The predictive Power of Fund Ratings with a novel approach using uncertainty measures to analyzing risk

in Decisions in Economics and Finance (2009), 32(2), 149-160

Using two approaches to panel data, Granger causality analysis with semi-asymptotic tests, and a structural approach based on entropies measured on sequences of multi-period ratings and returns, we ... [more ▼]

Using two approaches to panel data, Granger causality analysis with semi-asymptotic tests, and a structural approach based on entropies measured on sequences of multi-period ratings and returns, we specify the relationship between a fund’s performance and both Morningstar and Europerformance ratings. We conclude on the Europerformance agency forecasting ability for the Luxembourg funds, and the Morningstar agency for the French funds. Indeed, we find two groups of funds depending on their domiciliation and appropriated rating. The results of this paper have implications for the management of fund portfolios, and the structural approach, more robust to our data, must be a first process for forecast models on the basis of similar funds, minor uncertainty or risk measure, and appropriated rating. [less ▲]

The shapley decomposition for risk portfolios

in Applied Economics Letters (2008), 15(9), 713-715

The aim of this paper is to propose the Shapley Value to decompose financial risk portfolios. Decomposing the sample covariance risk measure, gives us relative measures, which can be, classified ... [more ▼]

The aim of this paper is to propose the Shapley Value to decompose financial risk portfolios. Decomposing the sample covariance risk measure, gives us relative measures, which can be, classified securities of a portfolio according to a risk scale [less ▲]

Value at Risk, Outliers and Chaotic Dynamics

in COSTANTINO, M.; LARRAN, M. (Eds.) Computational Finance and its Applications III (2008)

Financial returns series typically exhibit excess kurtosis and volatility clustering. GARCH models are often applied to describe these two stylised facts. Nevertheless, applications of these processes to ... [more ▼]

Financial returns series typically exhibit excess kurtosis and volatility clustering. GARCH models are often applied to describe these two stylised facts. Nevertheless, applications of these processes to stock returns have shown that they cannot capture all excess kurtosis, high Jarque-Bera and inherent non-linearity. The aim of this work is to suggest a new non-linear framework for the calculation of the Value-at-Risk. As it has been demonstrated in Kyrtsou and Terraza V. (2004) the use of a mixed non-linear model in the estimation of VaR can improve the obtained results. In this paper we apply the traditional VaR-GARCH and the VaR-Mackey-Glass-GARCH models both to the initial and the filtered Nikkei returns series without outliers. [less ▲]

Timing inconsistencies in the calculation of Funds of funds Net Asset Value

in Fundexpert (2006)

The purpose of this paper is to analyze the impact upon tracking errors of timing inconsistencies in the calculation of Funds of Funds (FoF) Net Asset Value (NAV). When the NAV of a FoF is calculated, the ... [more ▼]

The purpose of this paper is to analyze the impact upon tracking errors of timing inconsistencies in the calculation of Funds of Funds (FoF) Net Asset Value (NAV). When the NAV of a FoF is calculated, the NAVs of underlying funds are not always available at the same market date. This can be due to the time of publication of the underlying funds NAVs and also due to the fact that the funds relate to different markets with different closing times. The NAV of the FoF are therefore calculated using diverse market days. Benchmarks of FoF are usually composed of indices. The level of these benchmarks are calculated using underlying prices at the same market day. If we compare the return series of a FoF with the return series of its benchmark, a problem occurs due to the timing inconsistencies in the calculation. We examine how these timing inconsistencies produce noise in the NAV of FoF and therefore noise in the tracking error. We construct Funds of Funds and calculate NAVs of these FoF using underlying NAVs at different dates. We then compare series of tracking errors to analyze the impact of the timing inconsistencies and formalize a relation including a systematic error term generated by these timing inconsistencies. [less ▲]