Frank Schlottmann

A hybrid heuristic approach to discrete multi-objective optimization of credit portfolios

A hybrid heuristic approach combining multi-objective evolutionary and problem-specific local search methods is proposed to support the risk-return analysis of credit portfolios. Its goal is to compute approximations of discrete sets of Pareto-efficient portfolio structures concerning both the respective portfolio return and the respective portfolio risk using the non-linear, non-convex Credit-Value-at-Risk downside risk measure which is relevant to real world credit portfolio optimization. In addition, constraints like capital budget restrictions are considered in the hybrid heuristic framework. The computational complexity of selected parts of the algorithm is analyzed. Moreover, empirical results indicate that the hybrid method is superior in convergence speed to a non-hybrid evolutionary approach and finds approximations of risk-return efficient portfolios within reasonable time.

Handbook on Information Technology in Finance

This Handbook contains surveys of state-of-the-art concepts, systems, applications, best practices as well as contemporary research in the intersection between IT and finance. Included are recent trends and challenges, IT systems and architectures in finance, essential developments and case studies on management information systems, service oriented architecture modelling, IT architectures for securities trading, IT-systems in banking, process-oriented systems in corporate treasuries, grid computing and networking. The IT applications in banking, trading and insurance cover risk management and controlling, financial portals, electronic payment and others. In addition, also finance-related IT applications in non-financial companies are considered. The concept-oriented part of the book focuses on IT methods in finance like financial models and modelling financial data, planning and processes, security, algorithms and complexity.

A multi-objective approach to integrated risk management

The integrated management of financial risks represents one of the main challenges in contemporary banking business. Deviating from a rather silo-based approach to risk management banks put increasing efforts into aggregating risks across different risk types and also across different business units to obtain an overall risk picture and to manage risk and return on a consolidated level. Up to now no state-of-the-art approach to fulfill this task has emerged yet. Risk managers struggle with a number of important issues including unstable and weakly founded correlation assumptions, inconsistent risk metrics and differing time horizons for the different risk types. In this contribution we present a novel approach that overcomes parts of these unresolved issues. By defining a multi-objective optimization problem we avoid the main drawback of other approaches which try to aggregate different risk metrics that do not fit together. A MOEA is a natural choice in our multi-objective context since some common real-world objective functions in risk management are non-linear and non-convex. To illustrate the use of a MOEA, we apply the NSGA-II to a sample real-world instance of our multi-objective problem. The presented approach is flexible with respect to modifications and extensions concerning real-world risk measurement methodologies, correlation assumptions, different time horizons and additional risk types.

Modern Heuristics for Finance Problems: A Survey of Selected Methods and Applications

The high computational complexity of many problems in financial decision-making has prevented the development of time-efficient deterministic solution algorithms so far. At least for some of these problems, e.g., constrained portfolio selection or non-linear time series prediction problems, the results from complexity theory indicate that there is no way to avoid this problem. Due to the practical importance of these problems, we require algorithms for finding optimal or near-optimal solutions within reasonable computing time. Hence, heuristic approaches are an interesting alternative to classical approximation algorithms for such problems. Over the last years many interesting ideas for heuristic approaches were developed and tested for financial decision-making. We present an overview of the relevant methodology, and, some applications that show interesting results for selected problems in finance.

Integrated Risk Management: Risk Aggregation and Allocation Using Intelligent Systems

In recent years integrated approaches have become state-of-the-art practice for risk management in financial institutions. Contrary to the still common silo-based approach where risk categories and business lines are predominantly analyzed separately, an integrated risk management system adopts an enterprisewide perspective to appropriately account for cross-sectional dependencies between all significant banking risks. In this contribution an application of intelligent systems that provides management with risk-return efficient bank-wide asset allocation strategies is outlined. It is based on multi-objective evolutionary algorithms and considers different market risks and credit risk as well as position volume constraints. The presented novel approach is not only able to integrate the differing goals concerning the risk management function but also to partly overcome the obstacles for risk integration and aggregation. Using real market data a sample portfolio analysis is performed and possible conclusions for a bank risk manager are drawn. The approach is extendable concerning for instance advanced risk measurement methodologies, correlation assumptions, different time horizons and additional risk types. Further real-world constraints, such as regulatory capital, portfolio or P&L restrictions can also be easily integrated into the model.

Comparison of multi-objective evolutionary algorithms in optimizing combinations of reinsurance contracts

Our paper concerns optimal combinations of different types of reinsurance contracts. We introduce a novel approach based on the Mean-Variance-Criterion to solve this task. Two state-of-the-art MOEAs are used to perform an optimization of yet unresolved problem instances. In addition to that, we focus on finding a dense set of solutions to derive analogies to theoretic results of easier problem instances.

Heuristic Optimization of Reinsurance Programs and Implications for Reinsurance Buyers

Reinsurance contracts represent a very important tool for insurance companies to manage their risk portfolio. In general, they are used if an insurer is not willing or not able to hold certain risk exposures or parts thereof on its own. There exist two main contract types to cede claims to a reinsurer, namely proportional and non-proportional ones. With the quota share reinsurance, a well-known variant of the former ones, a fixed percentage of the claim sizes is ceded to the reinsurance company. Excess of loss and stop loss are non-proportional types and the reinsurer is only liable to pay if certain losses are exceeded. In practice insurance companies usually place a number of different reinsurance contracts, a so-called reinsurance program.

Finding Constrained Downside Risk-Return Efficient Credit Portfolio Structures Using Hybrid Multi-Objective Evolutionary Computation

In contemporary credit portfolio management, the portfolio risk-return analysis of financial instruments using certain downside credit risk measures requires the computation of a set of Pareto-efficient portfolio structures in a non-linear, non-convex setting. For real-world problems, additional constraints, e.g. supervisory capital limits, have to be respected. Particularly for formerly non-traded instruments, e.g. corporate loans, a discrete set of decision alternatives has to be considered for each instrument.

Die Skalierung der Preisschwankungen an einem virtuellen Kapitalmarkt mit probabilistischen und trendverfolgenden Agenten

In diesem Artikel wird ein virtueller Kapitalmarkt vorgestellt, der zur Untersuchung finanzwirtschaftlicher Fragestellungen von uns entwickelt wird und sich an Komponenten des XETRACB®1-Systems der Deutschen Borse AG orientiert. Ziel ist es, die Strukturen der Handler und des Marktes gezielt festzulegen, um einerseits bestimmte Phanomene realer Kapitalmarkte mit Hilfe von Informatikmethoden zu untersuchen, die durch die klassische Finance-Theorie nicht erklart werden konnen und um andererseits intelligente Softwareagenten zu entwickeln bzw. zu trainieren, die spater an realen Kapitalmarkten einsetzbar sind. Ferner werden die Ergebnisse einer ersten Studie mit zwei einfachen Handlertypen dargestellt, in der die Skalierung der Preisschwankungen bezuglich des Zeitablaufes in einem simulierten Marktmodell untersucht wurde. Diese wird im Kontext der Struktur der modellierten Marktteilnehmer interpretiert. Schlieslich werden Pramissen fur eine Untersuchung des Einflusses von Insider-Handlern im Marktmodell entwickelt, die ein explizites Wissen uber die Struktur des Marktes besitzen und ausnutzen.

Discovery of Risk-Return Efficient Structures in Middle-Market Credit Portfolios

We discuss a hybrid approach that combines Multi-Objective Evolutionary Algorithms and quantitative methods of portfolio credit risk management to support the discovery of downside risk-return efficient structures in middle-market credit portfolios. In an empirical study, we compare the performance of the solutions discovered by our hybrid method to the solutions found by a corresponding non-hybrid algorithm on two different real-world loan portfolios.