Nonlinear autoregressive model with stochastic volatility innovations: Semiparametric and Bayesian approach

被引：5

作者：

Hajrajabi, A. ^{[1
]}

Yazdanian, A. R. ^{[2
]}

Farnoosh, R. ^{[3
]}

机构：

[1] Imam Khomeini Int Univ, Fac Basic Sci, Dept Stat, Qazvin, Iran

[2] Semnan Univ, Fac Math Stat & Comp Sci, Semnan, Iran

[3] Iran Univ Sci & Technol, Sch Math, Tehran, Iran

来源：

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2018年 / 344卷

关键词：

Stochastic volatility; Semiparametric estimation; Sequential Monte Carlo filtering; Bayesian estimation; CHAIN MONTE-CARLO;

D O I：

10.1016/j.cam.2018.05.036

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The first-order nonlinear autoregressive model with the stochastic volatility as the model of dependent innovations is considered and a semiparametric method is proposed to estimate the unknown function. Optimal filtering technique based on sequential Monte Carlo perspective is used for estimation of the hidden log-volatility in this model. Bayesian paradigm is applied for estimation of both the unknown parameters and hidden process using particle marginal Metropolis-Hastings scheme. Furthermore, an empirical application on simulated data and on the monthly excess returns of S&P 500 index is presented to study the performance of the schemes implemented. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：37 / 46

页数：10

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