• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>The North American journal of economics and finance >Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models
【24h】

Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models

机译：利用马尔可夫交换加油模型的高收入和新兴股票及外汇市场返回波动的经验造型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Using weekly data for stock and Forex market returns, a set of MS-GARCH models is estimated for a group of high-income (HI) countries and emerging market economies (EMEs) using algorithms proposed by Augustyniak (2014) and Ardia et al. (2018, 2019a,b), allowing for a variety of conditional variance and distribution specifications. The main results are: (i) the models selected using Ardia et al. (2018) have a better fit than those estimated by Augustyniak (2014), contain skewed distributions, and often require that the main coefficients be different in each regime; (ii) in Latam Forex markets, estimates of the heavy-tail parameter are smaller than in HI Forex and all stock markets; (iii) the persistence of the high-volatility regime is considerable and more evident in stock markets (especially in Latam EMEs); (iv) in (HI and Latam) stock markets, a TEL single-regime GJR model (leverage effects) with skewed distributions is selected; but when using MS models, virtually no MS-GJR models are selected. However, this does not happen in Forex markets, where leverage effects are not found either in single-regime or MS-GARCH models.
机译：使用每周数据进行库存和外汇市场回报，使用Augustyniak（2014）和Ardia等人的算法，估计了一组高收入（HI）和新兴市场经济体（EME）的MS-GARCH模型。 （2018,2019a，b），允许各种条件方差和分配规格。主要结果是：（i）使用Ardia等人选择的模型。 （2018）比Augustynk（2014年）估计的人更好，含有偏斜分布，并且通常要求每个制度的主要系数不同; （ii）在拉姆外汇市场中，重型尾参数的估计小于外汇和所有股市; （iii）高波动制度的持久性在股票市场（特别是Latam Emes）中相当明显，更明显; （iv）在（HI和LATAM）股票市场，选择具有偏斜分布的电话单制度GJR模型（杠杆效果）;但是使用MS模型时，几乎没有选择MS-GJR模型。但是，这不会发生在外汇市场中，其中在单个制度或MS-GARCH模型中找不到杠杆效应。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号