Narrow Results

This paper explores how macroeconomic fundamentals affect the long-run dynamics in the volatility of and correlation between the stock and bond market. We use China as a laboratory by employing the GARCH-MIDAS and DCC-MIDAS models, which possess proven ability to capture the long-run component of second-moment market performance in a rapidly-growing economy. With actual data and predictivity evaluation, the finding is that the industrial production growth rate accounts for 32.04% of variations in the total conditional volatility for Chinese stocks, and the industrial production growth volatility drives 29.63% of movements in the total conditional volatility for Chinese bonds. Our proposed model is particularly advantageous in long-run volatility forecasts. Moreover, the present study complements the literature concerning the impacts of macro factors on the time-varying correlations between China’s stock and bond markets. We find weak evidence in this regard, probably due to the absence of multi-market macro-strategy investors that prevail in more developed markets.

This paper examines the effect of Chinese macroeconomic variables, the industrial production growth rate, the producer price index, the 3-month short-term Shanghai Interbank Offer Rate and the consumer price index, on the volatility of the Shanghai and Hong Kong stock markets. We apply the generalized autoregressive conditional heteroskedastic mixed data sampling model for the study. Our empirical findings on various indexes and enterprises in the Shanghai and Hong Kong markets show that Chinese macroeconomic variables have a greater power to explain the volatility in Hong Kong than in Shanghai. They also contribute significantly to Hong Kong’s market volatility.

This chapter offers an extension to the literature on energy prices by forecasting the return volatility of these prices using the GARCH-MIDAS approach. In addition to the realized volatility, it also evaluates the predictability of relevant macroeconomic information such as industrial growth and consumer prices (with and without energy components) in the predictive model for the return volatility of energy prices. The analyses are distinctly conducted for full sample, pre-GFC and post-GFC periods. On average, the findings support the inclusion of these macroeconomic information, particularly output growth and realized volatility, as they yield good in-sample and out-of-sample predictability results for the return volatility. However, the study finds contrasting evidence between the pre-GFC and post-GFC periods.