No abstract

The aim of this article is to provide an application of the Shapley value to decompose financial portfolio risk. Decomposing the sample covariance risk measure, gives us relative measures, which can be, classified securities of a portfolio according to risk scales.

The hedge fund industry has experienced some very troublesome periods in the recent past. In this study, we test the efficiency of simple and advanced risk measures during these difficult market periods according to the Basel II requirements. We concentrate on Fund of Hedge Fund (FoHF) data, as some studies propose that they suffer least from database and measurement biases, and are therefore likely to yield the most representative results compared to other alternative investment data. We examine model stability and risk measure efficiency using unconditional and conditional GMMbased and likelihood ratio tests, as well as independence tests. We find that model stability is very dependent on the successful specification of autoregressive and volatility models. In addition, custom quantile estimation is less susceptible to misspecification than volatility models. Further, we assess the hypothesis of market efficiency for the special case of FoHF. Finally, we find evidence of different level of managerial skill in terms of asset choice, allocation and market timing.

In this article, we develop the concept of histogram-valued data on value at risk for the classification of hedge fund risk. By using recent developments in data mining, it is a question of the classification of heterogeneous data in order to sort hedge funds by risk class. In practical terms, risk levels relative to measures of histogram-valued data on VaR are calculated as an aid to decision-making. The empirical study was carried out on 1023 HFR-based hedge funds, where we had estimated monthly ARMA-GARCH or asymmetric GARCH VaR and CVaR measures between 01 January 2003 and 31 December 2008. We identify two sub-periods: from 2003 to 2005, and from 2006 to 2008 in order to identify a recovery period after the 2001-2002 crisis and the impact of the 2007-2008 crisis. First, the symbolic approach allows us to construct the measures of histogram-valued data on VaR by optimizing the definition of categories.A symbolic principal component analysis shows that the indices coming from the VaR of the GARCH and asymmetrical GARCH are the most pertinent. Second, we apply a criterion of inter-class inertia and retain a partitioning of hedge funds into three classes by dynamic K-means cluster analysis. For each of our subperiods and for each class, a risk level is defined on the basis of the categories of the most discriminating variable.

Fund ratings, Granger causality in panel, Factor analysis, Sequences of multiperiod, Uncertainty measures, C10, G20,