150 research outputs found

    Long memory and fractional integration in high frequency data on the US Dollar / British Pound spot exchange rate

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    This paper analyses the long-memory properties of a high-frequency financial time series dataset. It focuses on temporal aggregation and other features of the data, and how they might affect the degree of dependence of the series. Fractional integration or I(d) models are estimated with a variety of specifications for the error term. In brief, we find evidence that a lower degree of integration is associated with lower data frequencies. In particular, when the data are collected every 10 minutes there are several cases with values of d strictly smaller than 1, implying mean-reverting behaviour; however, for higher data frequencies the unit root null cannot be rejected. This holds for all four series examined, namely Open, High, Low and Last observations for the US dollar / British pound spot exchange rate and for different sample periods.This study is financially supported from the Ministry of Education of Spain (ECO2011-2014 – 28196 - ECON Y FINANZAS, Spain) and from a Jeronimo de Ayanz project of the Government of Navarra

    Modelling Stochastic Volatility In Asset Returns Using Fractionally Integrated Semiparametric Techniques

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    In this article we estimate the order of integration of the volatility process of several exchange rates and stock returns using fractionally integrated semiparametric techniques, namely a local Whittle semiparametric estimator. The results suggest that all series can be well described in terms of I(d) statistical models, with values of d higher than 0, indicating long-memory behaviour

    A Multivariate Long-Memory Model with Structural Breaks

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    This paper introduces a multivariate long-memory model with structural breaks. In the proposed framework, time series exhibit possibly fractional orders of integration which are allowed to be different in each subsample. The break date is endogenously determined using a procedure which minimises the residual sum of squares (RSS). Monte Carlo experiments show that this method for detecting breaks performs well in large samples. As an illustration, we estimate a trivariate VAR including prices, employment and GDP in both the US and Mexico. For the subsample preceding the break our findings are similar to those of earlier studies based on a standard VAR approach in both countries, in the sense that the variables exhibit integer degrees of integration. On the contrary, the series are found to be fractionally integrated after the break, with the fractional differencing parameters being higher than 1 in the case of Mexico
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