I think modelling hedge fund returns is a very interesting yet demanding task. Your model will have to strike a balance between the tangibility of the model on the one hand and the possibility of parameter estimation on the other. Plus I think you will encounter hedge funds that resist all modelling attempts because there strategies are just too elusive.
The following very recent paper does a decent job in my opinion. They model hedge fund returns as a combination of factors (they use even investable ETFs to replicate the hedge fund returns) and estimate the parameters through a three step process.
In Search of Missing Risk Factors: Hedge Fund Return Replication with ETFs by J. Duanmu, Y. Li and A. Malakhov (March 2014)
From the abstract:
Properly considering all potential risk factors through tradable
liquid portfolios in the context of a risk based factor model is
paramount to quantifying the benefits of investing in hedge funds. We
attempt to span the space of potential risk factors with exchange
traded funds (ETFs). We develop a methodology of hedge fund return
replication with ETFs based on cluster analysis and LASSO factor
selection that overcomes multicollinearity among ETFs and the data
mining bias. We find that the overall out-of-sample accuracy of hedge
fund replication with ETFs increases with the number of ETFs
available. This is consistent with our interpretation of ETF returns
as proxies to a multitude of alternative risk factors that could be
driving hedge fund returns.
We further consider portfolios of “cloneable” and “non-cloneable”
hedge funds, defined as top and bottom in-sample R2 matches. We find
superior risk-adjusted performance for “non-cloneable” funds, while
“cloneable” funds fail to deliver significantly positive risk-adjusted
performance. We conclude that our methodology provides value in both
identifying skilled managers of “non-cloneable” hedge funds, and also
successfully replicating out-of-sample returns that are due to
alternative risk exposures of “cloneable” hedge funds, thus providing
a transparent and liquid alternative to investors who may find these
return patterns attractive.
You can then also use the resultant model to feed it into a monte carlo simulation because it is then only a combination of (tradable) ETFs which can be modeled and estimated with greater ease. So you effectively broke the whole task down into simpler subtasks which is always a good strategy.