Mauricio Zevallos

Assessing stock market dependence and contagion

This paper assesses evidence of the linkages and contagion among important stock markets in Latin America (Brazil, Mexico and Argentina), Europe (UK and Germany), Asia (Japan and Singapore) and the USA from 6 September 1995 to 19 April 2013. To accomplish this task, this paper combines copula modelling with time-varying parameters and pair-copula composition of multiple dependence. The bivariate analyses show an asymmetric dependence between the stock markets as well as contagion. In addition, this work proposes a method to assess the linkages and contagion between two stock markets which takes into account the effects of a third stock market. In applying this method, conditioned on the USA market, most of the evidence of contagion between the Latin American or European markets disappears, but important dependence levels still remain.

Influential observations in GARCH models

This paper examines local influence assessment in generalized autoregressive conditional heteroscesdasticity models with Gaussian and Student-t errors, where influence is examined via the likelihood displacement. The analysis of local influence is discussed under three perturbation schemes: data perturbation, innovative model perturbation and additive model perturbation. For each case, expressions for slope and curvature diagnostics are derived. Monte Carlo experiments are presented to determine the threshold values for locating influential observations. The empirical study of daily returns of the New York Stock Exchange composite index shows that local influence analysis is a useful technique for detecting influential observations; most of the observations detected as influential are associated with historical shocks in the market. Finally, based on this empirical study and the analysis of simulated data, some advice is given on how to use the discussed methodology.

Riemann manifold Langevin methods on stochastic volatility estimation

ABSTRACT In this article, we perform Bayesian estimation of stochastic volatility models with heavy tail distributions using Metropolis adjusted Langevin (MALA) and Riemman manifold Langevin (MMALA) methods. We provide analytical expressions for the application of these methods, assess the performance of these methodologies in simulated data, and illustrate their use on two financial time series datasets.

Portfolio risk decomposition through pair-copula models

ABSTRACT In this work, we applied pair-copula models to estimate the market risk of a portfolio composed by future contracts. Pair-copula models (also known as vine copulas) have received much attention mainly because of their flexibility to reproduce various patterns of correlations and tail dependence, and we assessed how different pair-copula specifications change the risk estimation and decomposition of the chosen portfolio. We conclude that even though the backtesting results are nearly the same, different pair-copula models change the risk decomposition of a portfolio.

Bayesian Estimation and Prediction of Stochastic Volatility Models via INLA

In this article, we assess Bayesian estimation and prediction using integrated Laplace approximation (INLA) on a stochastic volatility (SV) model. This was performed through a Monte Carlo study with 1,000 simulated time series. To evaluate the estimation method, two criteria were considered: the bias and square root of the mean square error (smse). The criteria used for prediction are the one step ahead forecast of volatility and the one day Value at Risk (VaR). The main findings are that the INLA approximations are fairly accurate and relatively robust to the choice of prior distribution on the persistence parameter. Additionally, VaR estimates are computed and compared for three financial time series returns indexes.