Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Methods to Estimate Dynamic Stochastic General Equilibrium Models

This paper employs the one-sector Real Business Cycle model as a testing ground for four different procedures to estimate Dynamic Stochastic General Equilibrium (DSGE) models. The procedures are: 1 ) Maximum Likelihood, with and without measurement errors and incorporating Bayesian priors, 2) Generalized Method of Moments, 3) Simulated Method of Moments, and 4) Indirect Inference. Monte Carlo analysis indicates that all procedures deliver reasonably good estimates under the null hypothesis. However, there are substantial differences in statistical and computational efficiency in the small samples currently available to estimate DSGE models. GMM and SMM appear to be more robust to misspecification than the alternative procedures. The implications of the stochastic singularity of DSGE models for each estimation method are fully discussed.

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

Méthode de simulation avec les variables antithétiques

Les fichiers qui accompagnent mon document ont été réalisés avec le logiciel Latex et les simulations ont été réalisés par Splus(R).

E-commerce system simulation for construction and demolition waste exchange

This article introduces a quantitative approach to e-commerce system evaluation based on the theory of process simulation. The general concept of e-commerce system simulation is presented based on the considerations of some limitations in e-commerce system development such as the huge amount of initial investments of time and money, and the long period from business planning to system development, then to system test and operation, and finally to exact return; in other words, currently used system analysis and development method cannot tell investors about some keen attentions such as how good their e-commerce system could be, how many investment repayments they could have, and which area they should improve regarding the initial business plan. In order to exam the value and its potential effects of an e-commerce business plan, it is necessary to use a quantitative evaluation approach and the authors of this article believe that process simulation is an appropriate option. The overall objective of this article is to apply the theory of process simulation to e-commerce system evaluation, and the authors will achieve this though an experimental study on a business plan for online construction and demolition waste exchange. The methodologies adopted in this article include literature review, system analysis and development, simulation modelling and analysis, and case study. The results from this article include the concept of e-commerce system simulation, a comprehensive review of simulation methods adopted in e-commerce system evaluation, and a real case study of applying simulation to e-commerce system evaluation. Furthermore, the authors hope that the adoption and implementation of the process simulation approach can effectively support business decision-making, and improve the efficiency of e-commerce systems.

Computer simulation of configurational properties of partially ionized polyelectrolytes

Monte Carlo simulation methods were used in order to study the conformational properties of partially ionized polyelectrolyte chains with Debye-Hückel screening in 1:1 electrolyte solution at room temperature. Configurational properties such as the distributions of probability for the square end to end distances, for the square radii of gyration and for the angles between polyion bonds were investigated as a function of the chain ionization and the salt concentration. © 1993.

A mesoscopic simulation method for electrolyte solutions

This thesis deals with the development of a novel simulation technique for macromolecules in electrolyte solutions, with the aim of a performance improvement over current molecular-dynamics based simulation methods. In solutions containing charged macromolecules and salt ions, it is the complex interplay of electrostatic interactions and hydrodynamics that determines the equilibrium and non-equilibrium behavior. However, the treatment of the solvent and dissolved ions makes up the major part of the computational effort. Thus an efficient modeling of both components is essential for the performance of a method. With the novel method we approach the solvent in a coarse-grained fashion and replace the explicit-ion description by a dynamic mean-field treatment. Hence we combine particle- and field-based descriptions in a hybrid method and thereby effectively solve the electrokinetic equations. The developed algorithm is tested extensively in terms of accuracy and performance, and suitable parameter sets are determined. As a first application we study charged polymer solutions (polyelectrolytes) in shear flow with focus on their viscoelastic properties. Here we also include semidilute solutions, which are computationally demanding. Secondly we study the electro-osmotic flow on superhydrophobic surfaces, where we perform a detailed comparison to theoretical predictions.

Ultracold Quantum Gases and Lattice Systems: Quantum Simulation of Lattice Gauge Theories

Abelian and non-Abelian gauge theories are of central importance in many areas of physics. In condensed matter physics, AbelianU(1) lattice gauge theories arise in the description of certain quantum spin liquids. In quantum information theory, Kitaev’s toric code is a Z(2) lattice gauge theory. In particle physics, Quantum Chromodynamics (QCD), the non-Abelian SU(3) gauge theory of the strong interactions between quarks and gluons, is nonperturbatively regularized on a lattice. Quantum link models extend the concept of lattice gauge theories beyond the Wilson formulation, and are well suited for both digital and analog quantum simulation using ultracold atomic gases in optical lattices. Since quantum simulators do not suffer from the notorious sign problem, they open the door to studies of the real-time evolution of strongly coupled quantum systems, which are impossible with classical simulation methods. A plethora of interesting lattice gauge theories suggests itself for quantum simulation, which should allow us to address very challenging problems, ranging from confinement and deconfinement, or chiral symmetry breaking and its restoration at finite baryon density, to color superconductivity and the real-time evolution of heavy-ion collisions, first in simpler model gauge theories and ultimately in QCD.

Cost and power analysis of linkage disequilibrium methods for locating genes influencing quantitative traits in humans

Linkage disequilibrium methods can be used to find genes influencing quantitative trait variation in humans. Linkage disequilibrium methods can require smaller sample sizes than linkage equilibrium methods, such as the variance component approach to find loci with a specific effect size. The increase in power is at the expense of requiring more markers to be typed to scan the entire genome. This thesis compares different linkage disequilibrium methods to determine which factors influence the power to detect disequilibrium. The costs of disequilibrium and equilibrium tests were compared to determine whether the savings in phenotyping costs when using disequilibrium methods outweigh the additional genotyping costs.^ Nine linkage disequilibrium tests were examined by simulation. Five tests involve selecting isolated unrelated individuals while four involved the selection of parent child trios (TDT). All nine tests were found to be able to identify disequilibrium with the correct significance level in Hardy-Weinberg populations. Increasing linked genetic variance and trait allele frequency were found to increase the power to detect disequilibrium, while increasing the number of generations and distance between marker and trait loci decreased the power to detect disequilibrium. Discordant sampling was used for several of the tests. It was found that the more stringent the sampling, the greater the power to detect disequilibrium in a sample of given size. The power to detect disequilibrium was not affected by the presence of polygenic effects.^ When the trait locus had more than two trait alleles, the power of the tests maximized to less than one. For the simulation methods used here, when there were more than two-trait alleles there was a probability equal to 1-heterozygosity of the marker locus that both trait alleles were in disequilibrium with the same marker allele, resulting in the marker being uninformative for disequilibrium.^ The five tests using isolated unrelated individuals were found to have excess error rates when there was disequilibrium due to population admixture. Increased error rates also resulted from increased unlinked major gene effects, discordant trait allele frequency, and increased disequilibrium. Polygenic effects did not affect the error rates. The TDT, Transmission Disequilibrium Test, based tests were not liable to any increase in error rates.^ For all sample ascertainment costs, for recent mutations ($<$100 generations) linkage disequilibrium tests were less expensive than the variance component test to carry out. Candidate gene scans saved even more money. The use of recently admixed populations also decreased the cost of performing a linkage disequilibrium test. ^

Post-­‐occupancy monitoring of two flats in Madrid: development and assessment of a mixed methods methodology

Recent research has shown large differences between the expected and the actual energy consumption in buildings. The differences have been attributed partially, to the assumptions made during the design phase of buildings when simulation methods are employed. More accurate occupancy profiles on building operation could help to carry out more precise building performance calculations. This study focuses on the post-occupancy evaluation of two apartments, one renovated and one non renovated, in Madrid within the same building complex. The aim of this paper is to present an application of the mixed-methods methodology (Creswell, 2007) to assess thermal comfort and occupancy practices used in the case studies, and to discuss the shortcomings and opportunities associated with it. The mixed-methods methodology offers strategies for integrating qualitative and quantitative methods to investigate complex phenomena. This approach is expected to contribute to the growing knowledge of occupants’ behaviour and building performance by explaining the differences observed between energy consumption and thermal comfort in relation to people’s saving and comfort practices and the related experiences, preferences and values.

Frog : design and simulation study of a mechanism which learns self preservative reactions to its environment.

"Contract no. US AEC AT (11-1)1469."

The small business executive decision simulation: administrator's manual

Appendix IB (p. 55-90): Operating manual.

Executive decision simulation computerized model,

Mode of access: Internet.

Multivariate analyses of variance and covariance for simulation studies involving normal time series

Photocopy.