1,721,008 research outputs found
How to measure credit risk concentration
No full textCredit risk concentration is one of the leading topics in modern -
nance, as the bank regulation has made increasing use of external and
internal credit ratings. Concentration risk in credit portfolios comes into
being through an uneven distribution of bank loans to individual borrow-
ers (single-name concentration) or in a hierarchical dimension such as in
industry and services sectors and geographical regions (sectorial concen-
tration).
To measure single-name concentration risk the literature proposes spe-
cic concentration indexes such as the Herndahl-Hirschman index, the
Gini index or more general approaches to calculate the appropriate eco-
nomic capital needed to cover the risk arising from the potential default
of large borrowers.
However, in our opinion, the Gini index and the Herndahl-Hirschman
index can be improved taking into account methodological and theoretical
issues which are explained in this paper.
We propose a new index to measure single-name credit concentration
risk and we prove the properties of our contribution.
Furthermore, considering the guidelines of Basel II, we describe how
our index works on real nancial data. Finally, we compare our index
with the common procedures proposed in the literature on the basis of
simulated and real data
A simple method for unconstrained optimization without using derivatives
No full textA simple derivative free optimization method is presented. Some examples are provided showing the speed and the accuracy of the method
Risk-adjusted geometric diversified portfolios
No full textIn this paper, exploiting a geometric argument, a novel and intuitive approach to portfolio diversification is proposed. The risk-adjusted geometric diversified portfolio is obtained as the point that is equally distant, for a given distance, from the vertices of the simplex, as they represent the single asset portfolios, the worst portfolios in terms of diversification. The definition of risk-adjusted distance as a special case of weighted Euclidean distance permits to introduce the information on the risks of the assets composing the portfolio in a very general way. The closed form solution for the allocation problem is provided and interesting theoretical properties are proved. Further, a direct comparison with Rao’s Quad- ratic Entropy maximization problem is outlined, thus yielding a different perspective to the use of such entropy as a diversification measure. Finally, the effectiveness of our proposal is emphasized through a comprehensive empirical out-of-sample exercise on real financial data
The reasons why maximum diversification is better than minimum risk, including in terms of risk
No full textIn well-defined experimental settings, we evaluate the out-of-sample performance of two asset allocation paradigms: minimum risk and maximum diversification. Specifically, for each given risk measure, we compare the optimal minimum risk allocation with the allocation obtained by maximizing a portfolio diversification measure induced by the same risk measure. The experiment is performed in an out-of-sample, long-only framework, accounting for proportional transaction costs and different lengths of both the estimation window and the holding period. The strategies are compared in terms of numerical stability, return, the Sharpe ratio, and risk, as measured through the same risk measures used for calculating the optimal allocation: variance of returns, mean absolute deviation, value at risk, and expected shortfall at significance levels of 1% and 5%. We show that the maximum diversification strategies are highly competitive, if not generally superior, to the risk minimization allocations. This result supports well-known empirical findings of naive investment strategies that are difficult to beat in practice. Risk minimization strategies require highly accurate forecasts of future returns to perform well. Moreover, these strategies exhibit extreme numerical instability, where even infinitesimal variations in the inputs can dramatically alter the optimal allocation. Therefore, implementation costs are high, significantly impairing performance. In contrast, maximum diversification strategies are less sensitive to minor changes in the input parameters, providing stable allocations that are less affected by transaction costs. Furthermore, these strategies do not require accurate predictions of future returns and are effective in controlling investment risk
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