Researcher:
Meitz, Mika

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Mika

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Meitz

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Meitz, Mika

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2010 - 20134

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Researcher Of Publications: search.filters.isAuthorOfPublication.cbd337f5-4076-47e8-ae7a-89181f31ec0c

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    Testing for linear and nonlinear predictability of stock returns
    (Oxford University Press (OUP), 2013) Lanne, Markku; Saikkonen, Pentti; Department of Economics; Meitz, Mika; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; N/A
    We develop tests for predictability in a first-order ARMA model oftensuggested for stock returns. Instead of the conventional ARMA model,we consider its non-Gaussian and noninvertible counterpart that has identical autocorrelation properties but allows for conditionalheteroskedasticity prevalent in stock returns. In addition to autocorrelation,the tests can also be used to test for nonlinear predictability, incontrast to previously proposed predictability tests based on invertible ARMA models. Simulation results attest to improved power. We apply our tests to postwar U.S. stock returns. All return series considered are found serially uncorrelated but dependent and, hence, nonlinearly predictable.
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    A note on the geometric ergodicity of a nonlinear AR-ARCH model
    (Elsevier Science Bv, 2010) Saikkonen, Pentti; Department of Economics; Meitz, Mika; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; N/A
    This note studies the geometric ergodicity of nonlinear autoregressive models with conditionally heteroskedastic errors. A nonlinear autoregression of order p (AR(p)) with the conditional variance specified as the conventional linear autoregressive conditional heteroskedasticity model of order q (ARCH(q)) is considered. Conditions under which the Markov chain representation of this nonlinear AR-ARCH model is geometrically ergodic and has moments of known order are provided. The obtained results complement those of Liebscher [Liebscher, E., 2005. Towards a unified approach for proving geometric ergodicity and mixing properties of nonlinear autoregressive processes, journal of Time Series Analysis, 26,669-689] by showing how his approach based on the concept of the joint spectral radius of a set of matrices can be extended to establish geometric ergodicity in nonlinear autoregressions with conventional ARCH(q) errors.
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    Parameter estimation in nonlinear AR-GARCH models
    (Cambridge Univ Press, 2011) Saikkonen, Pentti; Department of Economics; Meitz, Mika; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; N/A
    This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a general nonlinear autoregression of order p (AR(p)) with the conditional variance specified as a general nonlinear first-order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. We do not require the rescaled errors to be independent, but instead only to form a stationary and ergodic martingale difference sequence. Strong consistency and asymptotic normality of the global Gaussian quasi-maximum likelihood (QML) estimator are established under conditions comparable to those recently used in the corresponding linear case. To the best of our knowledge, this paper provides the first results on consistency and asymptotic normality of the QML estimator in nonlinear autoregressive models with GARCH errors.
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    Maximum likelihood estimation of a noninvertible ARMA model with autoregressive conditional heteroskedasticity
    (Elsevier, 2013) Saikkonen, Pentti; Department of Economics; Meitz, Mika; Faculty Member; Department of Economics; College of Administrative Sciences and Economics; N/A
    We consider maximum likelihood estimation of a particular noninvertible ARMA model with autoregressive conditionally heteroskedastic (ARCH) errors. The model can be seen as an extension to the so-called all-pass models in that it allows for autocorrelation and for more flexible forms of conditional heteroskedasticity. These features may be attractive especially in economic and financial applications. Unlike in previous literature on maximum likelihood estimation of noncausal and/or noninvertible ARMA models and all-pass models, our estimation theory does allow for Gaussian innovations. We give conditions under which a strongly consistent and asymptotically normally distributed solution to the likelihood equations exists, and we also provide a consistent estimator of the limiting covariance matrix.