Advanced Measurement Approach models of operational risk : a major task for banks Building Advanced Measurement Approach models of operational risk has been a major task for banks seeking to comply with Basel II these last three years. Because of the elusive nature of operational risk, scarce loss data, loose qualitative inputs, a plethora of statistical functions to try out and also because any model must be supported by its organizational and managerial counterpart, banks have been confronted at first with too many diverging choices. Gradually, as regulatory constraints made themselves felt, and the difficulties of keeping different components consistent with each other, general architectural and computational options have narrowed down to a few, apparently reliable choices. Today, though technical level components still reflect idiosyncratic development paths, banks op risk approach offer more comparability. JEL classification : G21, G28, K23Les grands groupes bancaires ont investi dans la construction de modèles pratiques permettant de mesurer l’étendue du risque opérationnel dans le respect des orientations de l’Accord Bâle II. Des facteurs historiques et techniques ont d’abord entraîné des divergences dans la conception même de ces modèles, mais les difficultés et contraintes pratiques induisent actuellement une convergence des architectures et des modes de calcul. L’examen en détail des mises en oeuvre révèle encore de nombreuses différences qui compliquent la tâche des superviseurs nationaux. Classification JEL : G21, G28, K23Pham-Hi Duc. Modèles de mesure du risque opérationnel : quelle convergence dans les banques ?. In: Revue d'économie financière, n°84, 2006. Le risque opérationnel. pp. 25-45

Optimizing Automated Trading Systems with Deep Reinforcement Learning

In this paper, we propose a novel approach to optimize parameters for strategies in automated trading systems. Based on the framework of Reinforcement learning, our work includes the development of a learning environment, state representation, reward function, and learning algorithm for the cryptocurrency market. Considering two simple objective functions, cumulative return and Sharpe ratio, the results showed that Deep Reinforcement Learning approach with Double Deep Q-Network setting and the Bayesian Optimization approach can provide positive average returns. Among the settings being studied, Double Deep Q-Network setting with Sharpe ratio as reward function is the best Q-learning trading system. With a daily trading goal, the system shows outperformed results in terms of cumulative return, volatility and execution time when compared with the Bayesian Optimization approach. This helps traders to make quick and efficient decisions with the latest information from the market. In long-term trading, Bayesian Optimization is a method of parameter optimization that brings higher profits. Deep Reinforcement Learning provides solutions to the high-dimensional problem of Bayesian Optimization in upcoming studies such as optimizing portfolios with multiple assets and diverse trading strategies

Etude technico-economique. Prospective de dimensionnement de centrales electro solaires a tour

SIGLECNRS RP 440 (241) / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc