Abstract
This paper further extends the existing GARCH-MIDAS model to deal with the effect of microstructure noise in mixed frequency data. This paper has two highlights. First, according to the estimation of the long-term volatility components of the GARCH-MIDAS model, rAVGRV is adopted to substitute for the RV estimator. rAVGRV uses the rich data sources in tick-by-tick data and significantly corrects the impact of the microstructure noise on volatility estimation. Second, besides introducing macroeconomic variables (i.e., macroeconomic consistency index (MCI), deposits in financial institutions (DFI), industrial value-added (IVA), and M2), Chinese Economic Policy Uncertainty (CEPU) index and Infectious Disease Equity Market Volatility Tracker (EMV) are introduced in the long-run volatility component of the GARCH-MIDAS model. As indicated by the results of this paper, the rAVGRV-based GARCH-MIDAS is slightly better than the RV model-based GARCH-MIDAS. In addition to the common macroeconomic variables significantly impacting stock market volatility, CEPU also substantially impacts stock market volatility. Nevertheless, the effect of EMV on the stock market is insignificant.
Highlights
Traditional econometric models have been extensively used to analyze macroeconomic and financial consistent sampling frequency data
Explanatory variables consist of macroeconomic consistency index (MCI), deposits in financial institutions (DFI), industrial value-added (IVA), and M2, as well as economic policy uncertainty index (EPUI) and Infectious Disease Equity Market Volatility Tracker (EMV). ey are monthly data
−5902.481 −5894.395 −5896.277 −5895.538 −5894.433 −5902.443 e bracketed numbers are the p value of the estimations. ∗∗∗, ∗∗, and ∗indicate rejection at the 1%, indicated by the above Table, the p value of the GARCHMIDAS model based on the rAVGRV statistic is slightly larger than the p value of the GARCH-Mixed Data Sampling (MIDAS) model based on the RV statistic, and the ranking of the model is higher after a two-by-two comparison. us, the results above demonstrate that the GARCH-MIDAS model can be better than the GARCH-MIDAS (RV) model to some extent, since the rAVGRV statistic removes the effect of noise in the estimation, and the estimated realized volatility can be more accurate
Summary
Traditional econometric models have been extensively used to analyze macroeconomic and financial consistent sampling frequency data. Most of the mentioned studies complied with low-frequency data models to examine the correlation between macroeconomics and stock market volatility. Over the past few years, among the studies on modeling problems of variables at different sampling frequencies, the Mixed Data Sampling (MIDAS) proposed by Ghysels et al [1] has aroused the biggest attention. Such a model can develop a linear correlation between high-frequency explanatory variables and low-frequency explanatory variables, and it has been extensively applied in studies on macroeconomics, stock market, and crude oil futures for its ability to fully draw upon available information. Asgharian et al [3] examined the effect of U.S macroeconomic variables on stock market volatility by adopting the GARCH-MIDAS model. E rest of the study is organized as follows. e second section elucidates the GARCH-MIDAS model. e third section refers to an empirical study that explores the estimation, forecasting the GARCH-MIDAS model built in the study at several levels. e fourth section presents the application of the model to the portfolio. e fifth section is the robustness analysis of this paper. e last section concludes the present study
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