A family of autoregressive conditional duration models

This paper develops a family of autoregressive conditional duration (ACD) models that encompasses most specifications in the literature. The nesting relies on a Box-Cox transformation with shape parameter λ to the conditional duration process and a possibly asymmetric shocks impact curve. We establish conditions for the existence of higher-order moments, strict stationarity, geometric ergodicity and β-mixing property with exponential decay. We next derive moment recursion relations and the autocovariance function of the power λ of the duration process. Finally, we assess the practical usefulness of our family of ACD models using NYSE transactions data, with special attention to IBM price durations. The results warrant the extra flexibility provided either by the Box-Cox transformation or by the asymmetric response to shocks.

A family of autoregressive conditional duration models

This paper develops a family of autoregressive conditional duration (ACD) models that encompasses most specifications in the literature. The nesting relies on a Box-Cox transformation with shape parameter λ to the conditional duration process and a possibly asymmetric shocks impact curve. We establish conditions for the existence of higher-order moments, strict stationarity, geometric ergodicity and β-mixing property with exponential decay. We next derive moment recursion relations and the autocovariance function of the power λ of the duration process. Finally, we assess the practical usefulness of our family of ACD models using NYSE price duration data on the IBM stock. The results warrant the extra flexibility provided either by the Box-Cox transformation or by the asymmetric response to shocks.

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.

Non- parametric specification tests for conditional duration models

This paper deals with the estimation and testing of conditional duration models by looking at the density and baseline hazard rate functions. More precisely, we foeus on the distance between the parametric density (or hazard rate) function implied by the duration process and its non-parametric estimate. Asymptotic justification is derived using the functional delta method for fixed and gamma kernels, whereas finite sample properties are investigated through Monte Carlo simulations. Finally, we show the practical usefulness of such testing procedures by carrying out an empirical assessment of whether autoregressive conditional duration models are appropriate to oIs for modelling price durations of stocks traded at the New York Stock Exchange.

Prevendo a volatilidade realizada de ações brasileiras : evidências empíricas

Este estudo compara previsões de volatilidade de sete ações negociadas na Bovespa usando 02 diferentes modelos de volatilidade realizada e 03 de volatilidade condicional. A intenção é encontrar evidências empíricas quanto à diferença de resultados que são alcançados quando se usa modelos de volatilidade realizada e de volatilidade condicional para prever a volatilidade de ações no Brasil. O período analisado vai de 01 de Novembro de 2007 a 30 de Março de 2011. A amostra inclui dados intradiários de 5 minutos. Os estimadores de volatilidade realizada que serão considerados neste estudo são o Bi-Power Variation (BPVar), desenvolvido por Barndorff-Nielsen e Shephard (2004b), e o Realized Outlyingness Weighted Variation (ROWVar), proposto por Boudt, Croux e Laurent (2008a). Ambos são estimadores não paramétricos, e são robustos a jumps. As previsões de volatilidade realizada foram feitas através de modelos autoregressivos estimados para cada ação sobre as séries de volatilidade estimadas. Os modelos de variância condicional considerados aqui serão o GARCH(1,1), o GJR (1,1), que tem assimetrias em sua construção, e o FIGARCH-CHUNG (1,d,1), que tem memória longa. A amostra foi divida em duas; uma para o período de estimação de 01 de Novembro de 2007 a 30 de Dezembro de 2010 (779 dias de negociação) e uma para o período de validação de 03 de Janeiro de 2011 a 31 de Março de 2011 (61 dias de negociação). As previsões fora da amostra foram feitas para 1 dia a frente, e os modelos foram reestimados a cada passo, incluindo uma variável a mais na amostra depois de cada previsão. As previsões serão comparadas através do teste Diebold-Mariano e através de regressões da variância ex-post contra uma constante e a previsão. Além disto, o estudo também apresentará algumas estatísticas descritivas sobre as séries de volatilidade estimadas e sobre os erros de previsão.

The importance of common cyclical features in VAR analysis: a Monte-Carlo study

Despite the commonly held belief that aggregate data display short-run comovement, there has been little discussion about the econometric consequences of this feature of the data. We use exhaustive Monte-Carlo simulations to investigate the importance of restrictions implied by common-cyclical features for estimates and forecasts based on vector autoregressive models. First, we show that the ìbestî empirical model developed without common cycle restrictions need not nest the ìbestî model developed with those restrictions. This is due to possible differences in the lag-lengths chosen by model selection criteria for the two alternative models. Second, we show that the costs of ignoring common cyclical features in vector autoregressive modelling can be high, both in terms of forecast accuracy and efficient estimation of variance decomposition coefficients. Third, we find that the Hannan-Quinn criterion performs best among model selection criteria in simultaneously selecting the lag-length and rank of vector autoregressions.

O canal de crédito na transmissão de política monetária: evidências para o Brasil

Este trabalho traz avaliação empírica a respeito do canal de crédito no Brasil, feita com base no artigo de Holtemöller (2002). Para tanto, foi feita análise descritiva sobre a evolução do crédito no país, bem como testes econométricos utilizando dados monetários, de crédito e economia real. Observamos o aumento da importância do crédito nos últimos anos, assim como o aumento do endividamento corporativo via emissão de títulos. Portanto, seria natural esperar que o canal de crédito no mecanismo de transmissão da política monetária também se tornasse mais importante. Contudo, a análise empírica mostra que seus efeitos sobre a atividade econômica são limitados. Após estimações feitas para o canal monetário tradicional, a partir de vetores autorregressivos estruturais (SVAR) com vetores de correção de erros (VEC), incluímos variáveis de crédito para avaliar o impacto sobre o produto. Apesar de concluirmos que choques de política monetária possuem efeitos sobre a oferta de crédito, o impacto de condições creditícias restritivas sobre a produção industrial é pequeno. Alguns fatores como a existência de crédito corporativo direcionado via BNDES, maior importância da captação via mercado de capitais, medidas macroprudenciais adotadas e aumento do prazo médio concorrem para esse resultado.

Fiscal policy in Brazil: from counter-cyclical response to crisis

The main goal of this article is to identify the dynamic effects of fiscal policy on output in Brazil from 1997 to 2014, and, more specifically, to estimate those effects when the output falls below its potential level. To do so, we estimate VAR (vector autoregressive) models to generate impulse-response functions and causality/endogeneity tests. Our most remarkable results indicate the following channel of economic policy in Brazil: to foster output, government spending increases causing increases in both tax rates and revenue and the short-term interest rate. A fiscal stimulus via spending seems efficient for economic performance as well as monetary policy; however, the latter operates pro-cyclically in the way we defined here, while the former is predominantly countercyclical. As the monetary shock had a negative effect on GDP growth and GDP growth responded positively to the fiscal shock, it seems that the economic policy has given poise to growth with one hand and taken it with the other one. The monetary policy is only reacting to the fiscal stimuli. We were not able to find any statistically significant response of the output to tax changes, but vice versa seems work in the Brazilian case.

A (semi-)parametric functional coefficient autoregressive conditional duration model

In this paper, we propose a class of ACD-type models that accommodates overdispersion, intermittent dynamics, multiple regimes, and sign and size asymmetries in financial durations. In particular, our functional coefficient autoregressive conditional duration (FC-ACD) model relies on a smooth-transition autoregressive specification. The motivation lies on the fact that the latter yields a universal approximation if one lets the number of regimes grows without bound. After establishing that the sufficient conditions for strict stationarity do not exclude explosive regimes, we address model identifiability as well as the existence, consistency, and asymptotic normality of the quasi-maximum likelihood (QML) estimator for the FC-ACD model with a fixed number of regimes. In addition, we also discuss how to consistently estimate using a sieve approach a semiparametric variant of the FC-ACD model that takes the number of regimes to infinity. An empirical illustration indicates that our functional coefficient model is flexible enough to model IBM price durations.

Tests of conditional asset pricing models in the brazilian stock market

In this paper, we test a version of the conditional CAPM with respect to a local market portfolio, proxied by the Brazilian stock index during the period 1976-1992. We also test a conditional APT modeI by using the difference between the 3-day rate (Cdb) and the overnight rate as a second factor in addition to the market portfolio in order to capture the large inflation risk present during this period. The conditional CAPM and APT models are estimated by the Generalized Method of Moments (GMM) and tested on a set of size portfolios created from individual securities exchanged on the Brazilian markets. The inclusion of this second factor proves to be important for the appropriate pricing of the portfolios.

A conditional likelihood ratio test for structural models

This paper develops a general method for constructing similar tests based on the conditional distribution of nonpivotal statistics in a simultaneous equations model with normal errors and known reducedform covariance matrix. The test based on the likelihood ratio statistic is particularly simple and has good power properties. When identification is strong, the power curve of this conditional likelihood ratio test is essentially equal to the power envelope for similar tests. Monte Carlo simulations also suggest that this test dominates the Anderson- Rubin test and the score test. Dropping the restrictive assumption of disturbances normally distributed with known covariance matrix, approximate conditional tests are found that behave well in small samples even when identification is weak.

Forecasting accuracy and estimation uncertainty using VAR models with short- and long-term economic restrictions: a Monte-Carlo study

Using vector autoregressive (VAR) models and Monte-Carlo simulation methods we investigate the potential gains for forecasting accuracy and estimation uncertainty of two commonly used restrictions arising from economic relationships. The Örst reduces parameter space by imposing long-term restrictions on the behavior of economic variables as discussed by the literature on cointegration, and the second reduces parameter space by imposing short-term restrictions as discussed by the literature on serial-correlation common features (SCCF). Our simulations cover three important issues on model building, estimation, and forecasting. First, we examine the performance of standard and modiÖed information criteria in choosing lag length for cointegrated VARs with SCCF restrictions. Second, we provide a comparison of forecasting accuracy of Ötted VARs when only cointegration restrictions are imposed and when cointegration and SCCF restrictions are jointly imposed. Third, we propose a new estimation algorithm where short- and long-term restrictions interact to estimate the cointegrating and the cofeature spaces respectively. We have three basic results. First, ignoring SCCF restrictions has a high cost in terms of model selection, because standard information criteria chooses too frequently inconsistent models, with too small a lag length. Criteria selecting lag and rank simultaneously have a superior performance in this case. Second, this translates into a superior forecasting performance of the restricted VECM over the VECM, with important improvements in forecasting accuracy ñreaching more than 100% in extreme cases. Third, the new algorithm proposed here fares very well in terms of parameter estimation, even when we consider the estimation of long-term parameters, opening up the discussion of joint estimation of short- and long-term parameters in VAR models.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties for a lack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties. In order to compute the fit of each model, we propose an iterative procedure to compute the maximum likelihood estimates of parameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank, relative to the commonly used procedure of selecting the lag-length only and then testing for cointegration.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian inflation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in different measures of forecasting accuracy are substantial, especially for short horizons.

Estimating and forecasting the volatility of Brazilian finance series using arch models (Preliminary Version)

The goal of this paper is to present a comprehensive emprical analysis of the return and conditional variance of four Brazilian …nancial series using models of the ARCH class. Selected models are then compared regarding forecasting accuracy and goodness-of-…t statistics. To help understanding the empirical results, a self-contained theoretical discussion of ARCH models is also presented in such a way that it is useful for the applied researcher. Empirical results show that although all series share ARCH and are leptokurtic relative to the Normal, the return on the US$ has clearly regime switching and no asymmetry for the variance, the return on COCOA has no asymmetry, while the returns on the CBOND and TELEBRAS have clear signs of asymmetry favoring the leverage e¤ect. Regarding forecasting, the best model overall was the EGARCH(1; 1) in its Gaussian version. Regarding goodness-of-…t statistics, the SWARCH model did well, followed closely by the Student-t GARCH(1; 1)