Fitting conditional and simultaneous autoregressive spatial models in hglm

We present a new version (> 2.0) of the hglm package for fitting hierarchical generalized linear models (HGLMs) with spatially correlated random effects. CAR() and SAR() families for conditional and simultaneous autoregressive random effects were implemented. Eigen decomposition of the matrix describing the spatial structure (e.g., the neighborhood matrix) was used to transform the CAR/SAR random effects into an independent, but eteroscedastic, Gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR and SAR models. This gives a computationally efficient algorithm for moderately sized problems.

Nonparametric specification tests for conditional duration models

This paper deals with the testing of autoregressive conditional duration (ACD) models by gauging the distance between the parametric density and hazard rate functions implied by the duration process and their non-parametric estimates. We derive the asymptotic justification using the functional delta method for fixed and gamma kernels, and then investigate the finite-sample properties through Monte Carlo simulations. Although our tests display some size distortion, bootstrapping suffices to correct the size without compromising their excellent power. We show the practical usefulness of such testing procedures for the estimation of intraday volatility patterns.

Do higher moments really matter in portfolio choice?

We present explicit formulas for evaluating the difference between Markowitz weights and those from optimal portfolios, with the same given return, considering either asymmetry or kurtosis. We prove that, whenever the higher moment constraint is not binding, the weights are never the same. If, due to special features of the first and second moments, the difference might be negligible, in quite many cases it will be very significant. An appealing illustration, when the designer wants to incorporate an asset with quite heavy tails, but wants to moderate this effect, further supports the argument.

A few more moments to finance theory: introducing higher moments in investment theory and econometrics

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DCE: the dynamic conditional execution in a multipath control independent architecture

This thesis presents DCE, or Dynamic Conditional Execution, as an alternative to reduce the cost of mispredicted branches. The basic idea is to fetch all paths produced by a branch that obey certain restrictions regarding complexity and size. As a result, a smaller number of predictions is performed, and therefore, a lesser number of branches are mispredicted. DCE fetches through selected branches avoiding disruptions in the fetch flow when these branches are fetched. Both paths of selected branches are executed but only the correct path commits. In this thesis we propose an architecture to execute multiple paths of selected branches. Branches are selected based on the size and other conditions. Simple and complex branches can be dynamically predicated without requiring a special instruction set nor special compiler optimizations. Furthermore, a technique to reduce part of the overhead generated by the execution of multiple paths is proposed. The performance achieved reaches levels of up to 12% when comparing a Local predictor used in DCE against a Global predictor used in the reference machine. When both machines use a Local predictor, the speedup is increased by an average of 3-3.5%.

Introducing higher moments in the CAPM: some basic ideas

We show how to include in the CAPM moments of any order, extending the mean-variance or mean-variance-skewness versions available until now. Then, we present a simple way to modify the formulae, in order to avoid the appearance of utility parameters. The results can be easily applied to practical portfolio design, with econometric inference and testing based on generalised method of moments procedures. An empirical application to the Brazilian stock market is discussed.

A CAPM with higher moments: theory and econometrics

We develop portfolio choice theory taking into consideration the first p~ moments of the underIying assets distribution. A rigorous characterization of the opportunity set and of the efficient portfolios frontier is given, as well as of the solutions to the problem with a general utility function and short sales allowed. The extension of c1assical meanvariance properties, like two-fund separation, is also investigated. A general CAPM is derived, based on the theoretical foundations built, and its empirical consequences and testing are discussed

On certain geometric aspects of portfolio optimisation with higher moments

We discuss geometric properties related to the minimisation of a portfolio kurtosis given its first two odd moments, considering a risk-less asset and allowing for short sales. The findings are generalised for the minimisation of any given even portfolio moment with fixed excess return and skewness, and then for the case in which only excess return is constrained. An example with two risky assets provides a better insight on the problems related to the solutions. The importance of the geometric properties and their use in the higher moments portfolio choice context is highlighted.

Finding a maximum skewness portfolio - A general solution to three-moments portfolio choice

Considering the three first moments and allowing short sales, the efficient portfolios set for n risky assets and a riskless one is found, supposing that agents like odd moments and dislike even ones. Analytical formulas for the solution surface are obtained and important geometric properties provide insights on its shape in the three dimensional space defined by the moments. A special duality result is needed and proved. The methodology is general, comprising situations in which, for instance, the investor trades a negative skewness for a higher expected return. Computation of the optimum portfolio weights is feasible in most cases.

Reusing values in a dynamic conditional execution architecture

A Execução Condicional Dinâmica (DCE) é uma alternativa para redução dos custos relacionados a desvios previstos incorretamente. A idéia básica é buscar todos os fluxos produzidos por um desvio que obedecem algumas restrições relativas à complexidade e tamanho. Como conseqüência, um número menor de previsões é executado, e assim, um número mais baixo de desvios é incorretamente previsto. Contudo, tal como outras soluções multi-fluxo, o DCE requer uma estrutura de controle mais complexa. Na arquitetura DCE, é observado que várias réplicas da mesma instrução são despachadas para as unidades funcionais, bloqueando recursos que poderiam ser utilizados por outras instruções. Essas réplicas são geradas após o ponto de convergência dos diversos fluxos em execução e são necessárias para garantir a semântica correta entre instruções dependentes de dados. Além disso, o DCE continua produzindo réplicas até que o desvio que gerou os fluxos seja resolvido. Assim, uma seção completa do código pode ser replicado, reduzindo o desempenho. Uma alternativa natural para esse problema é reusar essas seções (ou traços) que são replicadas. O objetivo desse trabalho é analisar e avaliar a efetividade do reuso de valores na arquitetura DCE. Como será apresentado, o princípio do reuso, em diferentes granularidades, pode reduzir efetivamente o problema das réplicas e levar a aumentos de desempenho.

Analysis of contagion from the constant conditional correlation model with Markov regime switching

Over the last decades, the analysis of the transmissions of international nancial events has become the subject of many academic studies focused on multivariate volatility models volatility. The goal of this study is to evaluate the nancial contagion between stock market returns. The econometric approach employed was originally presented by Pelletier (2006), named Regime Switching Dynamic Correlation (RSDC). This methodology involves the combination of Constant Conditional Correlation Model (CCC) proposed by Bollerslev (1990) with Markov Regime Switching Model suggested by Hamilton and Susmel (1994). A modi cation was made in the original RSDC model, the introduction of the GJR-GARCH model formulated in Glosten, Jagannathan e Runkle (1993), on the equation of the conditional univariate variances to allow asymmetric e ects in volatility be captured. The database was built with the series of daily closing stock market indices in the United States (SP500), United Kingdom (FTSE100), Brazil (IBOVESPA) and South Korea (KOSPI) for the period from 02/01/2003 to 09/20/2012. Throughout the work the methodology was compared with others most widespread in the literature, and the model RSDC with two regimes was de ned as the most appropriate for the selected sample. The set of results provide evidence for the existence of nancial contagion between markets of the four countries considering the de nition of nancial contagion from the World Bank called very restrictive. Such a conclusion should be evaluated carefully considering the wide diversity of de nitions of contagion in the literature.