New Markov Chain Models to Estimate the Premium for Extended Hedge Fund Lockups

To estimate the premium an investor should expect from extended hedge fund lockups, Derman et al. (2009) proposed a three-state discrete-time Markov Chain to model the state of a hedge fund, allowing the state to change randomly among the states “good,” “sick” and “dead” every year. The lockup premium measures the consequence of being stuck with a sick fund. To be more realistic, we propose an alternative three-state continuous-time Markov Chain model, which allows state changes continuously in time. We develop new techniques for parameter fitting, exploiting nonlinear programming. We fit the parameters indirectly to readily available hedge fund performance measures: the persistence factor, the death rate and the variance of annual returns, estimated from TASS hedge fund data. NEW MARKOV CHAIN MODELS TO ESTIMATE THE PREMIUM FOR EXTENDED HEDGE FUND LOCKUPS Kun Soo Park, Graduate School of Management, KAIST Business School, [email protected] Ward Whitt, Department of IE and OR, Columbia University, [email protected]