Efficient Risk Factor Allocation with Regime Based Models

Abstract: It is widely accepted that nancial mark behaviour is characterized by periodicity. However, in academia and practice financial markets are often modeled as time consistent, resulting in static investment strategies that are assumed to be ecient. This has great implications on investors' allocation decisions and in valuation of risks and performances. In this thesis we consider different risk factors and their behaviour over time. By using a hidden market model, which allow the risk factors to jump between 2 dierent regimes, we are able to identify distinctly different behaviour for all the risk factors in the different regimes. We are then able to design different investment strategies and use an online implementation of the model that field significant better returns than equivalent static investment strategies. Our results give further proof of the benefits of using regime based models when working with portfolio decisions. We also suggest a shrinkage approach to the covariance matrix estimations that gives increased stability to the model and makes it highly applicable in practice.