2012
DOI: 10.2139/ssrn.2315830
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Local Adaptive Multiplicative Error Models for High- Frequency Forecasts

Abstract: We propose a local adaptive multiplicative error model (MEM) accommodating time- JEL classification: C41, C51, C53, G12, G17

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Cited by 11 publications
(12 citation statements)
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“…The latter specification allows for changing parameters but requires to impose a priori structures on the form of the transition and on the number of underlying regimes. Alternatively, Haerdle et al (2012) propose to adaptively estimate the MEM based on a window of varying length and thus providing updated parameter estimates at each point in time.…”
Section: The Baseline Memmentioning
confidence: 99%
“…The latter specification allows for changing parameters but requires to impose a priori structures on the form of the transition and on the number of underlying regimes. Alternatively, Haerdle et al (2012) propose to adaptively estimate the MEM based on a window of varying length and thus providing updated parameter estimates at each point in time.…”
Section: The Baseline Memmentioning
confidence: 99%
“…The local parametric approach crucially rests on the sequencing test of local time-homogeneity to search for an interval of homogeneity among the considered intervals ( = 0, 1, … , ) at a fixed time point 0 . Here, we follow Härdle et al (2015) and Čížek et al (2009) and adopt the local change point detection test, in which the null hypothesis on parameter homogeneity for the intervals up to is tested against the alternative hypothesis that a change point at unknown location within interval exists. Assuming that the homogeneity assumption of interval −1 has not been rejected, the test statistic for testing possible change points in interval is defined via the corresponding fitted log-likelihood ( ; , ) by:…”
Section: Local Change Point Detection Testmentioning
confidence: 99%
“…Since the true distribution of the test statistic is unknown, the critical values have to be determined by simulation using the general approach of testing theory: to provide a prescribed performance of the procedure under the null hypothesis (Čížek et al, 2009;Chen and Niu, 2014;Härdle et al, 2015).…”
Section: Calculation Of Critical Valuesmentioning
confidence: 99%
“…Huberman and Halka (2001) evidenced the serial dependence of bid-ask spread and depth in the AutoRegressive model. Härdle, Hautsch and Mihoci (2015) proposed a local adaptive multiplicative error model to forecast the high-frequency series of one-minute cumulative trading volumes of several NASDAQ blue chip stocks. Serial dependence also exists in limit order demand and supply, see Dierker, Kim, Lee and Morck (2014).…”
Section: Vfarrandbidaskcurveplotmentioning
confidence: 99%