Asymptotic Expansions for Market Risk Assessment: Evidence in Energy and Commodity Indices

Erreferentzia bibliografikoak

• Hansen, B.E.: Autoregressive conditional density estimation. Int. Econ. Rev. 35, 705–730 (1994)
• Fernández, C., Steel, M.F.: On Bayesian modeling of fat tails and skewness. J. Am. Stat. Assoc. 93(441), 359–371 (1998)
• Theodossiou, P.: Financial data and the skewed generalized t distribution. Manag. Sci. 44(12-part-1), 1650–1661 (1998)
• Jones, M.C., Faddy, M.J.: A skew extension of the t-distribution, with applications. J. R. Stat. Soc., B: Stat. Methodol. 65(1), 159–174 (2003)
• Bauwens, L., Laurent, S.: A new class of multivariate skew densities, with application to generalized autoregressive conditional heteroscedasticity models. J. Bus. Econ. Stat. 23(3), 346–354 (2005)
• Cardona, E., Mora-Valencia, A., Velásquez-Gaviria, D.: Testing expected shortfall: an application to emerging market stock indices. Risk Manag. 21(3), 153–182 (2019)
• Jørgensen, B.: Statistical Properties of the Generalized Inverse Gaussian Distribution, vol. 9. Springer (1982)
• Barndorff-Nielsen, O.E.: Normal inverse Gaussian distributions and stochastic volatility modelling. Scand. J. Stat. 24(1), 1–13 (1997)
• Mencía, J., Sentana, E.: Estimation and testing of dynamic models with generalized hyperbolic innovations. Available at SSRN 790704 (2005)
• Gupta, R.D., Kundu, D.: Generalized exponential distribution: different method of estimations. J. Stat. Comput. Simul. 69(4), 315–337 (2001)
• Carrasco, J.M., Ortega, E.M., Cordeiro, G.M.: A generalized modified Weibull distribution for lifetime modeling. Comput. Stat. Data Anal. 53(2), 450–462 (2008)
• Ñíguez, T.-M., Paya, I., Peel, D., Perote, J.: Flexible distribution functions, higher-order preferences and optimal portfolio allocation. Quant. Finance. 19, 669–703 (2019)
• Edgeworth, F.: The asymmetrical probability-curve. The London, Edinburgh, and Dublin Philosophical Magazine and J. Sci. 41(249), 90–99 (1896)
• Barton, D.E., Dennis, K.E.: The conditions under which Gram-Charlier and Edgeworth curves are positive definite and unimodal. Biometrika. 39(3/4), 425–427 (1952)
• Jondeau, E., Rockinger, M.: Gram–Charlier densities. J. Econ. Dyn. Control. 25(10), 1457–1483 (2001)
• León, Á., Mencía, J., Sentana, E.: Parametric properties of semi-nonparametric distributions, with applications to option valuation. J. Bus. Econ. Stat. 27(2), 176–192 (2009)
• Abramowitz, M., Stegun, I. A.: Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Series 55, Tenth Printing. National Bureau of Standards Appl. Math. (1972)
• Kendall, M., Stuart, A.: Distribution Theory The Advanced Theory of Statistics, vol. 1. Griffin, London (1977)
• Trespalacios, A., Cortés, L.M., Perote, J.: Uncertainty in electricity markets from a semi-nonparametric approach. Energy Policy. 137, 111091 (2020)
• Ñíguez, T.-M., Perote, J.: Moments expansion densities for quantifying financial risk. N. Am. J. Econ. Finance. 42, 56–69 (2017)
• Cortés, L.M., Mora-Valencia, A., Perote, J.: The productivity of top researchers: a semi-nonparametric approach. Scientometrics. 109(2), 891–915 (2016)
• Mauleon, I., Perote, J.: Testing densities with financial data: an empirical comparison of the Edgeworth-Sargan density to the Students t. Eur. J. Financ. 6, 225–239 (2000)
• Del Brio, E.B., Perote, J.: Gram–Charlier densities: maximum likelihood versus the method of moments. Insur.: Math. Econ. 51(3), 531–537 (2012)
• Del Brio, E.B., Mora-Valencia, A., Perote, J.: Risk quantification for commodity ETFs: backtesting value-at-risk and expected shortfall. Int. Rev. Financ. Anal. 70, 101163 (2020)
• Kupiec, P.: Techniques for verifying the accuracy of risk measurement models. J. Deriv. 3(2) (1995)
• Christoffersen, P.F.: Evaluating interval forecasts. Int. Econ. Rev. 39, 841–862 (1998)
• Acerbi, C., Szekely, B.: General properties of backtestable statistics. Available at SSRN 2905109 (2017)
• Velásquez-Gaviria, D., Mora-Valencia, A., Perote, J.: A comparison of the risk quantification in traditional and renewable markets. Energies. 13(11), 2805 (2020)