Optimal Investment in a Levy Market

In this paper we consider the optimal investment problem in a market where the stock price process is modeled by a geometric Levy process (taking into account jumps). Except for the geometric Brownian model and the geometric Poissonian model, the resulting models are incomplete and there are many equivalent martingale measures. However, the model can be completed by the so-called power-jump assets. By doing this we allow investment in these new assets and we can try to maximize the expected utility of these portfolios. As particular cases we obtain the optimal portfolios based in stocks and bonds, showing that the new assets are superfluous for certain martingale measures that depend on the utility function we use.     

Quantitative aspects of gradient HPLC of copolymers from styrene and ethyl methacrylate

Summary Prerequisite of quantitative evaluation in chromatography is equivalence of sample composition and detector signal. This includes complete retention and proper elution of all sample constituents. In polymer HPLC, complete retention requires a poor starting eluent, a sufficiently active column, and a low ratio of injection volume to column volume. On small pore columns, insufficient retention caused the polymer to elute either in the interstitial volume (sample exclusion), together with the sample solvent, or immediately after the solvent plug.Stat-copoly(styrene/ethyl methacrylate) samples are more difficultly retained thanstat-copoly(styrene/acrylonitrile) specimes. With the former copolymer it could be shown that incomplete retention did not cause sample demixing. In order to gain complete retention, non-exclusion HPLC of polymers should be performed with columns whose solvent volume is at least 50 times as large as the injection volume. This consequence is of practical importance in chromatographic cross-fractionation where rather large volumes of SEC eluate are injected into the apparatus for gradient HPLC.     

Rejoinder on “Statistical reasoning with set-valued information: Ontic vs. epistemic views”

This note replies to comments made on our contribution to the Low Quality Data debate.     

An object-parameter approach to predicting unknown data in incomplete fuzzy soft sets

Incomplete data in soft sets lead to uncertainty and inaccuracy in representing and handling information. This paper introduces notions of complete distance between two objects and relative dominance degree between two parameters. Based on both the notions, an object-parameter method is proposed to predict unknown data in incomplete fuzzy soft sets. The proposal makes full use of known data, including the information from the relationship between known values of all objects on a certain parameter and the information from the relationship between known values of an object on all parameters. The effectiveness of the proposal is verified by many examples under the compared investigation of classical predicted methods.     

On subgame perfect equilibria in quantum Stackelberg duopoly with incomplete information

The Li–Du–Massar quantum duopoly model is one of the generally accepted quantum game schemes. It has applications in a wide range of duopoly problems. Our purpose is to study Stackelberg's duopoly with incomplete information in the quantum domain. The result of Lo and Kiang has shown that the correlation of players' quantities caused by the quantum entanglement enhances the first-mover advantage in the game. Our work demonstrates that there is no first-mover advantage if the players' actions are maximally correlated. Furthermore, we proved that the second mover gains a higher equilibrium payoff than the first one.     

Solution of the least squares method problem of pairwise comparison matrices

The aim of the paper is to present a new global optimization method for determining all the optima of the Least Squares Method (LSM) problem of pairwise comparison matrices. Such matrices are used, e.g., in the Analytic Hierarchy Process (AHP). Unlike some other distance minimizing methods, LSM is usually hard to solve because of the corresponding nonlinear and non-convex objective function. It is found that the optimization problem can be reduced to solve a system of polynomial equations. Homotopy method is applied which is an efficient technique for solving nonlinear systems. The paper ends by two numerical example having multiple global and local minima. This research was supported in part by the Hungarian Scientific Research Fund, Grant No. OTKA K 60480.     

Evaluation of insurance products with guarantee in incomplete markets

Life insurance products are usually equipped with minimum guarantee and bonus provision options. The pricing of such claims is of vital importance for the insurance industry. Risk management, strategic asset allocation, and product design depend on the correct evaluation of the written options. Also regulators are interested in such issues since they have to be aware of the possible scenarios that the overall industry will face. Pricing techniques based on the Black & Scholes paradigm are often used, however, the hypotheses underneath this model are rarely met.To overcome Black & Scholes limitations, we develop a stochastic programming model to determine the fair price of the minimum guarantee and bonus provision options. We show that such a model covers the most relevant sources of incompleteness accounted in the financial and insurance literature. We provide extensive empirical analyses to highlight the effect of incompleteness on the fair value of the option, and show how the whole framework can be used as a valuable normative tool for insurance companies and regulators.     

On spectral radii of matrices governing some numerical estimating algorithms with incomplete data

Summary Many estimating procedures are carried out with incomplete data by means of different types of EM algorithms. They allow us to obtain maximum likelihood parameter estimates in classical inference and also estimates based on the posterior mode in Bayesian inference. This paper analyzes in detail the spectral radii of the Jacobian matrices algorithm as a possible way to evaluate convergence rates. The eigenvalues of such matrices are explicitly obtained in some cases and, in all of them, a geometric convergence rate is, at least, guaranteed near the optimum. Finally, a comparison between the leading eigenvalues of EM and direct and approximate EM-Bayes algorithms may suggest the efficiency of each case.     

不完全信息随机截尾广义线性模型的极大似然估计

该文在回归子给定和随机两种情形下,分别定义了不完全信息随机截尾广义线性模型.在一定的条件下,讨论了这两种模型参数向量的似然方程解的存在性和唯一性,获得并证明了这两种模型的极大似然估计(MLE)的相合性与渐近正态性.     

A monotonicity property of the composition of regularized and inverted-regularized gamma functions with applications

The gamma function and its various modifications such as the (upper) incomplete, regularized and inverted-regularized incomplete gamma functions are of importance in both theory and applications. In this note we observe an ‘if and only if ’ relationship between a certain axiom of insurance risk management and a monotonicity property of the composition of regularized and inverted-regularized incomplete gamma functions, assuming that the risks follow gamma distributions. We derive the monotonicity property by utilizing the above noted relationship and a probabilistic technique. The aforementioned insurance axiom, called consistent no-undercut, is explained in detail and related to several techniques of analysis.