摘要翻译:
准确的波动性模型对于最佳的风险管理实践至关重要。影响建模过程的金融波动性的一个程式化特征是长记忆性,本文探讨了可供选择的风险度量,观察高频英国期货的绝对和平方收益。利用股票指数、利率和国债期货对三种不同资产类型的波动率序列进行了分析。长记忆对于债券合约来说是最强的。对于绝对收益序列和k<1的幂变换,长记忆性总是最强的。长记忆的发现通常包含日内周期性。APARCH模型包含七个相关的GARCH过程,通常对期货系列进行建模,充分记录了ARCH效应、GARCH效应和杠杆效应。
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英文标题:
《Uncovering Long Memory in High Frequency UK Futures》
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作者:
John Cotter
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最新提交年份:
2011
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分类信息:
一级分类:Quantitative Finance 数量金融学
二级分类:Statistical Finance 统计金融
分类描述:Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要:
Accurate volatility modelling is paramount for optimal risk management practices. One stylized feature of financial volatility that impacts the modelling process is long memory explored in this paper for alternative risk measures, observed absolute and squared returns for high frequency intraday UK futures. Volatility series for three different asset types, using stock index, interest rate and bond futures are analysed. Long memory is strongest for the bond contract. Long memory is always strongest for the absolute returns series and at a power transformation of k < 1. The long memory findings generally incorporate intraday periodicity. The APARCH model incorporating seven related GARCH processes generally models the futures series adequately documenting ARCH, GARCH and leverage effects.
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PDF链接:
https://arxiv.org/pdf/1103.5651