The primary objective of this thesis is to provide models capable of predicting financial distress in individual hedge funds (HFs) and funds-of-hedge funds (FOHFs). Two approaches were used to build these models. The first approach was based on a cross-sectional model while the second one was on a time-varying model. Using a survival analysis technique known as the Cox Proportional Hazards (CPH) model, the first study not only established a survival/hazard model to determine the factors which contributed most to the survival and failure probabilities, but also provided a forecast of survival probability until a specific failure time for HFs and FOHFs. It focused on the comparison between the financial distress forecasting models of HFs and FOHFs under three alternative risk measures of fund failure. Following the estimation of the model, an out-of-sample forecast for both the HFs and the FOHFs was conducted and the predictive accuracy of the estimated CPH models was tested and compared by using Signal Detection Model, Relative Operating Characteristic (ROC) curve and Area under ROC curve (AUROC). According to the test results of the predictive accuracy of the models, the estimated models exhibited satisfactory accuracy in forecasting the most likely failed funds in an out-of-sample test. The second approach used the CPH model incorporating both time-varying factors and fixed factors. After establishing survival/hazard models with time-varying and fixed covariates under three specifications of CPH model (mixed model, fixed model and time-varying model), the study used the mixed CPH model to predict dynamic changes of survival probabilities over the lifetime of HFs and FOHFs. In an effort to identify the effect that the recent Global Financial Crisis (GFC) has had on the financial distress experienced by hedge funds, modelling and prediction was firstly confined to the pre-GFC period. Further analysis that included data post-GFC, allowed for the evaluation of model stability through the identification of significant predictors that held across both the pre-and post-GFC periods, as distinct from those predictors that were significant in only one of these time periods. A SAS Macro program was developed for generating survival probabilities predicted by the mixed CPH model. Following the generation of survivor curves for all companies during the period that included the GFC, the resulting ROC curves and AUROC statistics confirmed the ability of the dynamic CPH models to provide early warning signals to investors about possible fund failures. The secondary objective of this thesis is to examine whether the available data on HFs and FOHFs can reveal the risk-return trade-off and, if so, to find the best risk measure that captured the cross-sectional variation in HF and FOHF returns. With the “Live Funds” and the “Dead Funds” datasets provided by Hedge Fund Research Inc. (HFR), alternative risk measures such as semi-deviation, value at risk, expected shortfall and tail risk were concentrated and compared with standard deviation in terms of their ability to describe the cross-sectional variation in expected returns of HFs and FOHFs. Firstly, the risk measures were analysed at the portfolio level of HFs and FOHFs by adopting the Fama and French (1992) approach. Secondly, the various estimated risk measures were compared at the individual HF and FOHF levels by using univariate and multivariate cross-sectional regressions. The results showed that the available data on HFs and FOHFs exhibited different risk-return trade-offs. The Cornish-Fisher expected shortfall or Cornish-Fisher tail risk could be an appropriate risk measure for HF return. Although appropriate alternative risk measures for the HFs were found, it was difficult to determine the risk measures that best captured the cross-sectional variation in FOHF returns.