Multifactor Quadratic Hobson and Rogers models
Abstract
A multi-factor extension of the Hobson and Rogers (HR) model, incorporating a quadratic variance function (QHR model), is proposed and analysed. The QHR model allows for greater flexibility in defining the moving average filter while maintaining the Markovian property of the original HR model. The use of a quadratic variance function permits the characterisation of weak-stationarity conditions for the variance process and allows for explicit expressions for forward variance. Under the assumption of stationarity, both the variance and the squared increment processes exhibit an ARMA autocorrelation structure. The stationary distribution of the prototypical scalar QHR model is that of a translated and rescaled Pearson type IV random variable. A numerical exercise illustrates the qualitative properties of the QHR model, including the implied volatility surface and the term structures of forward variance, at-the-money (ATM) volatility, and ATM skew.