Abstract
In this thesis we combine long memory processes and regime switching models to study the nonlinear dynamics of hedge funds returns and their exposure to market risk. The attractiveness of hedge funds lies in their ability to generate returns uncorrelated to those of traditional assets while allowing to improve returns and/or reduce the risk, regardless of market conditions. However, some specificity of returns of hedge funds as their nonlinear and asymmetric nature as well as the presence of a strong autocorrelation in related to illiquidity problems make this aspect only valid in a Gaussian framework. In this study, we adopt an econometric approach that reconciles the notion of long memory and that of pure performance persistence. In this regard, we focus on the risk of confusion between real and spurious long memory long memory since certain processes can generate similar characteristics to that of long memory processes. It appears from this study not only the inadequacy of standard models to take into account the characteristics of the series of financial returns but also the relevance of using mixed models to better understand all of these features within a unified framework. The Beta Switching ARFIMA-FIGARCH mode we suggest reveals the complexity of hedge fund return dynamics and proves the need to better understand the dynamics of returns of hedge funds in order to explain the interactions between hedge funds themselves and between hedge funds and standard markets. The long memory component is taken into account both at the conditional mean through the ARFIMA process and at the conditional variance through several specifications heteroscedatic fractional processes including FIGARCH, FIAPARCH and HYGARCH models. This model take into account several features of hedge fund returns, highlights their hidden risks and represents a new perspective to which managers could move.