Model selection in a multi-hypothesis test setting: applications in financial econometrics

In this thesis, we investigate model selection in a general setting and perform several exercises in financial econometrics. We present the multi-hypothesis testing (MHT) framework, with which we design different type of model comparisons. We distinguish between test of model performance significance, of relative and absolute model performance and apply our framework to market risk forecasting model, to latent factor jump-diffusion models employed for the estimation of the statistical measure of an equity index, as well as to equity option pricing models. We develop original tests and, with regard to the proper exercise of model selection from an initial battery of models without any reference to a benchmark model, we combine the MHT approach with the model confidence set (MCS) to deliver a novel test of model comparison that is performed along with the established version of the MCS, as well as with an alternative simplified new MCS test that are detailed in the course of this work. We collect empirical evidence concerning model comparison in several subjects. With respect to market risk forecasting models, we have found that models capturing volatility clustering or targeting directly an auto-correlated conditional distribution percentile, perform better than the target model set and in particular, better than the historical simulation, widely employed by practitioners, and better than the so called RiskMetrics model. With respect to the equity index data dynamics, we have found that the popular affine jump-diffusion model requires a CEV augmentation to perform appropriately and that those models are slightly overperformed by an alternative stochastic volatility model, characterised by stochastic hazard with high frequency small jumps. The test performed over a large model set employed in the option pricing exercise points to a wide similarity of the results obtained by the many model specifications of the superior exponential volatility model, therefore suggesting a more careful adjustment of the model complexity. The model selection framework has proven very flexible in dealing with the varied collection of statistical problems. In particular, our main contribution represented by the generalised MHT based MCS test provides a method for model selection that is robust to finite sample distribution and that has the advantage of an adjustable tolerance for false rejections, allowing conservative to aggressive testing profiles

Proceedings of USM-AUT International Conference 2012 Sustainable Economic Development: Policies and Strategies

This proceedings includes papers presented at the USM-AUT International Conference (UAIC 2012) carrying the theme “Sustainable Economic Development: Policies and Strategies”, held on 17-18 November 2012 at Bayview Beach Resort Penang Malaysia. This conference is jointly organized by the School of Social Sciences, Universiti Sains Malaysia (USM), Malaysia, and Faculty of Business and Law, Auckland University of Technology (AUT), New Zealand. We received a total of 167 papers from various institutions and organizations around the world where 82 papers were accepted for inclusion in this proceedings. The proceedings is compiled according to the three sub themes of the conference. It covers both theoretical and empirical works from the scholars globally. It is hoped that the collection of these conference papers will become a valuable reference to the conference participants, researchers, scholars, students, businesses and policy makers. The proceedings will be submitted to Thomson ISI for indexing

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

Electricity Consumption, Export and Production: Evidence from Malaysian Manufacturing Sector

Study on the impact of energy on economic development becomes the new interest in the economy since the industrial revolution where greater amount of energy have been used in industrial production with high scale. Numeral studies have been done at the micro and macro levels to discover the role of energy and its impact on economic growth. However, little have been done to explore the essence of energy in a particular sector especially the energy based sector like manufacturing sector. This paper investigates the relationship between electricity consumption, export and production in Malaysia’s manufacturing sector in a multivariate framework. This study has two objectives. The first objective is to discover the existence of long-run relationship among the variables and the second objective is to examine the short-run causality among the variables. This is a time series analysis with the sample period covers from 1980-2010. Johansen and Juselius cointegration test is employed to discover the long-run relationship while Vector Error Correction Model (VECM) Granger causality test will be used to find out the causal relationship. We found that GDP of manufacturing sector, electricity consumption of the manufacturing sector, export of manufacturing sector, labor of the manufacturing sector and capital of the manufacturing sector are cointegrated in the long run. The VECM results show unidirectional causality running from electricity consumption of manufacturing sector to GDP of the manufacturing sector and from electricity consumption of manufacturing sector to labor of the manufacturing sector. Hence, these results indicate electricity is essential in the manufacturing sector. Keywords: Electricity consumption, output, Granger causality, cointegratio

Bowdoin Orient v.133, no.1-24 (2003-2004)

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