On Quantile Estimator in Volatility Model with Non-negative Error Density and Bayesian Perspective
ISBN: 978-1-83867-420-5, eISBN: 978-1-83867-419-9
Publication date: 18 October 2019
Abstract
Stochastic volatility models are of great importance in the field of mathematical finance, especially for accurately explaining the dynamics of financial derivatives. A quantile-based estimator for the location parameter of a stochastic volatility model is proposed by solving an optimization problem. In this chapter, the asymptotic distribution of the estimator is derived without assuming that the density function of the noise is positive around the corresponding population quantile. We also discuss a Bayesian approach to the quantile estimation problem and establish a result regarding the nature of the posterior distribution.
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Acknowledgements
Acknowledgments
The authors are thankful to the editors and the reviewer, for their valuable and constructive comments on the chapter has substantially improved its quality. This research has been supported by the facilities at both University of Melbourne, Australia, and Indian Institute of Technology, Kanpur, India.
Citation
Dutta, D., Dhar, S.S. and Mitra, A. (2019), "On Quantile Estimator in Volatility Model with Non-negative Error Density and Bayesian Perspective", Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B (Advances in Econometrics, Vol. 40B), Emerald Publishing Limited, Leeds, pp. 193-210. https://doi.org/10.1108/S0731-90532019000040B010
Publisher
:Emerald Publishing Limited
Copyright © 2019 Emerald Publishing Limited