nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒02‒07
four papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Identifying High-Frequency Shockswith Bayesian Mixed-Frequency VARs By Alessia Paccagnini; Fabio Parla
  2. Too Many Shocks Spoil the Interpretation By Adrian Pagan; Tim Robinson
  3. Forecasting Stock Market Volatility with Regime-Switching GARCH-MIDAS: The Role of Geopolitical Risks By Mawuli Segnon; Rangan Gupta
  4. Limiting Spectral Distribution of High-dimensional Hayashi-Yoshida Estimator of Integrated Covariance Matrix By Arnab Chakrabarti; Rituparna Sen

  1. By: Alessia Paccagnini (University College Dublin and CAMA); Fabio Parla (Bank of Lithuania)
    Abstract: We contribute to research on mixed-frequency regressions by introducing an innovative Bayesian approach. We impose a Normal-inverse Wishart prior by adding a set of auxiliary dummies in estimating a Mixed-Frequency VAR. We identify a high frequency shock in a Monte Carlo experiment and in an illustrative example with uncertainty shock for the U.S. economy. As the main findings, we document a “temporal aggregation bias” when we adopt a common low-frequency model instead of estimating a mixed-frequency framework. The bias is amplified in case of a large mismatching between the highfrequency shock and low-frequency business cycle variables.
    Keywords: Bayesian mixed-frequency VAR, MIDAS, Monte Carlo, uncertainty shocks, macro-financial linkages
    JEL: C32 E44 E52
    Date: 2021–12–29
  2. By: Adrian Pagan (School of Economics, University of Sydney; CAMA, Australian National University); Tim Robinson (Melbourne Institute: Applied Economic & Social Research, The University of Melbourne)
    Abstract: We show that when a model has more shocks than observed variables the estimated filtered and smoothed shocks will be correlated. This is despite no correlation being present in the data generating process. Additionally the estimated shock innovations may be autocorrelated. These correlations limit the relevance of impulse responses, which assume uncorrelated shocks, for interpreting the data. Excess shocks occur frequently, e.g. in UnobservedComponent (UC) models, filters, including Hodrick-Prescott (1997), and some Dynamic Stochastic General Equilibrium (DSGE) models. Using several UC models and an estimated DSGE model, Ireland (2011), we demonstrate that sizable correlations among the estimated shocks can result.
    Keywords: Partial Information; Structural Shocks; Kalman Filter; Measurement Error; DSGE.
    JEL: E37 C51 C52
    Date: 2020–02
  3. By: Mawuli Segnon (Department of Economics, Institute for Econometric and Economic Statistics and Chair of Empirical Economics, University of Munster, Germany); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: We investigate the role of geopolitical risks (GPR) in forecasting stock market volatility in a robust autoregressive Markov-switching GARCH mixed data sampling (ARMSGARCH-MIDAS) framework that accounts for structural breaks through regime switching and allows us to disentangle short- and long-run volatility components driven by geopolitical risks. An empirical out-of-sample forecasting exercise is conducted using unique data sets on Dow Jones Industrial Average (DJIA) index and geopolitical risks that cover the time period from January 3, 1899 to December 31, 2020. We find that geopolitical risks as explanatory variables can help to improve the accuracy of stock market volatility forecasts. Furthermore, our empirical results show that the macroeconomic variables such as output measured by recessions, inflation and interest rates contain information that is complementary to the one included in the geopolitical risks.
    Keywords: Geopolitical risks, Volatility forecasts, Markov-switching GARCH-MIDAS
    JEL: C52 C53 C58
    Date: 2022–01
  4. By: Arnab Chakrabarti; Rituparna Sen
    Abstract: In this paper, the estimation of the Integrated Covariance matrix from high-frequency data, for high dimensional stock price process, is considered. The Hayashi-Yoshida covolatility estimator is an improvement over Realized covolatility for asynchronous data and works well in low dimensions. However it becomes inconsistent and unreliable in the high dimensional situation. We study the bulk spectrum of this matrix and establish its connection to the spectrum of the true covariance matrix in the limiting case where the dimension goes to infinity. The results are illustrated with simulation studies in finite, but high, dimensional cases. An application to real data with tick-by-tick data on 50 stocks is presented.
    Date: 2022–01

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