Peter Stahlecker

Bio: Peter Stahlecker is an academic researcher from University of Hamburg. The author has contributed to research in topics: Mean squared error & Estimator. The author has an hindex of 7, co-authored 31 publications receiving 153 citations.

A portfolio selection model using fuzzy returns

Abstract: We study a static portfolio selection problem, in which future returns of securities are given as fuzzy sets. In contrast to traditional analysis, we assume that investment decisions are not based on statistical expectation values, but rather on maximal and minimal potential returns resulting from the so-called α-cuts of these fuzzy sets. By aggregating over all α-cuts and assigning weights for both best and worst possible cases we get a new objective function to derive an optimal portfolio. Allowing for short sales and modelling α-cuts in ellipsoidal shape, we obtain the optimal portfolio as the unique solution of a simple optimization problem. Since our model does not include any stochastic assumptions, we present a procedure, which turns the data of observable returns as well as experts' expectations into fuzzy sets in order to quantify the potential future returns and the investment risk.

The Minimax, the Minimin, and the Hurwicz Adjustment Principle

Abstract: In this paper the Hurwicz decision rule is applied to an adjustment problem concerning the decision whether a given action should be improved in the light of some knowledge on the states of nature or on other actors' behaviour. In comparison with the minimax and the minimin adjustment principles the general Hurwicz rule reduces to these specific classes whenever the underlying loss function is quadratic and knowledge is given by an ellipsoidal set. In the framework of the adjustment model discussed in this paper Hurwicz's optimism index can be interpreted as a mobility index representing the actor's attitude towards new external information. Examples are given that serve to illustrate the theoretical findings.

Fuzzy prior information and minimax estimation in the linear regression model

Abstract: We consider the linear regression modely=Xβ+u with prior information on the unknown parameter vector β. The additional information on β is given by a fuzzy set. Using the mean squared error criterion we derive linear estimators that optimally combine the data with the fuzzy prior information. Our approach generalizes the classical minimax procedure firstly proposed by Kuks and Olman.

Precautionary Saving in the Small and in the Large

Abstract: The theory of precautionary saving is shown in this paper to be isomorphic to the Arrow-Pratt theory of risk aversion, making possible the application of a large body of knowledge about risk aversion to precautionary saving, and more generally, to the theory of optimal choice under risk In particular, a measure of the strength of precautionary saving motive analogous to the Arrow-Pratt measure of risk aversion is used to establish a number of new propositions about precautionary saving, and to give a new interpretation of the Oreze-Modigliani substitution effect

Convexity, Classification, and Risk Bounds

Abstract: Many of the classification algorithms developed in the machine learning literature, including the support vector machine and boosting, can be viewed as minimum contrast methods that minimize a convex surrogate of the 0–1 loss function. The convexity makes these algorithms computationally efficient. The use of a surrogate, however, has statistical consequences that must be balanced against the computational virtues of convexity. To study these issues, we provide a general quantitative relationship between the risk as assessed using the 0–1 loss and the risk as assessed using any nonnegative surrogate loss function. We show that this relationship gives nontrivial upper bounds on excess risk under the weakest possible condition on the loss function—that it satisfies a pointwise form of Fisher consistency for classification. The relationship is based on a simple variational transformation of the loss function that is easy to compute in many applications. We also present a refined version of this result in the...