Nonparametric Estimation of Volatility and Its Parametric Analogs
- Authors: Dobrovidov A.V.1, Tevosian V.E.1
- 
							Affiliations: 
							- Trapeznikov Institute of Control Sciences
 
- Issue: Vol 79, No 9 (2018)
- Pages: 1687-1702
- Section: Control Sciences
- URL: https://journals.rcsi.science/0005-1179/article/view/151022
- DOI: https://doi.org/10.1134/S0005117918090126
- ID: 151022
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Abstract
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.
About the authors
A. V. Dobrovidov
Trapeznikov Institute of Control Sciences
							Author for correspondence.
							Email: dobrovidov@gmail.com
				                					                																			                												                	Russian Federation, 							Moscow						
V. E. Tevosian
Trapeznikov Institute of Control Sciences
														Email: dobrovidov@gmail.com
				                					                																			                												                	Russian Federation, 							Moscow						
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