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203
Correlation And Dependence In Risk Management: Properties And Pitfalls
 RISK MANAGEMENT: VALUE AT RISK AND BEYOND
, 1999
"... Modern risk management calls for an understanding of stochastic dependence going beyond simple linear correlation. This paper deals with the static (nontimedependent) case and emphasizes the copula representation of dependence for a random vector. Linear correlation is a natural dependence measure ..."
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Cited by 195 (30 self)
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Modern risk management calls for an understanding of stochastic dependence going beyond simple linear correlation. This paper deals with the static (nontimedependent) case and emphasizes the copula representation of dependence for a random vector. Linear correlation is a natural dependence measure for multivariate normally and, more generally, elliptically distributed risks but other dependence concepts like comonotonicity and rank correlation should also be understood by the risk management practitioner. Using counterexamples the falsity of some commonly held views on correlation is demonstrated; in general, these fallacies arise from the naive assumption that dependence properties of the elliptical world also hold in the nonelliptical world. In particular, the problem of finding multivariate models which are consistent with prespecified marginal distributions and correlations is addressed. Pitfalls are highlighted and simulation algorithms avoiding these problems are constructed. ...
A Multivalued Logic Approach to Integrating Planning and Control
 Artificial Intelligence
, 1995
"... Intelligent agents embedded in a dynamic, uncertain environment should incorporate capabilities for both planned and reactive behavior. Many current solutions to this dual need focus on one aspect, and treat the other one as secondary. We propose an approach for integrating planning and control base ..."
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Cited by 106 (8 self)
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Intelligent agents embedded in a dynamic, uncertain environment should incorporate capabilities for both planned and reactive behavior. Many current solutions to this dual need focus on one aspect, and treat the other one as secondary. We propose an approach for integrating planning and control based on behavior schemas, which link physical movements to abstract action descriptions. Behavior schemas describe behaviors of an agent, expressed as trajectories of control actions in an environment, and goals can be defined as predicates on these trajectories. Goals and behaviors can be combined to produce conjoint goals and complex controls. The ability of multivalued logics to represent graded preferences allows us to formulate tradeoffs in the combination. Two composition theorems relate complex controls to complex goals, and provide the key to using standard knowledgebased deliberation techniques to generate complex controllers. We report experiments in planning and execution on a mobi...
DecisionTheoretic Foundations of Qualitative Possibility Theory
 European Journal of Operational Research
, 2000
"... This paper presents a justification of two qualitative counterparts of the expected utility criterion for decision under uncertainty, which only require bounded, linearly ordered, valuation sets for expressing uncertainty and preferences. This is carried out in the style of Savage, starting with ..."
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Cited by 51 (7 self)
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This paper presents a justification of two qualitative counterparts of the expected utility criterion for decision under uncertainty, which only require bounded, linearly ordered, valuation sets for expressing uncertainty and preferences. This is carried out in the style of Savage, starting with a set of acts equipped with a complete preordering relation. Conditions on acts are given that imply a possibilistic representation of the decisionmaker uncertainty. In this framework, pessimistic (i.e., uncertaintyaverse) as well as optimistic attitudes can be explicitly captured. The approach thus proposes an operationally testable description of possibility theory. 1
Soft Computing: the Convergence of Emerging Reasoning Technologies
 Soft Computing
, 1997
"... The term Soft Computing (SC) represents the combination of emerging problemsolving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to so ..."
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Cited by 50 (8 self)
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The term Soft Computing (SC) represents the combination of emerging problemsolving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to solve complex, realworld problems. After a brief description of each of these technologies, we will analyze some of their most useful combinations, such as the use of FL to control GAs and NNs parameters; the application of GAs to evolve NNs (topologies or weights) or to tune FL controllers; and the implementation of FL controllers as NNs tuned by backpropagationtype algorithms.
On the OutofSample Importance of Skewness and Asymmetric Dependence for Asset Allocation
 Journal of Financial Econometrics
, 2004
"... Recent studies in the empirical finance literature have reported evidence of two types of asymmetries in the joint distribution of stock returns. The first is skewness in the distribution of individual stock returns. The second is an asymmetry in the dependence between stocks: stock returns appear t ..."
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Cited by 47 (3 self)
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Recent studies in the empirical finance literature have reported evidence of two types of asymmetries in the joint distribution of stock returns. The first is skewness in the distribution of individual stock returns. The second is an asymmetry in the dependence between stocks: stock returns appear to be more highly correlated during market downturns than during market upturns. In this article we examine the economic and statistical significance of these asymmetries for asset allocation decisions in an outofsample setting. We consider the problem of a constant relative risk aversion (CRRA) investor allocating wealth between the riskfree asset, a smallcap portfolio, and a largecap portfolio. We use models that can capture timevarying moments up to the fourth order, and we use copula theory to construct models of the timevarying dependence structure that allow for different dependence during bear markets than bull markets. The importance of these two asymmetries for asset allocation is assessed by comparing the performance of a portfolio based on a normal distribution model with a portfolio based on a more flexible distribution model. For investors with no shortsales constraints, we find that knowledge of higher moments and asymmetric dependence leads to gains that are economically significant and statistically significant in some cases. For short salesconstrained investors the gains are limited.
Label Ranking by Learning Pairwise Preferences
"... Preference learning is an emerging topic that appears in different guises in the recent literature. This work focuses on a particular learning scenario called label ranking, where the problem is to learn a mapping from instances to rankings over a finite number of labels. Our approach for learning s ..."
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Cited by 46 (16 self)
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Preference learning is an emerging topic that appears in different guises in the recent literature. This work focuses on a particular learning scenario called label ranking, where the problem is to learn a mapping from instances to rankings over a finite number of labels. Our approach for learning such a mapping, called ranking by pairwise comparison (RPC), first induces a binary preference relation from suitable training data using a natural extension of pairwise classification. A ranking is then derived from the preference relation thus obtained by means of a ranking procedure, whereby different ranking methods can be used for minimizing different loss functions. In particular, we show that a simple (weighted) voting strategy minimizes risk with respect to the wellknown Spearman rank correlation. We compare RPC to existing label ranking methods, which are based on scoring individual labels instead of comparing pairs of labels. Both empirically and theoretically, it is shown that RPC is superior in terms of computational efficiency, and at least competitive in terms of accuracy.
Angular Brushing of Extended Parallel Coordinates
 in: Proceedings of IEEE Symposium on Information Visualization
, 2002
"... In this paper we present several extensions to the wellknown InfoViz technique of parallel coordinates, mainly concentrating on brushing and focus+context visualization. First, we propose angular brushing as a new approach to highlight rational dataproperties, i.e., features which depend on two da ..."
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Cited by 37 (4 self)
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In this paper we present several extensions to the wellknown InfoViz technique of parallel coordinates, mainly concentrating on brushing and focus+context visualization. First, we propose angular brushing as a new approach to highlight rational dataproperties, i.e., features which depend on two data dimensions (instead of one). We also demonstrate smooth brushing of parallel coordinates as an intuitive tool for specifying nonbinary degreeofinterest functions (then used for F+C visualization). Thirdly, we show how composite brushes provide lots of flexibility during data exploration. Additionally, we also present several further and more general extensions to parallel coordinates which there are (a) histograms to be used as axis overlays, (b) interaction features such as axis reordering, flipping, scaling and panning, and (c) detail on demand, implemented as a mouseover effect.