Econometrics
http://lists.repec.org/mailman/listinfo/nep-ecm
Econometrics2014-04-05Sune KarlssonMatrix Box-Cox Models for Multivariate Realized Volatility
http://d.repec.org/n?u=RePEc:bay:rdwiwi:29687&r=ecm
We propose flexible models for multivariate realized volatility dynamics which involve generalizations of the Box-Cox transform to the matrix case. The matrix Box-Cox model of realized covariances (MBC-RCov) is based on transformations of the covariance matrix eigenvalues, while for the Box-Cox dynamic correlation (BC-DC) specification the variances are transformed individually and modeled jointly with the correlations. We estimate transformation parameters by a new multivariate semiparametric estimator and discuss bias-corrected point and density forecasting by simulation. The methods are applied to stock market data where excellent in-sample and out-of-sample performance is found.Weigand, Roland2014-03Realized covariance matrix; dynamic correlation; semiparametric estimation; density forecastingThe seasonal KPSS Test: some extensions and further results
http://d.repec.org/n?u=RePEc:pra:mprapa:54920&r=ecm
The literature distinguishes finite sample studies of seasonal stationarity quite less intensely than it shows for seasonal unit root tests. Therefore, the use of both types of tests for better exploring time series dynamics is seldom noticed in the relative studies on such a topic. Recently, Lyhagen (2006) introduced for quarterly data the seasonal KPSS test which null hypothesis is no seasonal unit roots. In the same manner, as most unit root limit theory, the asymptotic theory of the seasonal KPSS test depends on whether the data has been filtered by a preliminary regression. More specifically, one may proceed to the extraction of deterministic components – such as the mean and trend – from the data before testing. In this paper, I took account of de-trending on the seasonal KPSS test. A sketch of its limit theory was provided in this case. Also, I studied in finite sample the behaviour of the test for monthly time series. This could enrich our knowledge about it since it was – as I mentioned above – early introduced for quarterly data. Overall, the obtained results showed that the seasonal KPSS test preserved its good size and power properties. Furthermore, like the test of Kwiatkowski et al. [KPSS] (1992), the nonparametric corrections of residual variances may smooth the wide variations of the seasonal KPSS empirical sizes.El Montasser, Ghassen2014-03-10KPSS test, deterministic seasonality, Brownian motion, LM testOn uniqueness of moving average representations of heavy-tailed stationary processes
http://d.repec.org/n?u=RePEc:pra:mprapa:54907&r=ecm
We prove the uniqueness of linear i.i.d. representations of heavy-tailed processes whose distribution belongs to the domain of attraction of an $\alpha$-stable law, with $\alphaGouriéroux, Christian, Zakoian, Jean-Michel2014-03-31$\alpha$-stable distribution; Domain of attraction; Infinite moving average; Linear process; Mixed causal/noncausal process; Nonparametric identification; Unobserved component model.Noncausal Bayesian Vector Autoregression
http://d.repec.org/n?u=RePEc:aah:create:2014-07&r=ecm
We propose a Bayesian inferential procedure for the noncausal vector autoregressive (VAR) model that is capable of capturing nonlinearities and incorporating effects of missing variables. In particular, we devise a fast and reliable posterior simulator that yields the predictive distribution as a by-product. We apply the methods to postwar quarterly U.S. inflation and GDP growth series. The noncausal VAR model turns out to be superior in terms of both in-sample fit and out-of-sample forecasting performance over its conventional causal counterpart. In addition, we find GDP growth to have predictive power for the future distribution of inflation over and above the own history of inflation, but not vice versa. This may be interpreted as evidence against the new Keynesian model that implies Granger causality from inflation to GDP growth, provided GDP growth is a reasonable proxy of the marginal cost.Markku Lanne, Jani Luoto2014-05-24Noncausal time series, non-Gaussian time series, Bayesian analysis, New Keynesian modelReduced-rank time-varying vector autoregressions
http://d.repec.org/n?u=RePEc:cpb:discus:270&r=ecm
The standard time-varying VAR workhorse suffers from overparameterization, which is a serious problem as it limits the number of variables and lags that can be incorporated in the model. Read also: CPB Discussion Paper 271 ' Time variation in the dynamic effects of unanticipated changes in tax policy '. As a solution for the overparameterization problem, we propose a new, more parsimonious time-varying VAR model setup with which we can reliably estimate larger models including more variables and/or more lags than was possible until now. The new model setup implies cross-equation restrictions on the time variation that are empirically supported, theoretically appealing, and make the Bayesian estimation procedure much faster.Joris de Wind, Luca Gambetti2014-03Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance
http://d.repec.org/n?u=RePEc:cbt:econwp:14/10&r=ecm
Modelling covariance structures is known to suffer from the curse of dimensionality. In order to avoid this problem for forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures that accommodates asymmetry and long memory. Using the basic structure of the fMSV model, the authors extend the dynamic correlation MSV model, the conditional/stochastic Wishart autoregressive models, the matrix-exponential MSV model, and the Cholesky MSV model. Empirical results for 7 financial asset returns for US stock returns indicate that the new fMSV models outperform existing dynamic conditional correlation models for forecasting future covariances. Among the new fMSV models, the Cholesky MSV model with long memory and asymmetry shows stable and better forecasting performance for one-day, five-day and ten-day horizons in the periods before, during and after the global financial crisis.Manabu Asai, Michael McAleer2014-03-17Dimension reduction; Factor Model; Multivariate Stochastic Volatility; Leverage Effects; Long Memory; Realized VolatilityTheoretical guidelines for a partially informed forecast examiner
http://d.repec.org/n?u=RePEc:pra:mprapa:55017&r=ecm
The paper explores probability theory foundations behind evaluation of probabilistic forecasts. The emphasis is on a situation when the forecast examiner possesses only partially the information which was available and was used to produce a forecast. We argue that in such a situation forecasts should be judged by their conditional auto-calibration. Necessary and sufficient conditions of auto-calibration are discussed and expressed in the form of testable moment conditions. The paper also analyzes relationships between forecast calibration and forecast efficiency.Tsyplakov, Alexander2014-04-02probabilistic forecast; forecast calibration; moment condition; probability integral transform; orthogonality condition; scoring rule; forecast encompassing