Matilde O. Fernández-Blanco

Financing successful small business projects

Purpose – The current credit rationing strongly influences the viability of SMEs innovation projects. In this context, the practice of screening borrowers by project success probability has become a paramount consideration for both lenders and firms. The aim of this paper is to test the screening role of loan contracts that consider collateral-interest margins simultaneously. Design/methodology/approach – This paper presents an empirical analysis that uses a unique data set composed of 323 bank loans granted by 28 banks to SMEs backed by a Spanish Mutual Guarantee Institution. Findings – The results show that appropriate combinations of collateral and interest rates can distinguish between borrowers with different project success probability: low success probability borrowers finance its projects without collateral and with high interest rates, whereas high success probability borrowers accept loans with real estate collateral and low interest rates. Practical implications – This screening mechanism reduc...

Several risk measures in portfolio selection: Is it worthwhile?

ABSTRACT This paper is concerned with asset allocation using a set of three widely used risk measures, which are the variance or deviation, Value at Risk and the Conditional Value at Risk. Our purpose is to evaluate whether solving the asset allocation problem under several risk measures is worthwhile or not, given the added computational complexity. The main contribution of the paper is the solution of two models that consider several risk measures: the mean-variance-VaR model and the mean- VaR-CVaR model. The inclusion of VaR as one of the objectives to minimize leads to nonconvex problems, therefore the approach we propose is based on a heuristic: multi-objective genetic algorithms. Our results show the adequacy of the multi-objective approach for the portfolio optimization problem and emphasize the importance of dealing with mean-σ-VaR or mean-VaR-CVaR models as opposed to mean-σ-CVaR, where both risk measures are redundant.

Minimising value-at-risk in a portfolio optimisation problem using a multi-objective genetic algorithm

In this paper, we develop a general framework for market risk optimisation that focuses on VaR. The reason for this choice is the complexity and problems associated with risk return optimisation (non-convex and non-differential objective function). Our purpose is to obtain VaR efficient frontiers using a multi-objective genetic algorithm (GA) and to show the potential utility of the algorithm to obtain efficient portfolios when the risk measure does not allow calculating an optimal solution. Furthermore, we measure differences between VaR efficient frontiers and variance efficient frontiers in VaR-return space and we evaluate out-sample capacity of portfolios on both bullish and bearish markets. The results indicate the reliability of VaR-efficient portfolios on both bullish and bearish markets and a significant improvement over Markowitz efficient portfolios in the VaR-return space. The improvement decreases as the portfolios level of risk increases. In this particular case, efficient portfolios do not depend on the risk measure minimised.

Entrepreneurship and Credit Rationing: How to Screen Successful Projects in this Current Crisis Period

The current credit rationing heavily influences entrepreneurship and, more dramatically, the viability of innovation projects. In this context, mechanisms to screen successful projects are of paramount importance for both lenders and entrepreneurs. We present an experiment to test the collateral-interest mechanism of credit screening. Our results confirm that incentive-compatible pairs of collateral-interest rate can distinguish between projects of different success probability, even in moral hazard settings.

A naïve approach to speed up portfolio optimization problem using a multiobjective genetic algorithm

Genetic algorithms (GAs) are appropriate when investors have the objective of obtaining meanvariance (VaR) efficient frontier as minimising VaR leads to nonconvex and nondifferential riskreturn optimisation problems. However GAs are a timeconsuming optimisation technique. In this paper, we propose to use a naive approach consisting of using samples split by quartile of risk to obtain complete efficient frontiers in a reasonable computation time. Our results show that using reduced problems which only consider a quartile of the assets allow us to explore the efficient frontier for a large range of risk values. In particular, the third quartile allows us to obtain efficient frontiers from the 1.8% to 2.5% level of VaR quickly, while that of the first quartile of assets is from 1% to 1.3% level of VaR.