nep-ets New Economics Papers
on Econometric Time Series
Issue of 2018‒02‒19
ten papers chosen by
Yong Yin
SUNY at Buffalo

  1. Asymptotics of Cholesky GARCH models and time-varying conditional betas By Darolles, Serges; Francq, Christian; Laurent, Sébastien
  2. Functional GARCH models: the quasi-likelihood approach and its applications By Cerovecki, Clément; Francq, Christian; Hormann, Siegfried; Zakoian, Jean-Michel
  3. Time Varying Heteroskedastic Realized GARCH models for tracking measurement error bias in volatility forecasting By Gerlach, Richard; Naimoli, Antonio; Storti, Giuseppe
  4. Dynamic panel probit: finite-sample performance of alternative random-effects estimators By Riccardo Lucchetti; Claudia Pigini
  5. Bayesian analysis of realized matrix-exponential GARCH models By Manabu Asai; Michael McAleer
  6. A comparison study of realized kernels using different sampling frequencies By Chen Zhou
  7. A Robust Sequential Procedure for Estimating the Number of Structural Changes in Persistence By Mohitosh Kejriwal
  8. Structural vector autoregression with time varying transition probabilities: identifying uncertainty shocks via changes in volatility By Wenjuan Chen; Aleksei Netsunajev
  9. Parametric estimation of hidden Markov models by least squares type estimation and deconvolution By Christophe Chesneau; Salima El Kolei; Fabien Navarro
  10. Forecasting with High-Dimensional Panel VARs By Koop, G; Korobilis, D

  1. By: Darolles, Serges; Francq, Christian; Laurent, Sébastien
    Abstract: This paper proposes a new model with time-varying slope coefficients. Our model, called CHAR, is a Cholesky-GARCH model, based on the Cholesky decomposition of the conditional variance matrix introduced by Pourahmadi (1999) in the context of longitudinal data. We derive stationarity and invertibility conditions and prove consistency and asymptotic normality of the Full and equation-by-equation QML estimators of this model. We then show that this class of models is useful to estimate conditional betas and compare it to the approach proposed by Engle (2016). Finally, we use real data in a portfolio and risk management exercise. We find that the CHAR model outperforms a model with constant betas as well as the dynamic conditional beta model of Engle (2016).
    Keywords: Multivariate-GARCH; conditional betas; covariance
    JEL: C5 C58
    Date: 2018–01–18
  2. By: Cerovecki, Clément; Francq, Christian; Hormann, Siegfried; Zakoian, Jean-Michel
    Abstract: The increasing availability of high frequency data has initiated many new research areas in statistics. Functional data analysis (FDA) is one such innovative approach towards modelling time series data. In FDA, densely observed data are transformed into curves and then each (random) curve is considered as one data object. A natural, but still relatively unexplored, context for FDA methods is related to financial data, where high-frequency trading currently takes a significant proportion of trading volumes. Recently, articles on functional versions of the famous ARCH and GARCH models have appeared. Due to their technical complexity, existing estimators of the underlying functional parameters are moment based---an approach which is known to be relatively inefficient in this context. In this paper, we promote an alternative quasi-likelihood approach, for which we derive consistency and asymptotic normality results. We support the relevance of our approach by simulations and illustrate its use by forecasting realised volatility of the S$\&$P100 Index.
    Keywords: Functional time series; High-frequency volatility models; Intraday returns; Functional QMLE; Stationarity of functional GARCH
    JEL: C13 C32 C58
    Date: 2018–01–18
  3. By: Gerlach, Richard; Naimoli, Antonio; Storti, Giuseppe
    Abstract: This paper proposes generalisations of the Realized GARCH model by Hansen et al. (2012), in three different directions. First, heteroskedasticity in the noise term in the measurement equation is allowed, since this is generally assumed to be time-varying as a function of an estimator of the Integrated Quarticity for intra-daily returns. Second, in order to account for attenuation bias effects, the volatility dynamics are allowed to depend on the accuracy of the realized measure. This is achieved by letting the response coefficient of the lagged realized measure depend on the time-varying variance of the volatility measurement error, thus giving more weight to lagged volatilities when they are more accurately measured. Finally, a further extension is proposed by introducing an additional explanatory variable into the measurement equation, aiming to quantify the bias due to effect of jumps and measurement errors.
    Keywords: Realized Volatility, Realized GARCH, Measurement Error, Realized Quarticity
    JEL: C22 C53 C58
    Date: 2018–01–08
  4. By: Riccardo Lucchetti (Di.S.E.S. - Universita' Politecnica delle Marche); Claudia Pigini (Di.S.E.S. - Universita' Politecnica delle Marche)
    Abstract: Estimation of random-effects dynamic probit models for panel data entails the so-called "initial conditions problem". We argue that the relative finitesample performance of the two main competing solutions is driven by the magnitude of the individual unobserved heterogeneity and/or of the state dependence in the data. We investigate our conjecture by means of a comprehensive Monte Carlo experiment and offer useful indications for the practitioner.
    Keywords: Dynamic panel probit; panel data; Monte Carlo study
    JEL: C23 C25
    Date: 2018–02
  5. By: Manabu Asai (Faculty of Economics Soka University, Japan.); Michael McAleer (Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute Erasmus School of Economics Erasmus University Rotterdam, The Netherlands and Department of Quantitative Economics Complutense University of Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.)
    Abstract: The paper develops a new realized matrix-exponential GARCH (MEGARCH) model, which uses the information of returns and realized measure of co-volatility matrix simultaneously. The paper also considers an alternative multivariate asymmetric function to develop news impact curves. We consider Bayesian MCMC estimation to allow non-normal posterior distributions. For three US financial assets, we compare the realized MEGARCH models with existing multivariate GARCH class models. The empirical results indicate that the realized MEGARCH models outperform the other models regarding in-sample and out-of-sample performance. The news impact curves based on the posterior densities provide reasonable results.
    Keywords: Multivariate GARCH; Realized Measure; Matrix-Exponential; Bayesian Markov chain Monte Carlo method; Asymmetry.
    JEL: C11 C32
    Date: 2018–01
  6. By: Chen Zhou (University of Paderborn)
    Abstract: A crucial problem by applying realized kernels (RK) is the selection of the bandwidth. We improve the iterative plug-in (IPI) algorithms for selecting bandwidth of RK under independent (Feng and Zhou, 2015) and dependent microstructure (MS) noise assumptions (Wang, 2013). The realized estimators calculated by both algorithms are consistent to the daily integrated volatility. The nice practical performance of these algorithms are illustrated by the application to 9 years of data on 10 European firms. Moreover, using these two algorithms we calculate RK based on different sampling frequencies and compare them with several other realized estimators. In total, we consider 9 different estimators. It is found that the data-frequencies over 5-minute are not suitable for calculating RK. RK computed from tick-by-tick returns by the algorithm under dependent noise assumption has the smallest standard deviation.
    JEL: C14 C51
    Date: 2018–01
  7. By: Mohitosh Kejriwal
    Abstract: This paper proposes a new procedure for estimating the number of structural changes in the persistence of a univariate time series. In contrast to the extant literature that primarily assumes (regime-wise) stationarity, our framework also allows the underlying stochastic process to switch between stationary [I(0)] and unit root [I(1)] regimes. We develop a sequential testing approach based on the simultaneous application of two Wald-type tests for structural change, one of which assumes the process is I(0)-stable under the null hypothesis while the other assumes the stable I(1) model as the null hypothesis. This feature allows the procedure to maintain correct asymptotic size regardless of whether the regimes are I(0) or I(1). We also propose a novel procedure for distinguishing processes with pure level and/or trend shifts from those that are also subject to concurrent shifts in persistence. The large sample properties of the recommended procedures are derived and the relevant asymptotic critical values tabulated. Our Monte Carlo experiments demonstrate that the advocated approach compares favorably relative to the commonly employed approach based on I(0) sequential testing, especially when the data contain an I(1) segment.
    Keywords: multiple structural changes, unit root, stationary, sequential procedure, Wald tests
    JEL: C22
    Date: 2017–12
  8. By: Wenjuan Chen; Aleksei Netsunajev
    Keywords: structural vector autoregression; Markov switching; time varying transition probabilities; identification via heteroscedasticity; uncertainty shocks; unemployment dynamics
    JEL: C32 D80 E24
    Date: 2018–02–13
  9. By: Christophe Chesneau (Université de Caen; LMNO); Salima El Kolei (CREST;ENSAI); Fabien Navarro (CREST; ENSAI)
    Abstract: In this paper, we study a speci?c hidden Markov chain de?ned by the equation: Yi = Xi + ei, i = 1,...,n + 1, where (Xi)i=1 is a real-valued stationary Markov chain and (ei)i=1 is a noise independent of (Xi)i=1. We develop a new parametric approach obtained by minimization of a particular contrast taking advantage of the regressive problem and based on deconvolution strategy. We provide theoretical guarantees on the performance of the resulting estimator; its consistency and its asymptotic normality are established.
    Keywords: Contrast function; deconvolution; least square estimation; parametric inference; stochastic volatility
    Date: 2017–09–30
  10. By: Koop, G; Korobilis, D
    Abstract: This paper develops methods for estimating and forecasting in Bayesian panel vector autoregressions of large dimensions with time-varying parameters and stochastic volatility. We exploit a hierarchical prior that takes into account possible pooling restrictions involving both VAR coeffcients and the error covariance matrix, and propose a Bayesian dynamic learning procedure that controls for various sources of model uncertainty. We tackle computational concerns by means of a simulation-free algorithm that relies on an analytical approximation of the posterior distribution. We use our methods to forecast inflation rates in the eurozone and show that forecasts from our flexible specification are superior to alternative methods for large vector autoregressions.
    Keywords: Panel VAR, inflation forecasting, Bayesian, time-varying parameter model
    Date: 2018–01–31

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