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Volatility behavior of asset returns based on robust volatility ratio: Empirical analysis on global stock indices

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  • Muneer Shaik
  • S. Maheswaran

Abstract

In this paper, we come up with an alternate theoretical proof for the independence and unbiased property of extreme value robust volatility estimator with respect to the standard robust volatility estimator. We show that the robust volatility ratio is unbiased both in the population and in the finite samples. We empirically test the robust volatility ratio on nine global stock indices from America, Asia Pacific and EMEA markets for the period from January 1996 to June 2017 based on daily open, high, low and close prices to understand the volatility behavior of stock returns over a period of time. Our results show that robust volatility ratio for different k-month periods is significantly less than 1 for all the global stock indices, thus finding the clear evidence of random walk behavior. This is possibly the first study based on robust volatility ratio to understand the volatility behavior of global stock indices.

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  • Muneer Shaik & S. Maheswaran, 2019. "Volatility behavior of asset returns based on robust volatility ratio: Empirical analysis on global stock indices," Cogent Economics & Finance, Taylor & Francis Journals, vol. 7(1), pages 1597430-159, January.
    • Handle: RePEc:taf:oaefxx:v:7:y:2019:i:1:p:1597430
    DOI: 10.1080/23322039.2019.1597430
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    Cited by:

    1. Jiayang Yu & Kuo-Chu Chang, 2020. "Neural Network Predictive Modeling on Dynamic Portfolio Management—A Simulation-Based Portfolio Optimization Approach," JRFM, MDPI, vol. 13(11), pages 1-23, November.

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