Challenges of integrated variance estimation in emerging stock markets

Abstract

Estimating integrated variance, using high frequency data, requires modelling experience and data crunching skills. Although intraday returns have attracted much attention in recent years, handling these data is challenging because of their unique characteristics. When dealing with ultra-high frequency or tick-by-tick observations the enormous amount of data needs to be processed prior to estimation of integrated variance for two reasons: eliminating microstructure noise and finding appropriate unbiased estimator. This paper contributes to the existing literature in a two ways. First, we propose how to handle quality issues of the high frequency data due to non-frequent trading and lower liquidity of emerging markets. Second, we find the optimal sampling frequency at slow time scale that should be used to obtain two-time scale estimator of integrated variance for each emerging market under consideration: Romania, Hungary, Bulgaria and Croatia. Empirical results indicate that intraday returns should be sampled every 7 to 10 minutes at slow time scale while the fast time scale should be fixed at the highest possible frequency. Realized variance estimator at the fast time scale mostly overestimates the integrated variance on all stock markets except Bulgaria; on average between 70% and 90% of the time. Moreover, the robustness of the results with respect to the price jumps has been verified for Romania and Hungary, unlike Croatia and Bulgaria, for which we recommend a robust version of two-time scale estimator of integrated variance within truncation technique. It is additionally found that intraday returns should be sampled more frequently in a highly volatile periods. These findings offer valuable information to market participants, as they are able to apply the most accurate ex-post volatility measure, as unbiased and consistent estimate of integrated variance.