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On Quantile Estimator in Volatility Model with Non-negative Error Density and Bayesian Perspective

In: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B

Author

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  • Debajit Dutta
  • Subhra Sankar Dhar
  • Amit Mitra

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.

Suggested Citation

  • Debajit Dutta & Subhra Sankar Dhar & Amit Mitra, 2019. "On Quantile Estimator in Volatility Model with Non-negative Error Density and Bayesian Perspective," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, volume 40, pages 193-210, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-90532019000040b010
    DOI: 10.1108/S0731-90532019000040B010
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