Search results

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.

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.

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.

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.

An additional management analysis tool for applying information‐gap modeling to credit risk has been provided.

This paper provides a new methodology for analyzing credit risk based on information‐gap decision theory.

Considering that uncertain factors widely exist in engineering practice, an adaptive collocation method (ACM) is developed for the structural fuzzy uncertainty analysis.

ACM arranges points in the axis of the membership adaptively. Through the adaptive collocation procedure, ACM can arrange more points in the axis of the membership where the membership function changes sharply and fewer points in the axis of the membership where the membership function changes slowly. At each point arranged in the axis of the membership, the level-cut strategy is used to obtain the cut-level interval of the uncertain variables; besides, the vertex method and the Chebyshev interval uncertainty analysis method are used to conduct the cut-level interval uncertainty analysis.

The proposed ACM has a high accuracy without too much additional computational efforts.

A novel ACM is developed for the structural fuzzy uncertainty analysis.

The purpose of this paper is to clarify a number of important facts about info‐gap decision theory.

Theorems are put forward to rebut claims made about info‐gap decision theory in papers published in this journal and elsewhere.

Info‐gap's robustness model is a simple instance of the most famous model in classical decision theory for the treatment of decision problems subject to severe uncertainty, namely Wald's maximin model. This simple instance is the equivalent of the well‐established model known universally as radius of stability. Info‐gap's robustness model has an inherent local orientation. Therefore, it is in principle unable to address the fundamental difficulties presented by the type of severe uncertainty that is postulated by info‐gap decision theory.

These findings caution against accepting the assertions made in the info‐gap literature about: info‐gap decision theory's role and place in decision making under severe uncertainty; and its ability to model, analyze, and manage severe uncertainty.

This paper exposes the serious difficulties with claims made in papers published in this journal and elsewhere regarding the place and role of info‐gap decision theory in decision theory and its ability to handle severe uncertainty.

To study the effect of Knightian uncertainty – as opposed to statistical estimation error – in the evaluation of value‐at‐risk (VaR) of financial investments. To develop methods for augmenting existing VaR estimates to account for Knightian uncertainty.

The value at risk of a financial investment is assessed as the quantile of an estimated probability distribution of the returns. Estimating a VaR from historical data entails two distinct sorts of uncertainty: probabilistic uncertainty in the estimation of a probability density function (PDF) from historical data, and non‐probabilistic Knightian info‐gaps in the future size and shape of the lower tail of the PDF. A PDF is estimated from historical data, while a VaR is used to predict future risk. Knightian uncertainty arises from the structural changes, surprises, etc., which occur in the future and therefore are not manifested in historical data. This paper concentrates entirely on Knightian uncertainty and does not consider the statistical problem of estimating a PDF. Info‐gap decision theory is used to study the robustness of a VaR to Knightian uncertainty in the distribution.

It is shown that VaRs, based on estimated PDFs, have no robustness to Knightian errors in the PDF. An info‐gap safety factor is derived that multiplies the estimated VaR in order to obtain a revised VaR with specified robustness to Knightian error in the PDF. A robustness premium is defined as a supplement to the incremental VaR for comparing portfolios.

The revised VaR and incremental VaR augment existing tools for evaluating financial risk.

Info‐gap theory, which underlies this paper, is a non‐probabilistic quantification of uncertainty that is very suitable for representing Knightian uncertainty. This enables one to assess the robustness to future surprises, as distinct from existing statistical techniques for assessing estimation error resulting from randomness of historical data.

This paper aims to provide a new quantitative methodology for predicting turning points and trends in financial markets time series based on information‐gap decision theory.

Uncertainty in future returns from financial markets is modeled using information‐gap decision theory. The robustness function, which measures immunity to uncertainty, yields an additional time series whose turning points anticipate and reflect those of the underlying financial market time series.

The robustness function falling above or below certain thresholds is shown to provide a new reliable technical indicator for predicting highs and lows in financial markets. In addition, iterates of the robustness function are shown in certain cases to predict trends in financial markets.

In the analysis and application presented here the authors have only considered a special case of the robustness function. Stricter performance requirements and alternative process model estimates for future returns could be included in the information‐gap model formulation and analysis.

An additional technical trading tool for applying Information‐Gap theory to financial markets has been provided.

This paper provides a new reliable methodology for constructing technical indicators for use by traders and fund managers in financial markets.

When organizations with disparate cultures are merged, the culture of the acquired organization often represents a counterculture for the acquiring firm. Scholars and consultants frequently recommend avoiding integration of an acquired company if it has a sharply different culture. This paper presents a case study of a recent hostile takeover that disproves the conventional wisdom and shows that careful implementation processes enable the company, not only to overcome post‐merger integration barriers due to culture clash, but also to maximize strategic benefits from those cultural differences. It shows that integration can be achieved through a process in which only some specific cultural dimensions are integrated while others are preserved.

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.

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.

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.

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.

An additional methodology for applying information‐gap modeling to portfolio management has been provided.

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

Numerical models are being increasingly relied upon to evaluate wind turbine performance by simulating phenomena that are infeasible to measure experimentally. These numerical models, however, require a large number of input parameters that often need to be calibrated against available experiments. Owing to the unavoidable scarcity of experiments and inherent uncertainties in measurements, this calibration process may yield non-unique solutions, i.e. multiple sets of parameters may reproduce the available experiments with similar fidelity. The purpose of this paper is to study the trade-off between fidelity to measurements and the robustness of this fidelity to uncertainty in calibrated input parameters.

Here, fidelity is defined as the ability of the model to reproduce measurements and robustness is defined as the allowable variation in the input parameters with which the model maintains a predefined level of threshold fidelity. These two vital attributes of model predictiveness are evaluated in the development of a simplified finite element beam model of the CX-100 wind turbine blade.

Findings of this study show that calibrating the input parameters of a numerical model with the sole objective of improving fidelity to available measurements degrades the robustness of model predictions at both tested and untested settings. A more optimal model may be obtained by calibration methods considering both fidelity and robustness. Multi-criteria Decision Making further confirms the conclusion that the optimal model performance is achieved by maintaining a balance between fidelity and robustness during calibration.

Current methods for model calibration focus solely on fidelity while the authors focus on the trade-off between fidelity and robustness.

During this month the average librarian is given furiously to think over the estimates, and in this year, perhaps more than any other, will that adverb be applicable. The matter is so important that we do not apologise for dealing with it once more. In March in nearly every town there will be a determined effort by men who call themselves “economists” to reduce the appropriation for public libraries. The war is the most handsome excuse that the opponents of public culture have ever had for their attacks upon the library movement. It is obvious that these attacks will take the direction of an endeavour to reduce the penny rate, where this has not been done already. In the year that has passed retrenchment has been the watchword of all municipal work, and many librarians have either ceased to buy new books or have bought only those of vital importance. This has meant that a certain amount of money usually devoted to books has accumulated. Seeing that legally money which has been raised for library purposes cannot be expended in any other direction, the only way in which the “economists” can work is to propose a reduction of next year's rate by an amount corresponding to the balance. It is an extraordinary thing that after decades of demonstration the average local public man cannot or will not see that money taken from the funds of a public library cannot be restored to it later. The limitation of the penny rate is nearly always forgotten or ignored, and the common phrase of such men: “You must economise now and we will give you more money after the war,” has been heard by most librarians. An endeavour should be made to drive home the fact that retrenchment in books, or in other matters in connexion with libraries, now means so much actual irreparable loss to the libraries. We have dealt several times in these pages with the vexed question of balances. Practice differs so much in different localities that it seems impossible to get any universal ruling in connexion with this matter. Many libraries have been able to invest their balances in some form of war loan ; in others the librarian has been told emphatically that such investment is illegal. We can speak of towns within five miles of each other in one of which money has been invested, and in the other investment is banned in this way. Unfortunately librarians have been rather silent upon this point, and it is difficult to obtain any reliable information as to how many towns have investments. It would strengthen the hands of many librarians if they knew that in so many other municipalities the library funds were so invested.

The purpose of this paper is to propose a robust reliability index to characterize the structural safety degree.

On the basis of the interval theory, a new interval reliability analysis method that the structural basic variables are described by the interval lower limit and interval length to characterize the structural uncertainty is proposed in this paper.

A novel structural reliability index solution method is proposed. Besides, both linear and non-linear problem of solving interval non-probabilistic reliability are further discussed in this paper.

Based on interval theory, variables are described by interval lower limit and interval length to characterize the structural uncertainty. A novel structural reliability index solution method is proposed.