Abstract
AbstractIn 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 vol...
Highlights
Estimation of volatility in asset returns has been an important area of research in the finance literature
Even though the standard deviation based on squared returns as a proxy of volatility has a statistical drawback over absolute deviation, the latter has been used extensively in the advancement of volatility estimation models in the financial literature (Black & Scholes, 1973; Engle, 1982; Bollerslev, 1986; Hull & White, 1987; Andersen & Bollerslev, 1997, Andersen, Bollerslev, & Ebens, 2001; Adriana & Chris, 2014)
Since the proposed estimators are independent of each other, we further propose a robust volatility ratio (RVR) to show that the Extreme Value Robust Volatility Estimator (EVRVE) is unbiased relative to the Classical Robust Volatility Estimator (CRVE) both in the population and in finite samples
Summary
Estimation of volatility in asset returns has been an important area of research in the finance literature. When the distribution of the financial data follows Gaussian normal distribution and is free of outliers, the standard deviation is considered to be a more efficient measure of dispersion than the absolute mean deviation (Fisher, 1920; Stigler, 1973; Aldrich, 1997; Hinton, 1995). Even though the standard deviation based on squared returns as a proxy of volatility has a statistical drawback over absolute deviation, the latter has been used extensively in the advancement of volatility estimation models in the financial literature (Black & Scholes, 1973; Engle, 1982; Bollerslev, 1986; Hull & White, 1987; Andersen & Bollerslev, 1997, Andersen, Bollerslev, & Ebens, 2001; Adriana & Chris, 2014)
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