Capturing Tail Risk in a Risk Budgeting Model

Sammanfattning: Risk budgeting, in contrast to conventional portfolio management strategies, is all about distributing the risk between holdings in a portfolio. The risk in risk budgeting is traditionally measured in terms of volatility and a Gaussian distribution is commonly utilized for modeling return data. In this thesis, these conventions are challenged by introducing different risk measures, focusing on tail risk, and other probability distributions for modeling returns. Two models for forming risk budgeting portfolios that acknowledge tail risk were chosen. Both these models were based on CVaR as a risk measure, in line with what previous researchers have used. The first model modeled returns with their empirical distribution and the second with a Gaussian mixture model. The performance of these models was thereafter evaluated. Here, a diverse set of asset classes, several risk budgets, and risk targets were used to form portfolios. Based on the performance, measured in risk-adjusted returns, it was clear that the models that took tail risk into account in general had superior performance in relation to the standard model. Nevertheless, it should be noted that the superiority was significantly higher for portfolios that constituted of mainly high-risk assets than for portfolios with more low-risk assets and also that the superior performance did not hold in all time periods considered. It was also clear that the model that used the empirical distribution to model returns performed better than the model based on an assumption of returns belonging to the Gaussian mixture model when the portfolio consisted of more assets with heavier tails.