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.

The role of no-arbitrage on forecasting: lessons from a parametric term structure model

Parametric term structure models have been successfully applied to innumerous problems in fixed income markets, including pricing, hedging, managing risk, as well as studying monetary policy implications. On their turn, dynamic term structure models, equipped with stronger economic structure, have been mainly adopted to price derivatives and explain empirical stylized facts. In this paper, we combine flavors of those two classes of models to test if no-arbitrage affects forecasting. We construct cross section (allowing arbitrages) and arbitrage-free versions of a parametric polynomial model to analyze how well they predict out-of-sample interest rates. Based on U.S. Treasury yield data, we find that no-arbitrage restrictions significantly improve forecasts. Arbitrage-free versions achieve overall smaller biases and Root Mean Square Errors for most maturities and forecasting horizons. Furthermore, a decomposition of forecasts into forward-rates and holding return premia indicates that the superior performance of no-arbitrage versions is due to a better identification of bond risk premium.

Custos de ajustamento e a dinâmica da estrutura de capital em empresas brasileiras

Diversos estudos de Finanças Corporativas consideram os custos associados aos ajustes da estrutura de capital das empresas irrelevantes tanto na forma quanto em magnitude. Este estudo analisou empiricamente a influência dos custos de ajustamento na dinâmica dos ajustes da estrutura de capital de empresas brasileiras de capital aberto no período de 1999 a 2007. A alavancagem foi abordada sob três diferentes cenários, considerando a presença de custos fixos, custos proporcionais e por uma composição de custos fixos e proporcionais através de simulações utilizando um modelo reduzido da estrutura de capital. Em seguida a análise não paramétrica da amostra revelou que as empresas apresentam um comportamento dinâmico em suas decisões de financiamento para o ajuste da estruturas de capital, mas que não se revelou contínuo. A utilização de um modelo de duration mostrou-se adequado para mensurar o intervalo de tempo entre os ajustes da estrutura de capital das empresas. Os resultados são extremamente relevantes e suportam a teoria de um comportamento de rebalanceamento dinâmico pelas empresas de suas estruturas de capital em torno de um intervalo ótimo. Entretanto os ajustes não ocorrem de forma imediata e a persistência de choques à estrutura de capital deve-se em sua maior parte aos custos associados aos ajustes do que a uma possível indiferença à estrutura de capital. . Este trabalho constitui-se como pioneiro no mercado brasileiro acerca dos custos de ajustamento da estrutura de capital e abre espaço para a discussão do comportamento ótimo em torno da estrutura de capital de empresas nacionais.

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.

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.