Ines Fortin

Tail-dependence in stock-return pairs

The empirical joint distribution of return-pairs on stock indices displays high tail-dependence in the lower tail and low tail-dependence in the upper tail. The presence of tail-dependence is not compatible with the assumption of (conditional) joint normality. The presence of asymmetric-tail dependence is not compatible with the assumption of a joint student-t distribution. A general test for one dependence structure versus another via the profile-likelihood is described and employed in a bivariate GARCH model, where the joint distribution of the disturbances is split into its marginals and its copula. The copula used is such that it allows for the presence of lower tail-dependence and for asymmetric tail-dependence, and that it encompasses the normal or t-copula. The model is estimated using bivariate data on a set of European stock indices. We find that the assumption of normal or student-t dependence is easily rejected in favour of an asymmetrically tail-dependent distribution.

An integrated CVaR and real options approach to investments in the energy sector

The objective of this paper is to combine a real options framework with portfolio optimization techniques and to apply this new framework to investments in the electricity sector. In particular, a real options model is used to assess the adoption decision of particular technologies under uncertainty. These technologies are coal-fired power plants, biomassfired power plants and onshore wind mills, and they are representative of technologies based on fossil fuels, biomass and renewables, respectively. The return distributions resulting from this analysis are then used as an input to a portfolio optimization, where the measure of risk is the Conditional Value-at-Risk (CVaR).

Downside loss aversion: Winner or loser?

We study the asset allocation of a quadratic loss-averse (QLA) investor. First, we derive conditions under which the QLA problem is equivalent to the mean-variance (MV) and conditional value-at-risk (CVaR) problems. Then we solve analytically the two-asset problem of the QLA investor for one risk-free and one risky asset. We find that the optimal QLA investment in the risky asset is finite, strictly positive, and minimal with respect to the reference point for a value strictly larger than the risk-free rate. Finally, we implement the trading strategy of a QLA investor who reallocates her portfolio on a monthly basis using 13 EU and 13 US assets. Using risk-adjusted performance measures that do not target specific types of utility we find that QLA portfolios mostly outperform MV and CVaR portfolios; and that incorporating a conservative dynamic update of the QLA parameters, which is based on the historical patterns of bull and bear markets, improves the performance of QLA portfolios. Compared with linear loss-averse portfolios, QLA portfolios display significantly less risk but they also yield lower returns.

Optimal window width choice in spectral density estimation: Review and simulation

This paper deals with optimal window width choice in on-parametric lag or spectral window estimation of the spectral density of a stationary zero-mean process. Several approaches are reviewed: cross-validation-based methods as described by Hurvich(1985) BelträHo and Bloomfield (1987) and Hurvich and Belträo (1990); an iterative pro-cedure developed by Bühlmann (1996); and a bootstrap approach followed by Franke and Hardle (1992). These methods are compared in terms of the mean square error,the mean square percentage error, and a third measure of the istance between the true spectral density and its estimate. The comparison is based on a simulation study, the simulated processes being in he class of ARMA (5,5) processes. On the basis of simu-lation evidence we suggest to use a slightly modified version of Biihlmanns (1996)iterative method. This paper also makes a minor correction of the bootstrap criterion by Franke and Härdle (1992).This paper deals with optimal window width choice in on-parametric lag or spectral window estimation of the spectral density of a stationary zero-mean process. Several approaches are reviewed: cross-validation-based methods as described by Hurvich(1985) BeltraHo and Bloomfield (1987) and Hurvich and Beltrao (1990); an iterative pro-cedure developed by Buhlmann (1996); and a bootstrap approach followed by Franke and Hardle (1992). These methods are compared in terms of the mean square error,the mean square percentage error, and a third measure of the istance between the true spectral density and its estimate. The comparison is based on a simulation study, the simulated processes being in he class of ARMA (5,5) processes. On the basis of simu-lation evidence we suggest to use a slightly modified version of Biihlmanns (1996)iterative method. This paper also makes a minor correction of the bootstrap criterion by Franke and Hardle (1992).