Within-regime volatility dynamics for observable- and Markov-switching score-driven models

IF 7.4 2区经济学 Q1 BUSINESS, FINANCE

Finance Research Letters Pub Date : 2024-12-16 DOI:10.1016/j.frl.2024.106631

Szabolcs Blazsek, Dejun Kong, Samantha R. Shadoff

引用次数: 0

Abstract

We study the novel Markov-switching (MS) Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model, using within-regime volatility dynamics, similar to the recent observable-switching (OS) Beta-t-EGARCH model. We report in-sample results on the Standard & Poor’s 500 (S&P 500) and a random sample of 50 firms from the S&P 500 from March 1986 to July 2024. We compare the out-of-sample forecasting performances of OS-Beta-t-EGARCH and MS-Beta-t-EGARCH from May 2005 to July 2024 and confirm that OS-Beta-t-EGARCH is superior to MS-Beta-t-EGARCH.

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可观测和马尔可夫切换分数驱动模型的区间波动动态

我们研究了新的马尔可夫转换（MS）Beta-t-EGARCH（指数广义自回归条件异方差）模型，该模型使用了与最近的可观测转换（OS）Beta-t-EGARCH 模型类似的区间内波动动态。我们报告了 1986 年 3 月至 2024 年 7 月期间标准普尔 500 指数（S&P 500）和从 S&P 500 指数中随机抽取的 50 家公司的样本内结果。我们比较了 OS-Beta-t-EGARCH 和 MS-Beta-t-EGARCH 在 2005 年 5 月至 2024 年 7 月期间的样本外预测性能，结果证实 OS-Beta-t-EGARCH 优于 MS-Beta-t-EGARCH。

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

Finance Research Letters BUSINESS, FINANCE-

CiteScore

11.10

自引率

14.40%

发文量

863

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