Journal of Applied Finance & Banking
Modified component GARCH, long-run volatility, macroeconomic effect, forecasting, business cycles
Business | Macroeconomics
Forecasting equity volatility was thoroughly investigated during the past three decades. The majority based their forecasts on the dynamics of the underlying equity time series. They helped better understand the dynamics of these time series and understand different aspects of volatility. Other models went a step further to include the effect of news announcement on equity volatility. The vast majority ignored the effect of macroeconomic variable or the state of the economy. This paper proposes a volatility-forecasting model that accounts for effect of fundamental macroeconomic variables that reflect the state of the economy. The explanatory variables used measure the stage of business cycle, uncertainty about the fundamental economic variables, and a prediction of the future state of the economy. All these variables have been documented in the empirical literature or in the economic theory to have an effect on equity volatility. Another major contribution is the way volatility is being measured. The proposed model uses MC-GARCH model to measure the long-term volatility without losing much of the relevant information or the characteristics of the volatility time series. This paper also has some policy implications as it shows the relationship between fundamental macroeconomic variables and equity market volatility.
Jang Hyung Cho and Ahmed Elshahat. "Macroeconomic Variables Effect on US Market Volatility using MC-GARCH Model" Journal of Applied Finance & Banking 4.1 (2014): 91-102.
This article was published in Journal of Applied Finance & Banking, volume 4, issue 1, 2014 and can also be downloaded here.