Nonparametric Estimation of Volatility and Its Parametric Analogs


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This paper suggests a nonparametric method for stochastic volatility estimation and its comparison with other widespread econometric algorithms. A major advantage of this approach is that the volatility can be estimated even in the case of its completely unknown probability distribution. As demonstrated below, the new method has better characteristics against the popular parametric algorithms based on the GARCH model and Kalman filter.

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A. Dobrovidov

Trapeznikov Institute of Control Sciences

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Email: dobrovidov@gmail.com
俄罗斯联邦, Moscow

V. Tevosian

Trapeznikov Institute of Control Sciences

Email: dobrovidov@gmail.com
俄罗斯联邦, Moscow

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