用GARCH模型建模标准普尔孟买证券交易所BANKEX指数波动模式

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引用次数: 6

摘要

本章的主要目的是估计印度标准普尔孟买证券交易所(BSE) BANKEX指数的波动模式。近年来,印度银行业是增长最快的行业之一,所有主要银行都被纳入标准普尔银行指数,成为指数基准成分股公司。金融计量经济学框架基于不对称GARCH(1,1)模型,该模型是为了捕捉不对称波动聚类和钩峰度而执行的。数据时差从2002年1月的第一个交易日到2014年6月的最后一个交易日。实证结果表明,所选时间序列中存在波动冲击和波动聚类。波动性的影响产生了高度正顺时针和影响实际股票。此外,实证结果表明,BANKEX指数在12年内增长了17倍以上,上市股票存在波动性回报。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling S&P Bombay Stock Exchange BANKEX Index Volatility Patterns Using GARCH Model
The main objective of this chapter is to estimate volatility patterns in the case of S&P Bombay Stock Exchange (BSE) BANKEX index in India. In recent past, the Indian banking sector was one of the fastest-growing industries and all major banks have been included in S&P BANKEX index as index benchmark constituent companies. The financial econometric framework is based on asymmetric GARCH (1, 1) model which is performed in order to capture asymmetric volatility clustering and leptokurtosis. Data time lag is considered from the first transaction day of January 2002 to last transaction day of June 2014. The empirical results revealed the existence of volatility shocks in the selected time series and also volatility clustering. The volatility impact has generated highly positive clockwise and impacted actual stocks. Moreover, the empirical findings reveal that the BANKEX index grown over 17 times in 12 years and volatility returns have been found present in listed stocks.
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