nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒07‒09
nine papers chosen by
Yong Yin
SUNY at Buffalo

  1. Iterated Multi-Step Forecasting with Model Coefficients Changing Across Iterations By Michal Franta
  2. Sparse multivariate GARCH By Jianbin Wu; Geert Dhaene
  3. Mixed-frequency multivariate GARCH By Geert Dhaene; Jianbin Wu
  4. The risk-return tradeoff in international stock markets: one-step multivariate GARCH-M estimation with many assets By Geert Dhaene; Piet Sercu; Jianbin Wu
  5. Forecasting Economic Activity with Mixed Frequency Bayesian VARs By Brave, Scott; Butters, R. Andrew; Justiniano, Alejandro
  6. Measuring spot variance spillovers when (co)variances are time-varying - the case of multivariate GARCH models By Fengler, Matthias R.; Herwartz, Helmut
  7. Forecast evaluation with factor-augmented models By Jack Fosten
  8. Model selection with factors and variables By Jack Fosten
  9. Are Correlations Constant? Empirical and Theoretical Results on Popular Correlation Models in Finance By Adams, Zeno; Fuess, Roland; Glueck, Thorsten

  1. By: Michal Franta
    Abstract: Iterated multi-step forecasts are usually constructed assuming the same model in each forecasting iteration. In this paper, the model coefficients are allowed to change across forecasting iterations according to the in-sample prediction performance at a particular forecasting horizon. The technique can thus be viewed as a combination of iterated and direct forecasting. The superior point and density forecasting performance of this approach is demonstrated on a standard medium-scale vector autoregression employing variables used in the Smets and Wouters (2007) model of the US economy. The estimation of the model and forecasting are carried out in a Bayesian way on data covering the period 1959Q1-2016Q1.
    Keywords: Bayesian estimation, direct forecasting, iterated forecasting, multi-step forecasts, VAR
    JEL: C11 C32 C53
    Date: 2016–06
  2. By: Jianbin Wu; Geert Dhaene
    Abstract: We propose sparse versions of multivariate GARCH models that allow for volatility and correlation spillover effects across assets. The proposed models are generalizations of existing diagonal DCC and BEKK models, yet they remain estimable for high-dimensional systems of asset returns. To cope with the high dimensionality of the model parameter spaces, we employ the L1 regularization technique to penalize the off-diagonal elements of the coefficient matrices. A simulation experiment for the sparse DCC model shows that the true underlying sparse parameter structure can be uncovered reasonably well. In an application to weekly and daily market returns for 24 countries using data from 1994 to 2014, we find that the sparse DCC model outperforms the standard DCC and the diagonal DCC models in and out of sample. Likewise, the sparse BEKK model outperforms the diagonal BEKK model.
    Date: 2016–06
  3. By: Geert Dhaene; Jianbin Wu
    Abstract: We introduce and evaluate mixed-frequency multivariate GARCH models for forecasting low-frequency (weekly or monthly) multivariate volatility based on high-frequency intra-day returns (at five-minute intervals) and on the overnight returns. The low-frequency conditional volatility matrix is modelled as a weighted sum of an intra-day and an overnight component, driven by the intra-day and the overnight returns, respectively. The components are specified as multivariate GARCH (1,1) models of the BEKK type, adapted to the mixed-frequency data setting. For the intra-day component, the squared high-frequency returns enter the GARCH model through a parametrically specified mixed-data sampling (MIDAS) weight function or through the sum of the intra-day realized volatilities. For the overnight component, the squared overnight returns enter the model with equal weights. Alternatively, the low-frequency conditional volatility matrix may be modelled as a single-component BEKK-GARCH model where the overnight returns and the high-frequency returns enter through the weekly realized volatility (defined as the unweighted sum of squares of overnight and high-frequency returns), or where the overnight returns are simply ignored. All model variants may further be extended by allowing for a non-parametrically estimated slowly-varying long-run volatility matrix. The proposed models are evaluated using five-minute and overnight return data on four DJIA stocks (AXP, GE, HD, and IBM) from January 1988 to November 2014. The focus is on forecasting weekly volatilities (defined as the low frequency). The mixed-frequency GARCH models are found to systematically dominate the low-frequency GARCH model in terms of in-sample fit and out-of-sample forecasting accuracy. They also exhibit much lower low-frequency volatility persistence than the low-frequency GARCH model. Among the mixed-frequency models, the low-frequency persistence estimates decrease as the data frequency increases from daily to five-minute frequency, and as overnight returns are included. That is, ignoring the available high-frequency information leads to spuriously high volatility persistence. Among the other findings are that the single-component model variants perform worse than the two-component variants; that the overnight volatility component exhibits more persistence than the intra-day component; and that MIDAS weighting performs better than not weighting at all (i.e., than realized volatility).
    Date: 2016–06
  4. By: Geert Dhaene; Piet Sercu; Jianbin Wu
    Abstract: We study international asset pricing in a large-dimensional multivariate GARCH-in-mean framework. We examine different estimation methods and find that the two-step estimation method proposed by Bali and Engle (2010) tends to underestimate the risk-return coefficient and the corresponding standard error. We also show that the estimate is improved by one-step estimation and by increasing the cross-sectional dimension. Using stock index returns for up to 24 countries and 4 major currencies in the period 2001-2015, one-step estimation gives a market risk-return coefficient of around 6. The estimate is robust to variations in model specification, data frequency, and the number of stock markets considered.
    Date: 2016–06
  5. By: Brave, Scott (Federal Reserve Bank of Chicago); Butters, R. Andrew (Indiana University); Justiniano, Alejandro (Federal Reserve Bank of Chicago)
    Abstract: Mixed frequency Bayesian vector autoregressions (MF-BVARs) allow forecasters to incorporate a large number of mixed frequency indicators into forecasts of economic activity. This paper evaluates the forecast performance of MF-BVARs relative to surveys of professional forecasters and investigates the influence of certain specification choices on this performance. We leverage a novel real-time dataset to conduct an out-of-sample forecasting exercise for U.S. real gross domestic product (GDP). MF-BVARs are shown to provide an attractive alternative to surveys of professional forecasters for forecasting GDP growth. However, certain specification choices such as model size and prior selection can affect their relative performance.
    Keywords: Mixed frequency; Bayesian VAR; Real-time data; Nowcasting
    JEL: C32 C53 E37
    Date: 2016–05–20
  6. By: Fengler, Matthias R.; Herwartz, Helmut
    Abstract: We propose global and disaggregated spillover indices that allow us to assess variance and covariance spillovers, locally in time and conditionally on time-t information. Key to our approach is the vector moving average representation of the half-vectorized `squared' multivariate GARCH process of the popular BEKK model. In an empirical application to a four-dimensional system of broad asset classes (equity, fixed income, foreign exchange and commodities), we illustrate the new spillover indices at various levels of (dis)aggregation. Moreover, we demonstrate that they are informative of the value-at-risk violations of portfolios composed of the considered asset classes.
    Keywords: BEKK model, forecast error variance decomposition, multivariate GARCH, spillover index, value-at-risk, variance spillovers
    JEL: C32 C58 F3 G1
    Date: 2015–03–17
  7. By: Jack Fosten (University of East Anglia)
    Abstract: This paper provides an extension of Diebold-Mariano-West (DMW) forecast accuracy tests to allow for factor-augmented models to be compared with non-nested benchmarks. The out-of- sample approach to forecast evaluation requires that both the factors and the forecasting model parameters are estimated in a rolling fashion, which poses several new challenges which we address in this paper. Firstly, we show the convergence rates of factors estimated in different rolling windows, and then give conditions under which the asymptotic distribution of the DMW test statistic is not affected by factor estimation error. Secondly, we draw attention to the issue of "sign-changing" across rolling windows of factor estimates and factor-augmented model coefficients, caused by the lack of sign identification when using Principal Components Analysis to estimate the factors. We show that arbitrary sign-changing does not affect the distribution of the DMW test statistic, but it does prohibit the construction of valid bootstrap critical values using existing procedures. We solve this problem by proposing a novel new normalization for rolling factor estimates, which has the effect of matching the sign of factors estimated in different rolling windows. We establish the first-order validity of a simple-to-implement block bootstrap procedure and illustrate its properties using Monte Carlo simulations and an empirical application to forecasting U.S. CPI inflation.
    Keywords: boostrap, diffusion index, factor model, predictive ability
    JEL: C12 C22 C38 C53
    Date: 2016–01–28
  8. By: Jack Fosten (University of East Anglia)
    Abstract: This paper provides consistent information criteria for the selection of forecasting models which use a subset of both the idiosyncratic and common factor components of a big dataset. This hybrid model approach has been explored by recent empirical studies to relax the strictness of pure factor-augmented model approximations, but no formal model selection procedures have been developed. The main difference to previous factor-augmented model selection procedures is that we must account for estimation error in the idiosyncratic component as well as the factors. Our first contribution shows that this combined estimation error vanishes at a slower rate than in the case of pure factor-augmented models in circumstances in which N is of larger order than sqrt(T), where N and T are the cross-section and time series dimensions respectively. Under these circumstances we show that existing factor-augmented model selection criteria are inconsistent, and the standard BIC is inconsistent regardless of the relationship between N and T. Our main contribution solves this issue by proposing new information criteria which account for the additional source of estimation error, whose properties are explored through a Monte Carlo simulation study. We conclude with an empirical application to long-horizon exchange rate forecasting using a recently proposed model with country-specific idiosyncratic components from a panel of global exchange rates.
    Keywords: forecasting, factor model, model selection, information criteria, idiosyncratic
    JEL: C13 C22 C38 C52 C53
    Date: 2016–03–14
  9. By: Adams, Zeno; Fuess, Roland; Glueck, Thorsten
    Abstract: Multivariate GARCH models have been designed as an extension of their univariate counterparts. Such a view is appealing from a modeling perspective but imposes correlation dynamics that are similar to time-varying volatility. In this paper, we argue that correlations are quite different in nature. We demonstrate that the highly unstable and erratic behavior that is typically observed for the correlation among financial assets is to a large extent a statistical artefact. We provide evidence that spurious correlation dynamics occur in response to financial events that are sufficiently large to cause a structural break in the time-series of correlations. A measure for the autocovariance structure of conditional correlations allows us to formally demonstrate that the volatility and the persistence of daily correlations are not primarily driven by financial news but by the level of the underlying true correlation. Our results indicate that a rolling-window sample correlation is often a better choice for empirical applications in finance.
    Keywords: Change-point tests; correlation breaks; dynamic conditional correlation (DCC); multivariate GARCH models; spurious conditional correlation
    JEL: C12 C52 G01 G11
    Date: 2016–06

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