Score-driven location plus scale models: asymptotic theory and an application to forecasting Dow Jones volatility

IF 0.7 4区经济学 Q3 ECONOMICS

Studies in Nonlinear Dynamics and Econometrics Pub Date : 2022-03-07 DOI:10.1515/snde-2021-0083

Szabolcs Blazsek, A. Escribano, Adrián Licht

引用次数: 8

Abstract

Abstract We present the Beta-t-QVAR (quasi-vector autoregression) model for the joint modelling of score-driven location plus scale of strictly stationary and ergodic variables. Beta-t-QVAR is an extension of Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) and Beta-t-EGARCH-M (Beta-t-EGARCH-in-mean). We prove the asymptotic properties of the maximum likelihood (ML) estimator for correctly specified Beta-t-QVAR models. We use Dow Jones Industrial Average (DJIA) data for the period of 1985–2020. We find that the volatility forecasting accuracy of Beta-t-QVAR is superior to the volatility forecasting accuracies of Beta-t-EGARCH, Beta-t-EGARCH-M, A-PARCH (asymmetric power ARCH), and GARCH for the period of 2010–2020.

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分数驱动的位置加规模模型：渐近理论及其在道琼斯波动预测中的应用

摘要我们提出了Beta-t-QVAR（准向量自回归）模型，用于对严格平稳和遍历变量的分数驱动位置加尺度的联合建模。Beta-t-QVAR是Beta-t-EGARCH（指数广义自回归条件异方差）和Beta-t-EG ARCH-M（Beta-t-EGARCH-in-man）的扩展。我们证明了正确指定的Beta-t-QVAR模型的最大似然（ML）估计量的渐近性质。我们使用道琼斯工业平均指数（DJIA）1985-2020年的数据。我们发现，在2010-2020年期间，Beta-t-QVAR的波动率预测精度优于Beta-t-EGARCH、Beta-t-EGARCH-M、A-PARCH（不对称功率ARCH）和GARCH的波动率预报精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Studies in Nonlinear Dynamics and Econometrics Multiple-

CiteScore

1.40

自引率

12.50%

发文量

期刊介绍： Studies in Nonlinear Dynamics & Econometrics (SNDE) recognizes that advances in statistics and dynamical systems theory may increase our understanding of economic and financial markets. The journal seeks both theoretical and applied papers that characterize and motivate nonlinear phenomena. Researchers are required to assist replication of empirical results by providing copies of data and programs online. Algorithms and rapid communications are also published.