Modeling and Predicting the Market Volatility Index: The Case of VKOSPI [Dataset]

The KOSPI 200 options are one of the most actively traded derivatives in the world. This paper empirically examines (a) the statistical properties of the Korea’s representative implied volatility index (VKOSPI) derived from the KOSPI 200 options and (b) macroeconomic and financial variables that can predict the implied volatility process of the index, using augmented heterogeneous autoregressive (HAR) models with exogenous covariates. The results suggest that the dynamics of the VKOSPI is well described by the elaborate HAR framework and that some Korea’s macroeconomic variables significantly explain the VKOSPI. In addition, we find that the stock market return and implied volatility index of the US market (i.e., the S &P 500 spot return and the VIX from S&P 500 options) play a key role in predicting the level of VKOSPI and explaining its dynamics, and their explanatory power dominates that of Korea’s macro-finance variables. Further, while Korea’s stock market return does not predict the VKOSPI, US stock market return well predicts the future VKOSPI level. When both US stock market return and US implied volatility index are incorporated into the HAR framework, the model’s both in-sample fitting and out-of-sam ple forecasting ability exhibits the best performance.