The purpose of this paper is twofold: first, to propose a new robust volatility ratio (RVR) that compares the intraday high–low volatility with that of the intraday open–close volatility estimator; and second, to empirically test the proposed RVR on the cross-sectional (CS) average of the constituent stocks of India’s BSE Sensex and US’s Dow Jones Industrial Average index to find the evidence of “excess volatility.”
The authors model the proposed RVR by assuming the logarithm of the price process to follow the Brownian motion. The authors have theoretically shown that the RVR is unbiased in the case of zero drift parameter. Moreover, the RVR is found to be an even function of the non-zero drift parameter.
The empirical results show that the analysis based on the RVR supports the existence of “excess volatility” in the CS average of the constituent stocks of India’s BSE Sensex and US’s Dow Jones index. In particular, the authors have observed that the CS average of individual constituent stocks of BSE Sensex is found to be more excessively volatile than the US’s Dow Jones index during the period of the study from January 2008 to September 2016, based on multiple k-day time window analysis.
The study has implications for the policy makers and practitioners who would like to understand the volatility behavior in the asset returns based on the RVR of this study. In general, the proposed model can be used as a specification tool to find whether the stock prices follow the random walk behavior or excessively volatile.
The authors contribute to the existing volatility literature in finance by proposing a new RVR based on extreme values of asset prices and absolute returns. The authors implement the bootstrap technique on RVR to find the estimates of mean and standard error for multiple k-day time windows. The RVR can capture the excess volatility by comparing two independent volatility estimators. This is possibly the first study to find the CS average of all the constituent stocks of BSE Sensex based on the RVR.
The authors of this paper have not made their research data set openly available. Any enquiries regarding the data set can be directed to the corresponding author.
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