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

股市波动的准确预测对于有效的金融风险管理至关重要。除了包含在股市历史回报中的波动性信息外，标的股票上的期权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来源，利用它来预测波动性本身以及由于交易员对波动性风险的感知而计入资产价格的溢价。随着每个交易日都有新的市场数据，将使用最先进的统计方法来产生极端波动和/或极端避险的可能性的最新数据。

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.