Finance

Abstract

Purpose: The purpose of this paper is to provide a quantitative methodology based on information-gap decision theory for dealing with (true) Knightian uncertainty in the management of portfolios of assets with uncertain returns.

Design/methodology/approach: Portfolio managers aim to maximize returns for given levels of risk. Since future returns on assets are uncertain the expected return on a portfolio of assets can be subject to significant uncertainty. Information-gap decision theory is used to construct portfolios that are robust against uncertainty.

Findings: Using the added dimensions of aspirational parameters and performance requirements in information-gap theory, the paper shows that one cannot simultaneously have two robust-optimal portfolios that outperform a specified return and a benchmark portfolio unless one of the portfolios has arbitrarily large long and short positions.

Research limitations/implications: The paper has considered only one uncertainty model and two performance requirements in an information-gap analysis over a particular time frame. Alternative uncertainty models could be introduced and benchmarking against proxy portfolios and competitors are examples of additional performance requirements that could be incorporated in an information-gap analysis.

Practical implications: An additional methodology for applying information-gap modeling to portfolio management has been provided.

Originality/value: This paper provides a new and novel approach for managing portfolios in the face of uncertainties in future asset returns.

Keywords

Financial modelling, Information management, Portfolio investment, Uncertainty management

Abstract

Purpose: The paper aims to provide a quantitative methodology for dealing with (true) Knightian uncertainty in the management of credit risk based on information-gap decision theory.

Design/methodology/approach: Credit risk management assigns clients to credit risk categories with estimated probabilities of default for each category. Since probabilities of default are subject to uncertainty the estimated expected loss given default on a loan-book can be subject to significant uncertainty. Information-gap decision theory is applied to construct optimal loan-book portfolios that are robust against uncertainty.

Findings: By choosing optimal interest-rate ratios among the credit risk categories one can simultaneously satisfy regulatory requirements on expected losses and an institution’s aspirations on expected profits.

Research limitations/implications: In the analysis presented here only defaults over specific time frames have been considered. However, performance requirements expressed in terms of defaults and profits over multiple time frames that allow for transitions of clients between credit risk categories over time could also be incorporated into an information-gap analysis.

Practical implications: An additional management analysis tool for applying information-gap modeling to credit risk has been provided.

Originality/value: This paper provides a new methodology for analyzing credit risk based on information-gap decision theory.

Keywords

Credit control, Credit management, Financial modelling, Financial risk, Information modelling

Abstract

Uncertainty accompanies our life processes and covers almost all fields of scientific studies. Two general categories of uncertainty, namely, aleatory uncertainty and epistemic uncertainty, exist in the world. While aleatory uncertainty refers to the inherent randomness in nature, derived from natural variability of the physical world (e.g., random show of a flipped coin), epistemic uncertainty origins from human’s lack of knowledge of the physical world, as well as ability of measuring and modeling the physical world (e.g., computation of the distance between two cities). Different kinds of uncertainty call for different handling methods. Aggarwal, Yu, Sarma, and Zhang et al. have made good surveys on uncertain database management based on the probability theory. This paper reviews multidisciplinary uncertainty processing activities in diverse fields. Beyond the dominant probability theory and fuzzy theory, we also review information-gap theory and recently derived uncertainty theory. Practices of these uncertainty handling theories in the domains of economics, engineering, ecology, and information sciences are also described. It is our hope that this study could provide insights to the database community on how uncertainty is managed in other disciplines, and further challenge and inspire database researchers to develop more advanced data management techniques and tools to cope with a variety of uncertainty issues in the real world.

Keywords

Uncertainty management, probability theory, Dempster-Shafer theory, fuzzy theory, info-gap theory, probabilistic database, fuzzy database