A Bayesian State Space Methodology for Forecasting Stock Market Volatility and Associated Time-varying Risk Premia. 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 ....A Bayesian State Space Methodology for Forecasting Stock Market Volatility and Associated Time-varying Risk Premia. 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.Read moreRead less
Non-parametric estimation of forecast distributions in non-Gaussian state space models. The production of accurate forecasts is arguably one of the most challenging tasks in economics, business and finance, where data often assume strictly positive, integer or binary values, or are characterized by many extreme values far from the average. This project will produce new, state-of-the-art statistical methods for generating accurate estimates of the probabilities attached to different possible futu ....Non-parametric estimation of forecast distributions in non-Gaussian state space models. The production of accurate forecasts is arguably one of the most challenging tasks in economics, business and finance, where data often assume strictly positive, integer or binary values, or are characterized by many extreme values far from the average. This project will produce new, state-of-the-art statistical methods for generating accurate estimates of the probabilities attached to different possible future values of such variables. Although far-ranging in scope, the techniques advocated will have particular impact in the financial sphere, where the concept of future risk is inextricably linked to the probability of occurrence of extreme values and, hence, to the future probability distribution of the financial variable. Read moreRead less
Modelling non-linear price dynamics of primary commodities that are affected by seasonality, significant storage costs, and slow adjustment. Australia's economy relies substantially on exports of commodities. However, recent volatility of commodity prices has created tremendous uncertainties for traders, producers and consumers of those commodities. This adversely affects our national economy through the disruption of agricultural and mining production, and also more broadly impacts on investmen ....Modelling non-linear price dynamics of primary commodities that are affected by seasonality, significant storage costs, and slow adjustment. Australia's economy relies substantially on exports of commodities. However, recent volatility of commodity prices has created tremendous uncertainties for traders, producers and consumers of those commodities. This adversely affects our national economy through the disruption of agricultural and mining production, and also more broadly impacts on investment, employment and gross domestic income. This research will model more accurately the complex dynamics of primary commodity prices and their inter-market linkages, which will allow traders, producers and consumers to better forecast commodity price movements and protect themselves through inventory management, hedging and long-run production planning.Read moreRead less