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AbstractWe analyze exchange rates along with equity quotes for 3 German firms from New York (NYSE) and Frankfurt (XETRA) during overlapping trading hours to see where price discovery occurs and how stock prices adjust to an exchange rate shock. Findings include a) the exchange rate is exogenous with respect to the stock prices; b) exchange rate innovations are more important in understanding the evolution of NYSE prices than XETRA prices; and c) most (but not all) of the fundamental or random walk component of firm value is determined in Frankfurt JEL classification: F3; G15
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 l 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 b-mixing property with exponential decay. We next derive moment recursion relations and the autocovariance function of the power l of the duration process. Finally, we assess the practical usefulness of our family of ACD models using New York stock exchange (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. r 2004 Elsevier B.V. All rights reserved. JEL Classification: C22; C41
This paper shows that the monotonicity of the conditional hazard in traditional ACD models is both econometrically important and empirically invalid. To counter this problem we introduce a more flexible parametric model which is easy to fit and performs well both in simulation studies and in practice. In an empirical application to NYSE price duration processes, we show that non-monotonic conditional hazard functions are indicated for all stocks. Recently proposed specification tests for financial duration models clearly reject the standard ACD models, whereas the results for the new model are quite favorable.
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 finitesample 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. r
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