This paper evaluates several alternative formulations for minimizing the credit risk of a portfolio of financial contracts with different counterparties. Credit risk optimization is challenging because the portfolio loss distribution is typically unavailable in closed form. This makes it difficult to accurately compute Value-at-Risk (VaR) and expected shortfall (ES) at the extreme quantiles tha...

We propose a robust portfolio optimization approach based on quantile statistics. The proposed method is robust to extreme events in asset returns, and accommodates large portfolios under limited historical data. Specifically, we show that the risk of the estimated portfolio converges to the oracle optimal risk with parametric rate under weakly dependent asset returns. The theory does not rely ...

this paper will investigate the optimum portfolio for an investor, taking into account 5 criteria. the mean variance model of portfolio optimization that was introduced by markowitz includes two objective functions; these two criteria, risk and return do not encompass all of the information about investment; information like annual dividends, s&p star ranking and return in later years which...

Every acquisition program contains risks. But what impact do these risks have on the entire portfolio of acquisition activities? What does risk at the Enterprise level really mean? For example, risk collectively could portend great danger to the acquisition manager’s overall portfolio which might be otherwise masked by traditional program performance and analysis. Alternatively, these risks als...

Portfolio management of new product development has been studied in the context of large companies, but its relevance for small software companies has not been discussed. We conducted a multiple case study of four small software companies. The companies did not practice portfolio management, and seemed to suffer from problems that in the literature are considered symptomatic of inadequate portf...

The optimization of large portfolios displays an inherent instability due to estimation error. This poses a fundamental problem, because solutions that are not stable under sample fluctuations may look optimal for a given sample, but are, in effect, very far from optimal with respect to the average risk. In this paper, we approach the problem from the point of view of statistical learning theor...

We introduce a generic solver for dynamic portfolio allocation problems when the market exhibits return predictability, price impact and partial observability. We assume that the price modeling can be encoded into a linear state-space and we demonstrate how the problem then falls into the LQG framework. We derive the optimal control policy and introduce analytical tools that preserve the intell...

( ) The simulation-based optimization framework Sim-Opt uses a twin-loop computational architecture, which combines mathematical programming and discrete e®ent simulation, to address this problem. This article extends our earlier work to present methods ( for integrating information from the inner loop Sim-Opt time lines, reacti®e adjust) ( ) ment and using it in the outer risk-control loop Sto...

Recently, several optimization approaches for portfolio selection have been proposed in order to alleviate the estimation error in the optimal portfolio. Among such are the normconstrained variance minimization and the robust portfolio models. In this paper, we examine the role of the norm constraint in the portfolio optimization from several directions. First, it is shown that the norm constra...