基于非对称GARCH方法的达卡证券交易所金融市场参与者收益与波动溢出

Mohammad Kamrul Arefin, S. N. Ahkam
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引用次数: 1

摘要

摘要:本文是对孟加拉国金融市场参与者的回报和波动溢出形式的运动的调查。本研究使用了2009年至2016年期间在达卡证券交易所(DSE)交易的商业银行、非银行金融机构(NBFI)和保险公司的每日价格数据。在EGARCH的条件均值方程中使用贝叶斯向量自回归(VAR)模型,在GJR-GARCH模型中使用GJR-GARCH模型来检验收益溢出效应,而在条件方差方程中使用滞后平方残差和滞后条件方差作为方差回归量来检验历史波动率和创新以冲击的形式传递给同一市场中其他参与者的溢出效应。贝叶斯VAR输出揭示了银行-保险对之间以及nbfi -保险对之间高度显著的双向收益溢出。然而,商业银行与非银行金融机构之间的收益溢出是单向的;只有银行的回报会影响非银行金融机构的回报。nbfi的条件波动率表现出高度显著的不对称效应,这意味着坏消息比好消息更大程度地增加nbfi的波动率。GJR-GARCH和EGARCH的产出都揭示了商业银行、非银行金融机构和保险公司之间以历史波动率和创新为形式的双向波动溢出。
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Return and Volatility Spillover between Financial Market Participants of Dhaka Stock Exchange Using Asymmetric GARCH Methods
Abstract—This paper is an investigation of the comovement in the form of return and volatility spillover across financial market participants of Bangladesh. This study uses daily price data of commercial banks, non-bank financial institutions (NBFI), and insurance companies traded in the Dhaka Stock Exchange (DSE) for the period spanning 2009 to 2016. Bayesian Vector Autoregressive (VAR) model has been used in the conditional mean equations of EGARCH and GJR-GARCH models have been used to test the return spillover effects whereas lagged squared residuals and lagged conditional variances have been used as variance regressors in conditional variance equations to test the spillover effects of historical volatility and innovations transmitting in the form of shock to other participants operating in the same market. Bayesian VAR output reveals a highly significant bi-directional return spillover between bank-insurance pair and also between NBFIs-insurance pair. However, return spillover between commercial banks and NBFIs is unidirectional; only bank returns are affecting returns from NBFIs. Conditional volatility of NBFIs exhibit a highly significant asymmetric effect implying that bad news increases volatility of NBFIs to a greater degree than good news. Both GJR-GARCH and EGARCH output reveal bidirectional volatility spillover in the form of historical volatility and innovations among commercial banks, NBFIs and insurance companies.
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