1,720,984 research outputs found
Portfolio Leverage in Asset Allocation Problems
No full textIn the classical portfolio optimization framework, the leverage of a portfolio is not taken into account and, by assumption, the risk of a portfolio is totally described by the volatility of its returns. As a consequence, the portfolios on the classical mean-variance efficient frontier are not indifferent in terms of leverage. The introduction of leverage measurement in portfolio theory permits to consider other kinds of risk, like margin calls, forced liquidations at undesired prices and losses beyond the total capital. The literature on this topic is very limited while portfolio leverage is of central importance, in particular to set up operative investment strategies. In this paper we propose a simple definition of leverage and we try to introduce it in the classical portfolio selection scheme. We define the concept of leverage free equivalent portfolios in order to compare different investment alternatives for given levels of leverage. The central result of the paper is that the leverage free equivalent of the classical mean-variance efficient portfolios do not preserve the original mean-variance dominance structure. This permits to discriminate if an increase in the expected return of a portfolio totally depends on the leverage effect or is a consequence of a more efficient allocation
A Statistical Method to Optimize the Combination of Internal and External Data in operational Risk Measurement
No full textA threshold based approach to merge data in financial risk management
No full textAccording to the last proposals by the Basel Committee, banks are
allowed to use statistical approaches for the computation of their cap-
ital charge covering nancial risks such as credit risk, market risk and
operational risk.
It is widely recognized that internal loss data only do not suce to
provide accurate capital charge in nancial risk management, especially
for high severity and low frequency events. Financial institutions typically
use external loss data to augment the available evidence and, therefore,
provide more accurate risk estimates. Rigorous statistical treatments are
required to make internal and external data comparable and to ensure
that merging the two databases leads to unbiased estimates.
The goal of this paper is to propose a correct statistical treatment to
make external and internal data comparable and, therefore, mergeable.
Such methodology augments internal losses with relevant, rather than
redundant, external loss data
The market rank indicator to detect financial distress
No full textA novel measure is introduced to forecast financial crises, which can also be seen as a supplementary measure in systemic risk analysis. The indicator (the market rank indicator MRI) considers the relation between the largest singular value of a matrix of the return time series and its k smallest singular values. The rationale behind this is that, in times of market excitation and higher correlation, the vectors of the return time series become closer in the linear space containing them. The MRI is related to the notion of condition number, a measure of how close returns are; therefore, the MRI increases in periods of market tensions. The measure is applied to selected stock market indexes and tested em- pirically for its sensitivity as well as against alternative measures of systemic risk. The MRI could be of interest for both regulators and speculators due to its forecasting power. The empirical analysis underlines that the proposed methodology is particularly appealing to forecast market distress and it shows a clear superiority in terms of predictive capability with respect to other existing measures
Proper measures of connectedness
No full textThe concept of connectedness has been widely used in financial applications, in particular for systemic risk detection. Despite its popularity, at the state of the art, a rigorous definition of connectedness is still missing. In this paper we propose a general definition of connectedness introducing the notion of proper measures of connectedness (PMCs). Based on the classical concept of mean introduced by Chisini, we define a family of PMCs and prove some useful properties. Further, we investigate whether the most popular measures of connectedness available in the literature are consistent with the proposed theoretical framework. We also compare different measures in terms of forecasting performances on real financial data. The empirical evidence shows the forecasting superiority of the PMCs compared to the measures that do not satisfy the theoretical properties. Moreover, the empirical results support the evidence that the PMCs can be useful to detect in advance financial bubbles, crises, and, in general, for systemic risk detection
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