This paper investigates the dependence structure between daily oil price changes and stock market returns in six GCC countries (Bahrain, Kuwait,Oman, Qatar, Saudi Arabia and United Arab Emirates) during the period from June 1, 2005 to February 11, 2013. For that, we apply three Archimedean...

This paper proposes a new class of semiparametric generalized long memory model with FIA- PARCH errors (SEMIGARMA-FIAPARCH model) that extends the conventionnel GARMA model to incorporate nonlinear deterministic trend, in the mean equation, and to allow for time varying volatility, in the...

This paper investigates the dynamic properties of both return and volatility of the oil price. The analysis is carried out using a set of double long memory specifications incorporating several features such as long range dependence, asymmetry in conditional variances and time varying...

This paper proposes an original framework based on nonlinear panel data models to study the empirical influence of the interest rate and the inflation rate on the non-life insurance premiums for fourteen developed countries over the period 1965-2008. More specifically, we apply the panel smooth...

In this paper, we propose a time-varying long memory model where the fractional integration parameter varies nonlinearly according to Smooth Transition Regressive (STR) model. To estimate the fractional integration parameter, we suggest a new estimation method based on wavelet approach. In...

This paper examines the dynamic dependence between American and four developed stock markets, namely, Japan, United Kingdom, Germany and France during a recent period including the global financial crisis 2007-2009. The econometric approach is based on the extreme-value time-varying copula...

In this paper, we seek to examine the effect of the presence of long memory on the dependence structure between financial returns and on portfolio optimization. First, we focus on the dependence structure using copulas. To select the best copula, in addition to the goodness of fit tests, we...