Abstract
This paper calibrates risk assessment of alternative methods for modeling ETFs and commodities portfolios. We implement recently proposed backtesting techniques for both value-at-risk (VaR) and expected shortfall (ES) under parametric and semi-nonparametric techniques. Our results indicate that skewed-t and Gram-Charlier present the best relative performance for individual Commodity ETFs for those confidence levels recommended by Basel Accords. For Commodity ETF portfolios, multivariate semi-nonparametric distribution outperforms better than multivariate normal distribution. At the view of these results, we recommend the application of leptokurtic distributions and semi-nonparametric techniques to mitigate regulation concerns about global financial stability and financialization of commodity business.
Acerca del expositor
Andrés Mora es doctor en Economía de la Empresa de la Universidad de Salamanca, España y magíster en Banca y Finanzas Cuantitativas de la Universidad Complutense Madrid, Universidad del País Vasco, Universidad de Valencia y Universidad Castilla-La Mancha. Actualmente, es profesor asistente en la Universidad de los Andes. Cuenta con un CRM (Certified in Risk Management by IIPER). Sus áreas de interés son riesgo financiero, instrumentos financieros de renta fija y temas afínes a inversiones.