Title : ( Empirical Likelihood Inference for a Stochastic Volatility Model )
Authors: Vahid Fakoor ,
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Abstract
Volatility estimation is an important issue in certain aspects of the financial community, such as risk management and asset pricing. Our main focus in this paper, which is a summary of the article of Yazdani et al. (2022) †, is to study the behavior of volatility dynamics of some general stochastic economic models. First, we apply the local polynomial kernel smoothing method based on nonparametric regression to estimating the mean and the variance of the returns. We then implement and develop an empirical likelihood procedure in terms of conditional variance on daily log returns for inference on the nonparametric stochastic volatility as well as to construct a confidence interval for the volatility function. Some numerical results in connection to real data on the highly volatile Bitcoin dataset are also illustrated.
Keywords
, Stochastic volatility, Empirical likelihood, Local polynomial regression, Computational finance, Nonparametric methods.
@inproceedings{paperid:1091170,
author = {Fakoor, Vahid},
title = {Empirical Likelihood Inference for a Stochastic Volatility Model},
booktitle = {شانزدهمین کنفرانس آمار ایران},
year = {2022},
location = {بابلسر, IRAN},
keywords = {Stochastic volatility; Empirical likelihood; Local polynomial regression; Computational finance; Nonparametric methods.},
}
%0 Conference Proceedings
%T Empirical Likelihood Inference for a Stochastic Volatility Model
%A Fakoor, Vahid
%J شانزدهمین کنفرانس آمار ایران
%D 2022
