nep-ets New Economics Papers
on Econometric Time Series
Issue of 2014‒08‒02
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Density forecasts with MIDAS models By Knut Are Aastveit; Claudia Foroni; Francesco Ravazzolo
  2. Asymmetric Realized Volatility Risk By David E. Allen; Michael McAleer; Marcel Scharth
  3. On the Invertibility of EGARCH By Guillaume Gaetan Martinet; Michael McAleer
  4. Deviance Information Criterion for Comparing VAR Models By Tao Zeng; Yong Li; Jun Yu
  5. Simple Robust Tests for the Specification of High-Frequency Predictors of a Low-Frequency Series By J. Isaac Miller

  1. By: Knut Are Aastveit (Norges Bank (Central Bank of Norway)); Claudia Foroni (Norges Bank (Central Bank of Norway)); Francesco Ravazzolo (Norges Bank (Central Bank of Norway))
    Abstract: In this paper we derive a general parametric bootstrapping approach to compute density forecasts for various types of mixed-data sampling (MIDAS) regressions. We consider both classical and unrestricted MIDAS regressions with and without an autoregressive component. First, we compare the forecasting performance of the different MIDAS models in Monte Carlo simulation experiments. We find that the results in terms of point and density forecasts are coherent. Moreover, the results do not clearly indicate a superior performance of one of the models under scrutiny when the persistence of the low frequency variable is low. Some differences are instead more evident when the persistence is high, for which the ARMIDAS and the AR-U-MIDAS produce better forecasts. Second, in an empirical exercise we evaluate density forecasts for quarterly US output growth, exploiting information from typical monthly series. We find that MIDAS models applied to survey data provide accurate and timely density forecasts.
    Keywords: Mixed data sampling, Density forecasts, Nowcasting
    JEL: C11 C53 E37
    Date: 2014–07–18
  2. By: David E. Allen; Michael McAleer (University of Canterbury); Marcel Scharth
    Abstract: In this paper we document that realized variation measures constructed from highfrequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility model, which incorporates the fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks.
    Keywords: Realized volatility, volatility of volatility, volatility risk, value-at-risk, forecasting, conditional heteroskedasticity
    JEL: C58 G12
    Date: 2014–07–17
  3. By: Guillaume Gaetan Martinet; Michael McAleer (University of Canterbury)
    Abstract: Of the two most widely estimated univariate asymmetric conditional volatility models, the exponential GARCH (or EGARCH) specification can capture asymmetry, which refers to the different effects on conditional volatility of positive and negative effects of equal magnitude, and leverage, which refers to the negative correlation between the returns shocks and subsequent shocks to volatility. However, the statistical properties of the (quasi-) maximum likelihood estimator (QMLE) of the EGARCH parameters are not available under general conditions, but only for special cases under highly restrictive and unverifiable conditions. A limitation in the development of asymptotic properties of the QMLE for EGARCH is the lack of an invertibility condition for the returns shocks underlying the model. It is shown in this paper that the EGARCH model can be derived from a stochastic process, for which the invertibility conditions can be stated simply and explicitly. This will be useful in re-interpreting the existing properties of the QMLE of the EGARCH parameters.
    Keywords: Leverage, asymmetry, existence, stochastic process, asymptotic properties, invertibility
    JEL: C22 C52 C58 G32
    Date: 2014–07–26
  4. By: Tao Zeng (Singapore Management University); Yong Li (Renmin University of China); Jun Yu (Singapore Management University, School of Economics)
    Abstract: Vector Autoregression (VAR) has been a standard empirical tool used in macroeconomics and finance. In this paper we discuss how to compare alternative VAR models after they are estimated by Bayesian MCMC methods. In particular we apply a robust version of deviance information criterion (RDIC) recently developed in Li et al. (2014b) to determine the best candidate model. RDIC is a better information criterion than the widely used deviance information criterion (DIC) when latent variables are involved in candidate models. Empirical analysis using US data shows that the optimal model selected by RDIC can be different from that by DIC.
    Keywords: Bayes factor, DIC; VAR models; Markov Chain Monte Carlo.
    JEL: C11 C12 G12
    Date: 2014–06
  5. By: J. Isaac Miller (Department of Economics, University of Missouri-Columbia)
    Abstract: I propose two simple variable addition test statistics for three tests of the specification of high-frequency predictors in a model to forecast a series observed at a lower frequency. The first is similar to existing test statistics and I show that it is robust to biased forecasts, integrated and cointegrated predictors, and deterministic trends, while it is feasible and consistent even if estimation is not feasible under the alternative. It is not robust to biased forecasts with integrated predictors under the null of a fully aggregated predictor, and size distortion may be severe in this case. The second test statistic proposed is an easily implemented modification of the first that sacrifices some power in small samples but is also robust to this case.
    Keywords: temporal aggregation, mixed-frequency model, MIDAS, variable addition test, forecasting model comparison
    JEL: C12 C22
    Date: 2014–07–14

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