A Bayesian State Space Methodology for Forecasting Stock Market Volatility and Associated Time-varying Risk Premia

预测股票市场波动性和相关时变风险溢价的贝叶斯状态空间方法

• 批准号: FT0991045
• 负责人: Prof Gael Martin
• 金额: $ 59.51万
• 依托单位: Monash University
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2010
• 资助国家: 澳大利亚
• 起止时间: 2010-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT0991045
• 关键词: Bayesian; State; Space; Methodology; Forecasting

Accurate prediction of stock market volatility is critical for effective financial risk management. Along with information on volatility embedded in historical stock market returns, the prices of options written on the underlying stocks also reflect the option market's assessment of future volatility. This project will exploit this dual data source in a completely new way, using it to produce forecasts of both volatility itself and the premia factored into asset prices as a result of traders' perceptions of volatility risk. State-of-the-art statistical methods will be used to produce up-dates of the probability of extreme volatility and/or extreme risk aversion, as new market data becomes available each trading day.

准确预测股票市场波动性是有效管理金融风险的关键。沿着历史股票市场收益中的波动性信息，标的股票期权的价格也反映了期权市场对未来波动性的评估。该项目将以一种全新的方式利用这一双重数据源，利用它来预测波动性本身以及交易员对波动性风险的看法导致的计入资产价格的溢价。随着每个交易日出现新的市场数据，将使用最先进的统计方法来更新极端波动和/或极端风险规避的概率。