Yin Zheng

Abstract

This thesis empirically analyses the effect of market condition variables on modelling realised volatility and high frequency delta hedging strategies. The methodology used, Genetic programming (GP), allows the incorporation of time-varying nonlinear relationships in empirical studies. The analysis demonstrates that variables related to the condition of the market such as liquidity, bid-ask spread and volatility indicators have a dynamic impact on realised volatility forecasting and delta hedging strategies, which are two fundamental risk management issues in the financial derivative markets.