Pedro L. Valls Pereira
@fgv.br
Professor of Financial Econometrics
Sao Paulo School of Economics - FGV
Professor of Financial Econometrics at Sao Paulo School of Economics - FGV since 2008.
Professor of Finance at INSPER from 2000 to 2008
Associate Professor at Statistics Departament, Universidade de São Paulo 1990 to 2000
EDUCATION
Habilitation (Livre Docente) Universidade de São Paulo (1990)
PhD, Economics (Statistics) London School of Economics (1983)
MSc Statistics - IMPA (1978)
BSc Applied Mathematics - PUC-Rio (1974)
RESEARCH INTERESTS
Financial Econometrics; High Dimensional Model; Forecasting; Factor Models.
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Diogo de Prince, Emerson Fernandes Marçal, and Pedro L. Valls Pereira
MDPI AG
In this paper, we address whether using a disaggregated series or combining an aggregated and disaggregated series improves the forecasting of the aggregated series compared to using the aggregated series alone. We used econometric techniques, such as the weighted lag adaptive least absolute shrinkage and selection operator, and Exponential Triple Smoothing (ETS), as well as the Autometrics algorithm to forecast industrial production in Brazil one to twelve months ahead. This is the novelty of the work, as is the use of the average multi-horizon Superior Predictive Ability (aSPA) and uniform multi-horizon Superior Predictive Ability (uSPA) tests, used to select the best forecasting model by combining different horizons. Our sample covers the period from January 2002 to February 2020. The disaggregated ETS has a better forecast performance when forecasting horizons that are more than one month ahead using the mean square error, and the aggregated ETS has better forecasting ability for horizons equal to 1 and 2. The aggregated ETS forecast does not contain information that is useful for forecasting industrial production in Brazil beyond the information already found in the disaggregated ETS forecast between two and twelve months ahead.
Carlos Trucíos, João H. G. Mazzeu, Marc Hallin, Luiz K. Hotta, Pedro L. Valls Pereira, and Mauricio Zevallos
Informa UK Limited
Abstract Based on a General Dynamic Factor Model with infinite-dimensional factor space and MGARCH volatility models, we develop new estimation and forecasting procedures for conditional covariance matrices in high-dimensional time series. The finite-sample performance of our approach is evaluated via Monte Carlo experiments and outperforms the most alternative methods. This new approach is also used to construct minimum one-step-ahead variance portfolios for a high-dimensional panel of assets. The results are shown to match the results of recent proposals by Engle, Ledoit, and Wolf and achieve better out-of-sample portfolio performance than alternative procedures proposed in the literature.
Carlos Trucíos, João H.G. Mazzeu, Luiz K. Hotta, Pedro L. Valls Pereira, and Marc Hallin
Elsevier BV
Carlos Trucíos, Luiz K. Hotta, and Pedro L. Valls Pereira
Elsevier BV
Paula V. Tófoli, Flávio A. Ziegelmann, Osvaldo Candido, and Pedro L. Valls Pereira
Walter de Gruyter GmbH
Abstract Vine copulas are multivariate dependence models constructed from pair-copulas (bivariate copulas). In this paper, we allow the dependence parameters of the pair-copulas in a D-vine decomposition to be potentially time-varying, following a restricted ARMA(1, m) process, in order to obtain a very flexible dependence model for applications to multivariate financial return data. We investigate the dependence among the broad stock market indexes from Germany (DAX), France (CAC 40), Britain (FTSE 100), the United States (S&P 500) and Brazil (IBOVESPA) both in a crisis and in a non-crisis period. We find evidence of stronger dependence among the indexes in bear markets. Surprisingly, though, the dynamic D-vine copula indicates the occurrence of a sharp decrease in dependence between the indexes FTSE and CAC in the beginning of 2011, and also between CAC and DAX during mid-2011 and in the beginning of 2008, suggesting the absence of contagion in these cases. We evaluate the dynamic D-vine copula with respect to Value-at-Risk (VaR) forecasting accuracy in crisis periods. The dynamic D-vine outperforms the static D-vine in terms of predictive accuracy for our real data sets. We also investigate the dynamic D-vine copula in a simulation study and the overall results of the Monte Carlo experiments are quite favorable to the dynamic D-vine copula in comparison with a static D-vine copula.
Maximilian-Benedikt Herwarth Kohn and Pedro L. Valls Pereira
Informa UK Limited
Reviewing the definition and measurement of speculative bubbles in context of contagion, this paper analyses the DotCom bubble in American and European equity markets using the dynamic conditional correlation (DCC) model proposed as on one hand as an econometrics explanation and on the other hand the behavioral finance as an psychological explanation. Contagion is defined in this context as the statistical break in the computed DCCs as measured by the shifts in their means and medians. Even it is astonishing, that the contagion is lower during price bubbles, the main finding indicates the presence of contagion in the different indices among those two continents and prove the presence of structural changes during financial crisis.
Pedro Nielsen Rotta and Pedro L. Valls Pereira
Informa UK Limited
ABSTRACT Over the last decades, the transmissions of international financial events have been the subject of many academic studies focused on multivariate volatility models. This study evaluates the financial contagion between stock market returns. The econometric model employed, regime switching dynamic correlation (RSDC). A modification was made in the original RSDC model, the introduction of the GJR-GARCH-N and also GJR-GARCH-t models, on the equation of conditional univariate variances, thus allowing us to capture the asymmetric effects in volatility and also heavy tails. A database was built using series of indices in the United States (S&P500), the United Kingdom (FTSE100), Brazil (IBOVESPA) and South Korea (KOSPI) from 1 February 2003 to 20 September 2012. Throughout this study the methodology is compared with those frequently found in literature, and the model RSDC with two regimes was defined as the most appropriate for the selected sample with t-Student distribution in the disturbances. The adapted RSDC model used in this article can be used to detect contagion – considering the definition of financial contagion from the World Bank called very restrictive – with the help of the empirical exercise.
Rodrigo Chicaroli and Pedro L. Valls Pereira
Wiley
Bruno P. Arruda and Pedro L. Valls Pereira
Informa UK Limited
In this article, we test the hypothesis of contagion amongst sectors within the United States’ economy during the subprime crisis. The econometric methodology applied here is based on the dynamic conditional correlation model proposed by Engle (2002). Further, we applied several Lagrange multiplier (LM)-robust tests to test whether there were structural breaks in series’ dependency structures during the period of interest. Events theoretically classified as relevant to the crisis upshots as well as the interactions between the moments of the series were used as indicator functions to the referred structural breaks. The main conclusion of this study is that one can indeed observe contagion within almost all pairs of sectors’ indices. Thus, we conclude that the dependency structure of the sectors of interest has faced structural changes during the years of 2007 and 2008. Hence, diversification strategies as well as the risk analysis inherent to the portfolios’ management may have been drastically affected.
Emerson Fernandes Marcal, Pedro L. Valls Pereira, Diogenes Manoel Leiva Martin, Wilson Toshiro Nakamura, and Wagner Oliveira Monteiro
Wiley
Emerson Fernandes Marçal, Pedro L. Valls Pereira, Diógenes Manoel Leiva Martin, and Wilson Toshiro Nakamura
Informa UK Limited
This article investigates the existence of contagion between countries on the basis of an analysis of returns for stock indices over the period 1994 to 2003. The econometrics methodology used is that of multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) family volatility models, particularly the Dynamic Conditional Correlation (DCC) models in the form proposed by Engle and Sheppard (2001). The returns were duly corrected for a series of country-specific fundamentals. The relevance of this procedure is highlighted in the literature by the work of Pesaran and Pick (2003). The results obtained in this article provide evidence favourable for the hypothesis of regional contagion in both Latin America and Asia. As a rule, contagion spread from the Asian crisis to Latin America, but not in the opposite direction.
Márcio Poletti Laurini and Pedro L. Valls Pereira
Elsevier BV
Soosung Hwang, Steve E. Satchell, and Pedro L. Valls Pereira
Wiley
We propose generalised stochastic volatility models with Markov regime changing state equations (SVMRS) to investigate the important properties of volatility in stock returns, specifically high persistence and smoothness. The model suggests that volatility is far less persistent and smooth than the conventional GARCH or stochastic volatility. Persistent short regimes are more likely to occur when volatility is low, while far less persistence is likely to be observed in high volatility regimes. Comparison with different classes of volatility supports the SVMRS as an appropriate proxy volatility measure. Our results indicate that volatility could be far more difficult to estimate and forecast than is generally believed.
Soosung Hwang and Pedro L. Valls Pereira
Informa UK Limited
Abstract It is shown that the ML estimates of the popular GARCH(1,1) model are significantly negatively biased in small samples and that in many cases converged estimates are not possible with Bollerslev’s non-negativity conditions. Results also indicate that a high level of persistence in GARCH(1,1) models obtained using a large number of observations has autocorrelations lower than these ML estimates suggest in small samples. Considering the size of biases and convergence errors, it is proposed that at least 250 observations are needed for ARCH(1) models and 500 observations for GARCH(1,1) models. A simple measure of how much GARCH conditional volatility explains squared returns is proposed. The measure indicates that for a typical index return volatility whose ARCH parameter is very small, the conditional volatility hardly explains squared returns.
Márcio Laurini, Eduardo Andrade, and Pedro L. Valls Pereira
Informa UK Limited
This article analyses the evolution of relative per capita income distribution of Brazilian municipalities over the period 1970–1996. Analyses are based on non-parametric methodologies and do not assume probability distributions or functional forms for the data. Two convergence tests have been carried out – a test for sigma convergence based on the bootstrap principle and a beta convergence test using smoothing splines for the growth regressions. The results obtained demonstrate the need to model the dynamics of income for Brazilian municipalities as a process of convergence clubs, using the methodology of transition matrices and stochastic kernels. The results show the formation of two convergence clubs, a low income club formed by the municipalities of the North and Northeast regions, and another high income club formed by the municipalities of the Center-West, Southeast and South regions. The formation of convergence clubs is confirmed by a bootstrap test for multimodality.
Eduardo Andrade, Márcio Laurini, Regina Madalozzo, and Pedro L. Valls Pereira
Elsevier BV
Luiz K. Hotta, Pedro L. Valls Pereira, and Rissa Ota
Springer Science and Business Media LLC
Pedro L. Valls Pereira
Elsevier BV