Bart Frijns, Christian Tallau, A. Tourani-Rad
{"title":"The Information Content of Implied Volatility: Evidence from Australia","authors":"Bart Frijns, Christian Tallau, A. Tourani-Rad","doi":"10.2139/ssrn.1246142","DOIUrl":null,"url":null,"abstract":"This study develops an implied volatility index for the Australian stock market, termed as the AVX, and assesses its information content. The AVX is constructed using S&P/ASX 200 index options with a constant time‐to‐maturity of three months. It is observed that the AVX has a significant negative and asymmetric relationship with S&P/ASX 200 returns. When evaluating the forecasting power of the AVX for future stock market volatility, it is found that the AVX contains important information both in‐sample and out‐of‐sample. In‐sample, the AVX significantly improves the fit of a GJR‐GARCH(1, 1) model. Out‐of‐sample, the AVX significantly outperforms the RiskMetrics approach and the GJR‐GARCH(1, 1) model, with its highest forecasting power at the one‐month forecasting horizon. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:134–155, 2010","PeriodicalId":370682,"journal":{"name":"21st Australasian Finance & Banking Conference 2008 (Archive)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"116","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"21st Australasian Finance & Banking Conference 2008 (Archive)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.1246142","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 116
隐含波动率的信息内容:来自澳大利亚的证据
本研究为澳大利亚股票市场开发了一个隐含波动率指数,称为AVX,并评估其信息内容。AVX是使用标准普尔/ASX 200指数期权构建的,固定期限为三个月。可以观察到,AVX与S&P/ASX 200的回报有显著的负和不对称关系。当评估AVX对未来股票市场波动的预测能力时,发现AVX包含样本内和样本外的重要信息。在样本中,AVX显著改善了GJR‐GARCH(1,1)模型的拟合。在样本外,AVX显著优于RiskMetrics方法和GJR - GARCH(1,1)模型,在一个月的预测范围内具有最高的预测能力。©2009 Wiley期刊公司[j] [j] .中国科学:自然科学,2010
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