nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒08‒14
two papers chosen by
Yong Yin
SUNY at Buffalo

  1. Semiparametric inference on the fractal index of Gaussian and conditionally Gaussian time series data By Mikkel Bennedsen
  2. The cointegrated vector autoregressive model with general deterministic terms By Søren Johansen; Morten Ørregaard Nielsen

  1. By: Mikkel Bennedsen
    Abstract: Using theory on (conditionally) Gaussian processes with stationary increments developed in Barndorff-Nielsen et al. (2009, 2011), this paper presents a general semiparametric approach to conducting inference on the fractal index, $\alpha$, of a time series. Our setup encompasses a large class of Gaussian processes and we show how to extend it to a large class of non-Gaussian models as well. It is proved that the asymptotic distribution of the estimator of $\alpha$ does not depend on the specifics of the data generating process for the observations, but only on the value of $\alpha$ and a "heteroskedasticity" factor. Using this, we propose a simulation-based approach to inference, which is easily implemented and is valid more generally than asymptotic analysis. We detail how the methods can be applied to test whether a stochastic process is a non-semimartingale. Finally, the methods are illustrated in two empirical applications motivated from finance. We study time series of log-prices and log-volatility from $29$ individual US stocks; no evidence of non-semimartingality in asset prices is found, but we do find evidence of non-semimartingality in volatility. This confirms a recently proposed conjecture that stochastic volatility processes of financial assets are rough (Gatheral et al., 2014).
    Date: 2016–08
  2. By: Søren Johansen (University of Copenhagen and CREATES); Morten Ørregaard Nielsen (Queen?s University and CREATES)
    Abstract: In the cointegrated vector autoregression (CVAR) literature, deterministic terms have until now been analyzed on a case-by-case, or as-needed basis. We give a comprehensive unified treatment of deterministic terms in the additive model X(t)= Z(t) + Y(t), where Z(t) belongs to a large class of deterministic regressors and Y(t) is a zero-mean CVAR. We suggest an extended model that can be estimated by reduced rank regression and give a condition for when the additive and extended models are asymptotically equivalent, as well as an algorithm for deriving the additive model parameters from the extended model parameters. We derive asymptotic properties of the maximum likelihood estimators and discuss tests for rank and tests on the deterministic terms. In particular, we give conditions under which the estimators are asymptotically (mixed) Gaussian, such that associated tests are khi squared distributed.
    Keywords: Additive formulation, cointegration, deterministic terms, extended model, likelihood inference, VAR model
    JEL: C32
    Date: 2016–07–24

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