Heterogeneous Market Hypothesis Evaluations using Various Jump-Robust Realized Volatility

The availability of high frequency data has promoted the usage of realized volatility as the unobservable latent volatility in financial markets. However, the traditional realized volatility (RV) representation is not robust to abrupt jumps in nowadays volatile globalized financial markets. This study includes other alternatives of jump-robust realized volatilities namely the bipower, minimum and median nearest neighbor truncation (NNT) volatility proxies in the examination of the heterogeneous market hypothesis (HMH) through the extension of heterogeneous autoregressive (HAR) model specifications. The empirical results show that the aforementioned alternative realized volatilities provide better forecast evaluations as compared to the standard realized volatility. Thus, the alternative realized volatility proxies are better explained the heterogeneous market hypothesis. In addition, the combination forecast models using three weighting schemes indicated better forecast performance as compared to the individual forecast. To complete this study, we illustrate a value-at-risk determination for the emerging Brazilian stock exchange.